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{ "count": 238, "next": "https://memento.epfl.ch/api/v1/events/?format=api&limit=10&offset=210&ordering=event__url_link", "previous": "https://memento.epfl.ch/api/v1/events/?format=api&limit=10&offset=190&ordering=event__url_link", "results": [ { "id": 70949, "title": "Complex Fluids at Interfaces: Structure, Stability, and Molecular Effects", "slug": "complex-fluids-at-interfaces-structure-stability-a", "event_url": "https://memento.epfl.ch/event/complex-fluids-at-interfaces-structure-stability-a", "visual_url": "https://memento.epfl.ch/image/32337/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32337/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32337/max-size.jpg", "lang": "en", "start_date": "2026-06-17", "end_date": "2026-06-19", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/complex-fluids-at-interfaces-structure-stability-and-molecular-effects-1492\">https://www.cecam.org/workshop-details/complex-fluids-at-interfaces-structure-stability-and-molecular-effects-1492</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nComplex fluids are ubiquitous in biology, geophysics, and industry [1]. These materials are challenging to characterize and predict [1–4], particularly when they incorporate multiple interfaces, as in colloidal suspensions [4], foams [5–7], or nanoporous membranes [8–10]. Many of these interfaces are micro- or nano-scale and evolve over short times, which can obscure them to observation and pose challenges to experimentalists [2–5, 11, 12]. This opens exciting opportunities for a strong partnership between the development of novel theoretical, computational, and experimental techniques.<br>\r\nProbing interfaces presents unique challenges compared to probing complex fluids in the bulk. The interfacial structure and constitutive behavior then depend on the composition of two fluids as well as the interfacial configuration [13, 14]. Translating this increased complexity to a computational framework involves developing reliable models describing molecular interactions near fluid-fluid or fluid-solid interfaces [15–17], as well as models for continuum stresses [18]. Molecular modeling is necessary to reveal the physics of chemically-complex structures [17], but is computationally expensive, and it can be challenging to identify the relevant physics to include [19]. Yet the interface also provides unique opportunities for control: in liquid crystals, for example, interfacial stresses can be transmitted through the bulk, leading to novel pattern formation [20] and optical materials exploiting interfacial control [21]. Finally, interfaces are prone to instabilities, which can make flows unpredictable, but opens opportunities to exploit unstable growth for spontaneous patterning.<br>\r\nTo underscore the present challenges, even for a “simple” Newtonian fluid, the presence of an interface may hinder understanding of flow mechanics. For example, mechanisms for contact during drop impact are still debated [22]: molecular dynamics (MD) simulations can clarify which effects dominate among interfacial instabilities, electrostatic charge, gas-kinetic effects, and other driving forces [22–26], in addition to liquid/surface chemistry [27, 28]. Diffusive processes at interfaces [29] and nanoscale membrane flows, where osmotic and phoretic effects are significant [11, 30], also require further development in MD or coarse-grained models.<br>\r\n <br>\r\n<strong>This workshop aims to foster exchanges around the following </strong><strong>broad questions:</strong>\r\n</p><ul>\r\n\t<li>How do <strong>molecular phenomena</strong><strong> </strong>determine the <strong>structural properties and interfacial dynamics </strong>of complex fluid interfaces?</li>\r\n\t<li>How do we approach <strong>a rigorous, robust, and predictive upscaling </strong>between non-continuum computational approaches (e.g. MD, coarse-grained models), which are computationally costly, and large-scale systems? Can we extract universal quantities or concepts from MD to be used in a continuum model? Are these potential quantities intrinsic properties or do they depend on the flow configuration and hence require an ad hoc calibration for each flow situation?</li>\r\n\t<li><strong>How can emerging experimental and computational techniques inform our understanding of </strong><strong>interfacial instabilities in complex fluids? </strong>Can we account for instabilities arising from molecular and meso-scales in a macroscopic stability analysis?</li>\r\n\t<li>Is it possible to <strong>incorporate microscopic effects into macroscopic models </strong>which 'go beyond' the conventional Navier-Stokes-Fourier paradigm? For example, can effective viscosities adequately account for molecular effects, or can noise terms incorporate thermal fluctuations? Can these models be captured by extending existing computational approaches, or do they require entirely new frameworks?</li>\r\n</ul>\r\n<strong>The list of confirmed speakers will be announced in February. </strong>In addition, a limited number of abstracts may be submitted for the poster session – submissions will open in February.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1021/acs.langmuir.3c03727\" target=\"_blank\">[1] L. Veldscholte, J. Snoeijer, W. den Otter, S. de Beer, Langmuir, <strong>40</strong>, 4401-4409 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1017/jfm.2023.659\" target=\"_blank\">[2] G. Zampogna, P. Ledda, K. Wittkowski, F. Gallaire, J. Fluid Mech., <strong>970</strong>, A39 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.134.054001\" target=\"_blank\">[3] A. Carbonaro, G. Savorana, L. Cipelletti, R. Govindarajan, D. Truzzolillo, Phys. Rev. Lett., <strong>134</strong>, 054001 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1002/adma.202502173\" target=\"_blank\">[4] L. Buonaiuto, S. Reuvekamp, B. Shakhayeva, E. Liu, F. Neuhaus, B. Braunschweig, S. de Beer, F. Mugele, Advanced Materials, <strong>37</strong>, (2025)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpcb.4c02513\" target=\"_blank\">[5] J. Sun, L. Li, R. Zhang, H. Jing, R. Hao, Z. Li, Q. Xiao, L. Zhang, J. Phys. Chem. B, <strong>128</strong>, 7871-7881 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1063/5.0205314\" target=\"_blank\">[6] H. Liu, J. Zhang, Physics of Fluids, <strong>36</strong>, (2024)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.131.164001\" target=\"_blank\">[7] S. Perumanath, M. Chubynsky, R. Pillai, M. Borg, J. Sprittles, Phys. Rev. Lett., <strong>131</strong>, 164001 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.134.134001\" target=\"_blank\">[8] F. Yu, A. Ratschow, R. Tao, X. Li, Y. Jin, J. Wang, Z. Wang, Phys. Rev. Lett., <strong>134</strong>, 134001 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevfluids.8.103602\" target=\"_blank\">[9] R. Kaviani, J. Kolinski, Phys. Rev. Fluids, <strong>8</strong>, 103602 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-fluid-121021-021121\" target=\"_blank\">[10] J. Sprittles, Annu. Rev. Fluid Mech., <strong>56</strong>, 91-118 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41377-022-00930-5\" target=\"_blank\">[11] L. Ma, C. Li, J. Pan, Y. Ji, C. Jiang, R. Zheng, Z. Wang, Y. Wang, B. Li, Y. Lu, Light. Sci. Appl., <strong>11</strong>, 270 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-023-43978-6\" target=\"_blank\">[12] Q. Zhang, W. Wang, S. Zhou, R. Zhang, I. Bischofberger, Nat. Commun., <strong>15</strong>, 7 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1039/d4cc01557f\" target=\"_blank\">[13] R. Ishraaq, S. Das, Chem. Commun., <strong>60</strong>, 6093-6129 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-fluid-122316-045034\" target=\"_blank\">[14] S. Popinet, Annu. Rev. Fluid Mech., <strong>50</strong>, 49-75 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1039/d4cp02128b\" target=\"_blank\">[15] L. Smook, R. Ishraaq, T. Akash, S. de Beer, S. Das, Phys. Chem. Chem. Phys., <strong>26</strong>, 25557-25566 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-fluid-031821-104935\" target=\"_blank\">[16] R. Ewoldt, C. Saengow, Annu. Rev. Fluid Mech., <strong>54</strong>, 413-441 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1021/acsmacrolett.7b00812\" target=\"_blank\">[17] H. Liang, Z. Cao, Z. Wang, A. Dobrynin, ACS Macro Lett., <strong>7</strong>, 116-121 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-017-00636-y\" target=\"_blank\">[18] Q. Xu, K. Jensen, R. Boltyanskiy, R. Sarfati, R. Style, E. Dufresne, Nat. Commun., <strong>8</strong>, 555 (2017)</a><br>\r\n<a href=\"https://doi.org/10.1103/physreve.111.055103\" target=\"_blank\">[19] A. Fukushima, S. Oyagi, T. Tokumasu, Phys. Rev. E, <strong>111</strong>, 055103 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1088/1361-6501/ad66f9\" target=\"_blank\">[20] K. Jorissen, L. Veldscholte, M. Odijk, S. de Beer, Meas. Sci. Technol., <strong>35</strong>, 115501 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1073/pnas.2221304120\" target=\"_blank\">[21] A. Allemand, M. Zhao, O. Vincent, R. Fulcrand, L. Joly, C. Ybert, A. Biance, Proc. Natl. Acad. Sci. U.S.A., <strong>120</strong>, (2023)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-fluid-071320-095958\" target=\"_blank\">[22] N. Kavokine, R. Netz, L. Bocquet, Annu. Rev. Fluid Mech., <strong>53</strong>, 377-410 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41563-020-0625-8\" target=\"_blank\">[23] L. Bocquet, Nat. Mater., <strong>19</strong>, 254-256 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1126/science.aan2438\" target=\"_blank\">[24] R. Tunuguntla, R. Henley, Y. Yao, T. Pham, M. Wanunu, A. Noy, Science, <strong>357</strong>, 792-796 (2017)</a><br>\r\n<a href=\"https://doi.org/10.1073/pnas.1705181114\" target=\"_blank\">[25] P. Beltramo, M. Gupta, A. Alicke, I. Liascukiene, D. Gunes, C. Baroud, J. Vermant, Proc. Natl. Acad. Sci. U.S.A., <strong>114</strong>, 10373-10378 (2017)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.133.088202\" target=\"_blank\">[26] C. Guidolin, E. Rio, R. Cerbino, F. Giavazzi, A. Salonen, Phys. Rev. Lett., <strong>133</strong>, 088202 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1017/jfm.2021.529\" target=\"_blank\">[27] A. Bussonnière, I. Cantat, J. Fluid Mech., <strong>922</strong>, A25 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1103/physreve.95.030602\" target=\"_blank\">[28] L. Oyarte Gálvez, S. de Beer, D. van der Meer, A. Pons, Phys. Rev. E, <strong>95</strong>, 030602 (2017)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.macromol.4c01604\" target=\"_blank\">[29] V. Calabrese, A. Shen, S. Haward, Macromolecules, <strong>57</strong>, 9668-9676 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1073/pnas.2211347120\" target=\"_blank\">[30] M. Kumar, J. Guasto, A. Ardekani, Proc. Natl. Acad. Sci. U.S.A., <strong>120</strong>, (2023)</a><br>\r\n ", "image_description": "", "creation_date": "2026-01-26T14:11:21", "last_modification_date": "2026-01-26T16:41:44", "link_label": "Complex Fluids at Interfaces: Structure, Stability, and Molecular Effects", "link_url": "https://www.cecam.org/workshop-details/complex-fluids-at-interfaces-structure-stability-and-molecular-effects-1492", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Irmgard Bischofberger, </strong>MIT ; <strong>Lebo Molefe, </strong>EPFL ; <strong>James Sprittles, </strong>University of Warwick ; <strong>Giuseppe Zampogna, </strong>Università degli Studi di Genov", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119438/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70901, "title": "Emergent dynamics of active colloids: chirality, non-reciprocity and memory", "slug": "emergent-dynamics-of-active-colloids-chirality-n-2", "event_url": "https://memento.epfl.ch/event/emergent-dynamics-of-active-colloids-chirality-n-2", "visual_url": "https://memento.epfl.ch/image/32290/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32290/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32290/max-size.jpg", "lang": "en", "start_date": "2026-05-11", "end_date": "2026-05-13", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/emergent-dynamics-of-active-colloids-chirality-non-reciprocity-and-memory-1496\">https://www.cecam.org/workshop-details/emergent-dynamics-of-active-colloids-chirality-non-reciprocity-and-memory-1496</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nBiological systems in Nature are intrinsically out-of-equilibrium to maintain their structural complexity and functional diversity. Similarly, out-of-equilibrium dissipative colloidal systems subjected to an external energy injection often develop nontrivial collective dynamics and self-organize into large scale structures, which are far more complex than their equilibrium counterparts [1-17]. The main sources of such emergent behavior are the many-body dissipative interactions between colloids (e. g. steric, electrostatic, magnetic), the external energy injection, and the coupling of particles dynamics through the fluid flow around them. Collective dynamics and self-organization in out-of-equilibrium colloidal systems (often termed as <em>active colloids</em>) is a rapidly growing area of research which led to the discovery of novel dynamic architectures and functionalities that are not generally available at equilibrium.<br>\r\n Colloidal systems have been the subject of intense research for a long time due to their ubiquitous technological applications. Colloidal particles display Brownian motion, size in the visible wavelength and dynamics in experimentally accessible timeframes (milliseconds to seconds) making them an attractive platform for the experiments and the computational modeling. The pair interactions between particles can be easily adjusted in strength and range by applying relatively small external fields. When driven by external forces or an internal energy source, colloids can mimic motile biological entities and can serve as a testbed for exploring the rich and complex physics of out-of-equilibrium systems. These dissipative colloidal structures utilize energy to generate and maintain structural complexity. Experiments and numerical simulations along this line of research have often revealed nontrivial collective dynamics and emergent large-scale structures [1-17]. With the proposed workshop we would like to provide a platform for discussing several new and important trends in this field of active colloidal materials, that is, chirality, non-reciprocity, and memory.<br>\r\nA recent hot trend in the field of active colloids explores the emergence of coherent motion and self-organization in systems with chirality [5-11]. Chirality is an intrinsic fundamental property of many natural and synthetic systems. Colloidal particles driven by external torques [12-18] constitute an ideal model system to investigate these phenomena since they avoid the inherent complexity of biological active matter. Spinning particles dispersed in a fluid represent a special class of artificial active systems that inject vorticity at the microscopic level [19-25]. Dense collections of interacting spinning particles represent a chiral fluid [26], which breaks parity and time-reversal symmetries, and displays a novel viscosity feature called the odd viscosity and elasticity [27, 28]. The odd viscosity has been identified in interacting chiral spinners [29], and it led to remarkable effects such as production of flow perpendicular to the pressure [27], topological waves [30], or the emergence of edge currents [29]. Magnetic rollers dynamically assemble into a vortex under harmonic confinement, that spontaneously selects a sense of rotation and is capable of chirality switching [31,32]. Multiple motile vortices unbound from any confinement have been revealed in ensembles of magnetic rollers powered by a uniaxial field [33]. Oscillating chiral flows were generated when a roller liquid was coupled to fixed obstacles [34]. There has been an increasing effort to investigate collective phenomena in systems composed of chiral active units [11, 35-40]. Synchronized self-assembled magnetic spinners at the liquid interface revealed structural transitions from liquid to nearly crystalline states and demonstrated reconfigurability coupled to a self-healing behavior [41]. Activity-induced synchronization leading to a mutual flocking, and chiral self- sorting has been observed in modeled ensembles of self-propelled circle swimmers [42]. Shape anisotropic particles powered by the Quincke phenomenon led to the realization of chiral rollers (similar to circle swimmers) with spontaneously selected handedness of their motion and activity-dependent curvature of trajectories [43].<br>\r\nAnother fast-developing direction in the field of non-equilibrium active and driven colloids is the realization of systems characterized by non-reciprocity of interactions or memory effects and how they can lead to emerging collective phenomena. Due to the intrinsic nonequilibrium nature of active systems, the couplings between particles often deviate from the standard form derivable from a Hamiltonian. One intriguing example is a time-delayed coupling involving a discrete delay time (or a distribution of such times). Such a situation arises, for example, through a delay in communication or sensing, and can be artificially created via a feedback loop [44]. Another topic attracting a lot of attention in the community is based on active systems with nonreciprocal couplings that can arise, for example, through chemotaxis or phoretic interactions between self-propelling colloids [45], or through predator-prey or vision-cone interactions [46,47] in macroscopic active systems. On the collective level, is now well established that non-reciprocity can induce new types of phase transitions [48] and patterns with broken time- and parity symmetry, including travelling patterns [49,50] and globally chiral motion without chirality of the individual constituents [51]. While many of these studies have been pursued only at a mean field-theoretical level, there is also an increasing interest in understanding corresponding particle-scale effects, that can only be accessed by numerical simulations [52] or corresponding experiments. For example, non-reciprocal interactions may generate new types of self-assembled systems able to learn and to produce transition between different shapes [53]. Establishing the precise connection between the different length and time scales is still an important challenge. Here, computer simulations are an indispensable tool.<br>\r\nMany standard models of active motion implicitly assume an inert (equilibrium) environment yielding instantaneous friction and noise. In contrast, several recent studies [54,19] explore the impact of retarded friction as it arises in viscoelastic environments made, e.g., of polymers, liquid crystals, or biological tissues [55-57]. An extreme case is time-delay [44]. From a theoretical and computational perspective, retarded friction or, more generally, non-Markovian dynamics, still provides a severe challenge. This concerns, e.g., the extraction (or modelling) of memory kernels, but also the actual solution of the coupled equations of motion, each being subject to history effects. As a consequence, only few studies on the emerging collective behavior of active particles with memory are currently available, including collective effects in systems of feedback-driven colloids [58] and pattern formation in a non-Newtonian active system [59]. Advancing numerical methods capable of treating memory effects will become more and more important in view of the recent experimental progress in this field. Experimentally, the memory effects in the system can be induced, e.g., by temporal activity modulations at intermediate timescales of the interactions in the colloidal ensemble [60]. Such modulations generate active particles with partial memory (at the particle level) of their motion from the previous activity cycles (either through partial depolarization or remnant hydrodynamic flows induced by the particle motion). Novel dynamic patterns (such as localized multiple vortices, flocks, pulsating lattices) has been revealed in ensembles of Quinke rollers [60,61]. When coupled to the fluid flows, active particle with memory can produce activity shockwaves [62]. Also, it has been recently demonstrated that active colloidal ensembles realized by Quinke rollers can effectively develop “ensemble memory”, where the information about the dynamic state of the system is distributed over the whole ensemble [63]. This information can be effectively exploited to command subsequent collective polar states of the active colloidal ensemble through activity cycling [63] and can pave the way toward direct applications in different technological fields related to microfluidics and microrobotics.<br>\r\nDeveloping fundamental understanding of the complex colloidal dynamics in systems driven out-of-equilibrium by external fields represents a significant theoretical and computational challenge as it involves multi-body interactions, the overlapping of length- and timescales, and the coupling of particle interactions with the fluid flow. Some of the features may be understood using phenomenological using continuum descriptions [21-23] Nevertheless, the microscopic mechanisms leading to the dynamic self-assembly and their relations to the emergent behavior in active colloidal fluids with chirality, non-reciprocal interactions, and memory often remain unclear. <em>Computer simulations are practically the only method to theoretically investigate such questions. </em>However, modeling of the nonequilibrium dynamics presents a formidable computational challenge due to the complex many- body interactions and collective dynamics at different time and lengths scales. One of the main challenges is to properly account for the particle-fluid coupling. On a coarse-grained level, the fluid flow around colloids is modeled by molecular dynamics methods like Lattice-Boltzmann [64] and Multi Particle Collision Dynamics [65,66]. An alternative approach is to describe the colloidal dynamics by molecular dynamics simulation, or an amplitude equation (Ginzburg-Landau type equation) coupled to the Navier-Stokes equations describing large-scale time- averaged hydrodynamic flows induced by the colloids [67,68].<br>\r\n<br>\r\n<strong>Reference</strong><br>\r\n<br>\r\n[1] B. A. Grzybowski and G. M. Whitesides, “Dynamic Aggregation of Chiral Spinners” Science 296, 718-721 (2002).<br>\r\n[2] Y. Sumino, K. H. Nagai, Y. Shitaka, D. Tanaka, K. Yoshikawa, H. Chaté, K. Oiwa “Large-scale vortex lattice emerging from collectively moving microtubules”, Nature 483, 448-452 (2012).<br>\r\n[3] A Snezhko, I. Aranson, “Magnetic manipulation of self-assembled colloidal asters”, Nature Materials 10, 698-703 (2011).<br>\r\n[4] A. P. Petrov, X.-L. Wu, and A. Libchaber, “Fast-Moving Bacteria Self-Organize into Active Two- Dimensional Crystals of Rotating Cells”, Phys. Rev. Lett. 114, 158102 (2015).<br>\r\n[5] Bowick, M. J., Fakhri, N., Marchetti, M. C., & Ramaswamy, S. “Symmetry, thermodynamics, and topology in active matter”, Phys. Rev. X, 12(1), 010501 (2022).<br>\r\n[6] C. Scholz, A. Ldov, T. Pöschel, M. Engel, H. Löwen “Surfactants and rotelles in active chiral fluids” Science Advances 7 (16), eabf8998 (2021).<br>\r\n[7] G. Kokot, S. Das, R. Winkler, G. Gompper, I. Aranson, and A. Snezhko, “Active turbulence in a gas of self- assembled spinners”, Proc. Nat. Acad. Sci. U.S.A. 114, 12870 (2017).<br>\r\n[8] B. C. van Zuiden, J. Paulose, W. T. M. Irvine, D. Bartolo, and V. Vitelli, “Spatiotemporal order and emergent edge currents in active spinner materials” Proc. Natl Acad. Sci. USA 113, 12919 (2016).<br>\r\n[9] C. Scholz, M. Engel, and T. Pöschel, “Rotating robots move collectively and self-organize” Nature Comm. 9, 931 (2018).<br>\r\n[10] Han, M., Fruchart, M., Scheibner, C., Vaikuntanathan, S., De Pablo, J. J., Vitelli, V. “Fluctuating hydrodynamics of chiral active fluids”, Nature Physics, 17(11), 1260 (2021).<br>\r\n[11] T.H Tan, A. Mietke, J. Li, Y Chen, H. Higinbotham, PJ Foster, S Gokhale, Fakhri, N, “Odd dynamics of living chiral crystals”, Nature 607, 287 (2022).<br>\r\n[12] J. Dobnikar, A. Snezhko, A. Yethiraj, “Emergent colloidal dynamics in electromagnetic fields”, Soft Matter 9, 3693 (2013).<br>\r\n[13] F. Ma, S. Wang, D. T. Wu and N. Wu, \"Electric-field–induced assembly and propulsion of chiral colloidal clusters\" Proc. Natl. Acad. Sci. U. S. A. 112, 6307–6312 (2015).<br>\r\n[14] Z. Shen, A. Würger and J. S. Lintuvuori “Hydrodynamic self-assembly of active colloids: chiral spinners and dynamic crystals” Soft Matter, 15, 1508-1521 (2019).<br>\r\n[15] P. Tierno, R. Muruganathan, and T. M. Fischer, “Viscoelasticity of Dynamically Self-Assembled Paramagnetic Colloidal Clusters”, Phys. Rev. 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Ramaswamy, T. B. Liverpool, J. Prost, M. Rao, and R. Aditi Simha “Hydrodynamics of soft active matter” Reviews of Modern Physics 85 (3), 1143.<br>\r\n[22] I. Llopis and I. Pagonabarraga, “Dynamic regimes of hydrodynamically coupled self-propelling particles” Europhys. Lett. 75, 999 (2006).<br>\r\n[23] M. Leoni and T. B. Liverpool, “Dynamics and interactions of active rotors” Europhys. Lett. 92, 64004 (2010).<br>\r\n[24] N. H. P. Nguyen, D. Klotsa, M. Engel, and S. C. Glotzer, “Emergent Collective Phenomena in a Mixture of Hard Shapes through Active Rotation” Phys. Rev. Lett. 112, 075701 (2014).<br>\r\n[25] Z. Shen and J. S. Lintuvuori, “Hydrodynamic clustering and emergent phase separation of spherical spinners” Phys. Rev. Research 2, 013358 (2020).<br>\r\n[26] D. Banerjee, A. Souslov, A. G. Abanov, and V. Vitelli, “Odd viscosity in chiral active fluids” Nature Comm. 8, 1573 (2017).<br>\r\n[27] T. Markovich and T. C. 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Glotzer, Emergent collective phenomena in a mixture of hard shapes through active rotation. Phys. Rev. Lett. 112, 075701 (2014).<br>\r\n[39] Guo-Jun Liao, S.H.L. Klapp, \"Emergent vortices and phase separation in systems of chiral active particles with dipolar interactions\", Soft Matter, 2021, Advance Article (10.1039/d1sm00545f).<br>\r\n[40] K. Yeo, E. Lushi, P. M. Vlahovska, Collective dynamics in a binary mixture of hydrodynamically coupled microrotors. Phys. Rev. Lett. 114, 188301 (2015).<br>\r\n[41] K. Han, G. Kokot, S. Das, R. G. Winkler, G. Gompper, A. Snezhko, “Reconfigurable structure and tunable transport in synchronized active spinner materials.” Science advances 6 (12), eaaz8535 (2020).<br>\r\n[42] D. Levis, I. Pagonabarraga, B. Liebchen, Activity induced synchronization: mutual flocking, chiral self- sorting. Phys. Rev. Res. 1, 023026 (2019).<br>\r\n[43] B. Zhang, A. Sokolov, A.Snezhko, Reconfigurable emergent patterns in active chiral fluids. 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E 82 (R), 015301 (2010).<br>\r\n </p>", "image_description": "", "creation_date": "2026-01-19T10:25:40", "last_modification_date": "2026-01-26T16:41:11", "link_label": "Emergent dynamics of active colloids: chirality, non-reciprocity and memory", "link_url": "https://www.cecam.org/workshop-details/emergent-dynamics-of-active-colloids-chirality-non-reciprocity-and-memory-1496", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Sabine Klapp</strong>, Technical University Berlin ; <strong>Alexey Snezhko</strong>, Argonne National Laboratory ; <strong>Pietro Tierno</strong>, University of Barcelona", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119366/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70900, "title": "FAIR Data Management of Theoretical Spectroscopy and Green’s Function Methods", "slug": "fair-data-management-of-theoretical-spectroscopy-a", "event_url": "https://memento.epfl.ch/event/fair-data-management-of-theoretical-spectroscopy-a", "visual_url": "https://memento.epfl.ch/image/32289/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32289/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32289/max-size.jpg", "lang": "en", "start_date": "2026-04-20", "end_date": "2026-04-24", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/fair-data-management-of-theoretical-spectroscopy-and-greens-function-methods-1377\">https://www.cecam.org/workshop-details/fair-data-management-of-theoretical-spectroscopy-and-greens-function-methods-1377</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nBig-data-driven methodologies have emerged as a fundamental paradigm of science, but require an enormous amount of resources to achieve their promised impact. The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles [1] ensure that scientific data can be shared and reutilized, providing an efficient route for accumulating data and taking advantage of these powerful techniques. FAIR data management allows essential knowledge to be systematically extracted from data, accelerating discoveries and innovations across various domains [2]. Furthermore, open science is essential for the verifiability and reproducibility of results and has been a topic of major discussion over the last decade. In materials science, data-driven methodologies, coupled with the appropriate FAIR data management practices, are invaluable for the discovery of new materials due to the vast combinatorial space of chemical systems that emerge from the periodic table [3, 4]. Such methodologies have been successfully applied, e.g., to design and predict new materials with desired properties using ab-initio ground state simulations, i.e., data generated from Density Functional Theory (DFT) calculations [5]. However, there remains a critical gap in replicating this success in the context of other simulation frameworks. <br>\r\nTheoretical spectroscopy and Green's function method simulations [6, 7], including data simulated using the GW approximation, Time-Dependent Density Functional Theory (TDDFT), the Bethe-Salpeter equation (BSE), Dynamical Mean-Field Theory (DMFT), and Korringa-Kohn-Rostoker (KKR), pose especially difficult challenges in the context of FAIR data management. These simulations not only involve extensive computational resources and produce large datasets with associated complex workflows but are also executed using a large variety of public and in-house simulation software. At the same time, these methodologies are essential for understanding excited state properties of complex materials; they are more accurate than DFT calculations and provide better comparisons with experimental results since they incorporate excited states and electronic correlation effects in a more consistent manner [8]. <br>\r\nThere has recently been a number of individual efforts to improve the accessibility of data produced by theoretical spectroscopy and Green’s function methods through the usage of publicly accessible databases. For example, the Computational Materials Repository (CMR) [9] contains several individual databases, amongst which the Computational 2D Materials Database (C2DB) [10] contains GW and BSE data for a specific set of parameters and properties. The MaterialsCloud [11] database has some individual datasets published for these methodologies, however there is not a clear data structure for them. The NIST-JARVIS [12] database has a specific app for BeyondDFT simulations with DMFT data, but only for a specific simulation code. By making datasets findable, these efforts aim to avoid redundant computations and thus build upon existing work more efficiently. While these efforts represent an important step in the right direction, they fall short of fully achieving their goal due to a continued lack of consistency (i.e., <em>interoperability</em>) between individual databases. Moreover, these self-managed databases typically lack the ability to store the complete provenance of the simulated workflow, which is essential to ensure reproducibility. <br>\r\nRecently, FAIRmat [13], a consortium of the German research data infrastructure (NFDI) association, was formed to construct a scalable data infrastructure for Materials Science that can be easily customized for individual communities. This infrastructure consists of a primary software and repository called NOMAD [14]—a free web-service that enables the organization, analysis, sharing, and publishing of materials science data. One of the tasks within FAIRmat’s scope is to build support for theoretical spectroscopy and Green’s function simulations within NOMAD. Support for several of these methodologies have now been successfully built, and there already exists over 10 000 entries in the NOMAD repository containing GW [15], BSE [16], and DMFT [17] data, along with the full provenance of the corresponding complex workflows. The next step to developing a FAIR data infrastructure for these methods is to tackle the interoperability problem.<br>\r\nInteroperability within this domain is extremely challenging due to the heterogeneous character of theoretical spectroscopy and Green’s function simulations. Consequently, the adoption of common structures (e.g., describing the Green’s function, the self-energy, or the dielectric function) is the key for improving interoperability. Thus, various members of the community, including method developers, materials and data scientists, and data management experts, must come together to reach a consensus on specific common data structures.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://cmr.fysik.dtu.dk/\" target=\"_blank\">[1] Computational Materials Repository (CMR) website</a><br>\r\n<a href=\"http://dx.doi.org/10.1038/s41467-024-48169-5\" target=\"_blank\">[2] S. Di Cataldo, P. Worm, J. Tomczak, L. Si, K. Held, Nat. Commun., <strong>15</strong>, 3952 (2024)</a><br>\r\n<a href=\"http://dx.doi.org/10.1103/physrevmaterials.8.013801\" target=\"_blank\">[3] F. Meng, B. Maurer, F. Peschel, S. Selcuk, M. Hybertsen, X. Qu, C. Vorwerk, C. Draxl, J. Vinson, D. Lu, Phys. Rev. Materials, <strong>8</strong>, 013801 (2024)</a><br>\r\n<a href=\"http://dx.doi.org/10.1021/acs.jctc.5b00453\" target=\"_blank\">[4] M. van Setten, F. Caruso, S. Sharifzadeh, X. Ren, M. Scheffler, F. Liu, J. Lischner, L. Lin, J. Deslippe, S. Louie, C. Yang, F. Weigend, J. Neaton, F. Evers, P. Rinke, J. Chem. Theory Comput., <strong>11</strong>, 5665-5687 (2015)</a><br>\r\n<a href=\"http://dx.doi.org/10.21105/joss.05388\" target=\"_blank\">[5] M. Scheidgen, L. Himanen, A. Ladines, D. Sikter, M. Nakhaee, Á. Fekete, T. Chang, A. Golparvar, J. Márquez, S. Brockhauser, S. Brückner, L. Ghiringhelli, F. Dietrich, D. Lehmberg, T. Denell, A. Albino, H. Näsström, S. Shabih, F. Dobener, M. Kühbach, R. Mozumder, J. Rudzinski, N. Daelman, J. Pizarro, M. Kuban, C. Salazar, P. Ondračka, H. Bungartz, C. Draxl, JOSS., <strong>8</strong>, 5388 (2023)</a><br>\r\n<a href=\"https://www.fairmat-nfdi.eu/fairmat/\" target=\"_blank\">[6] FAIRmat website</a><br>\r\n<a href=\"http://dx.doi.org/10.1038/s41524-020-00440-1\" target=\"_blank\">[7] K. Choudhary, K. Garrity, A. Reid, B. DeCost, A. Biacchi, A. Hight Walker, Z. Trautt, J. Hattrick-Simpers, A. Kusne, A. Centrone, A. Davydov, J. Jiang, R. Pachter, G. Cheon, E. Reed, A. Agrawal, X. Qian, V. Sharma, H. Zhuang, S. Kalinin, B. Sumpter, G. Pilania, P. Acar, S. Mandal, K. Haule, D. Vanderbilt, K. Rabe, F. Tavazza, npj. Comput. Mater., <strong>6</strong>, 173 (2020)</a><br>\r\n<a href=\"http://dx.doi.org/10.1038/s41597-020-00637-5\" target=\"_blank\">[8] L. Talirz, S. Kumbhar, E. Passaro, A. Yakutovich, V. Granata, F. Gargiulo, M. Borelli, M. Uhrin, S. Huber, S. Zoupanos, C. Adorf, C. Andersen, O. Schütt, C. Pignedoli, D. Passerone, J. VandeVondele, T. Schulthess, B. Smit, G. Pizzi, N. Marzari, Sci. Data., <strong>7</strong>, 299 (2020)</a><br>\r\n<a href=\"http://dx.doi.org/10.1088/2053-1583/aacfc1\" target=\"_blank\">[9] S. Haastrup, M. Strange, M. Pandey, T. Deilmann, P. Schmidt, N. Hinsche, M. Gjerding, D. Torelli, P. Larsen, A. Riis-Jensen, J. Gath, K. Jacobsen, J. Jørgen Mortensen, T. Olsen, K. Thygesen, 2D Mater., <strong>5</strong>, 042002 (2018)</a><br>\r\n<a href=\"http://dx.doi.org/10.1038/sdata.2016.18\" target=\"_blank\">[10] M. Wilkinson, M. Dumontier, I. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J. Boiten, L. da Silva Santos, P. Bourne, J. Bouwman, A. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C. Evelo, R. Finkers, A. Gonzalez-Beltran, A. Gray, P. Groth, C. Goble, J. Grethe, J. Heringa, P. ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S. Lusher, M. Martone, A. Mons, A. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao, B. Mons, Sci. Data., <strong>3</strong>, 160018 (2016)</a><br>\r\n<a href=\"https://www.sciencedirect.com/journal/comptes-rendus-physique/vol/10/issue/6\" target=\"_blank\">[11] L. Reining et al., Comptes Rendus Physique 10, 6 (2009)</a><br>\r\n<a href=\"http://dx.doi.org/10.1088/2516-1075/ad48ec\" target=\"_blank\">[12] V. Blum, R. Asahi, J. Autschbach, C. Bannwarth, G. Bihlmayer, S. Blügel, L. Burns, T. Crawford, W. Dawson, W. de Jong, C. Draxl, C. Filippi, L. Genovese, P. Giannozzi, N. Govind, S. Hammes-Schiffer, J. Hammond, B. Hourahine, A. Jain, Y. Kanai, P. Kent, A. Larsen, S. Lehtola, X. Li, R. Lindh, S. Maeda, N. Makri, J. Moussa, T. Nakajima, J. Nash, M. Oliveira, P. Patel, G. Pizzi, G. Pourtois, B. Pritchard, E. Rabani, M. Reiher, L. Reining, X. Ren, M. Rossi, H. Schlegel, N. Seriani, L. Slipchenko, A. Thom, E. Valeev, B. Van Troeye, L. Visscher, V. Vlcek, H. Werner, D. Williams-Young, T. Windus, Electron. Struct., (2024)</a><br>\r\n<a href=\"http://dx.doi.org/10.1038/s41597-023-02501-8\" target=\"_blank\">[13] L. Ghiringhelli, C. Baldauf, T. Bereau, S. Brockhauser, C. Carbogno, J. Chamanara, S. Cozzini, S. Curtarolo, C. Draxl, S. Dwaraknath, Á. Fekete, J. Kermode, C. Koch, M. Kühbach, A. Ladines, P. Lambrix, M. Himmer, S. Levchenko, M. Oliveira, A. Michalchuk, R. Miller, B. Onat, P. Pavone, G. Pizzi, B. Regler, G. Rignanese, J. Schaarschmidt, M. Scheidgen, A. Schneidewind, T. Sheveleva, C. Su, D. Usvyat, O. Valsson, C. Wöll, M. Scheffler, Sci. Data., <strong>10</strong>, 626 (2023)</a><br>\r\n<a href=\"http://dx.doi.org/10.1038/s41524-019-0221-0\" target=\"_blank\">[14] J. Schmidt, M. Marques, S. Botti, M. Marques, npj. Comput. Mater., <strong>5</strong>, 83 (2019)</a><br>\r\n<a href=\"http://dx.doi.org/10.1002/advs.201900808\" target=\"_blank\">[15] L. Himanen, A. Geurts, A. Foster, P. Rinke, Advanced Science, <strong>6</strong>, (2019)</a><br>\r\n<a href=\"http://dx.doi.org/10.1038/s41586-022-04501-x\" target=\"_blank\">[16] M. Scheffler, M. Aeschlimann, M. Albrecht, T. Bereau, H. Bungartz, C. Felser, M. Greiner, A. Groß, C. Koch, K. Kremer, W. Nagel, M. Scheidgen, C. Wöll, C. Draxl, Nature, <strong>604</strong>, 635-642 (2022)</a><br>\r\n<a href=\"http://dx.doi.org/10.1557/mrs.2018.208\" target=\"_blank\">[17] C. Draxl, M. Scheffler, MRS Bull., <strong>43</strong>, 676-682 (2018)</a></p>", "image_description": "", "creation_date": "2026-01-19T10:12:45", "last_modification_date": "2026-01-26T16:39:31", "link_label": "FAIR Data Management of Theoretical Spectroscopy and Green’s Function Methods", "link_url": "https://www.cecam.org/workshop-details/fair-data-management-of-theoretical-spectroscopy-and-greens-function-methods-1377", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "Fabio Caruso, University of Kiel ; Claudia Draxl, Humboldt-Universität zu Berlin ; Jose M. Pizarro, Bundesanstalt für Materialforschung Und -prüfung ; Patrick Rinke, Technical University Munich ; Joseph Rudzinski, Humboldt-Universität zu Berlin", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119364/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70957, "title": "From Data to Dynamics: Machine Learning in Statistical Mechanics and Molecular Simulations", "slug": "from-data-to-dynamics-machine-learning-in-statis-2", "event_url": "https://memento.epfl.ch/event/from-data-to-dynamics-machine-learning-in-statis-2", "visual_url": "https://memento.epfl.ch/image/32346/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32346/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32346/max-size.jpg", "lang": "en", "start_date": "2026-10-14", "end_date": "2026-10-16", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/from-data-to-dynamics-machine-learning-in-statistical-mechanics-and-molecular-simulations-1487\">https://www.cecam.org/workshop-details/from-data-to-dynamics-machine-learning-in-statistical-mechanics-and-molecular-simulations-1487</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\nSince its introduction in the 1970s, molecular dynamics (MD) has become an indispensable computational microscope for studying complex biological systems at atomic resolution. It has enabled detailed investigations into protein folding, conformational dynamics, and ligand binding and unbinding. Over the past decade, increasing computational power has made microsecond-scale simulations routine, producing massive datasets that demand sophisticated analysis strategies [1]. Despite these advances, conventional MD simulations still face a fundamental limitation: many biologically relevant events occur over milliseconds to seconds—timescales largely inaccessible to standard MD.<br>\r\nTo bridge this gap, researchers increasingly turn to enhanced sampling techniques—such as metadynamics and umbrella sampling [2,3]—and coarse-grained (CG) modeling approaches [4]. These methods enable more comprehensive exploration of the system’s free energy landscape, yet their success critically depends on the selection of appropriate reaction coordinates or collective variables (CVs). CVs must capture the slowest, most functionally relevant motions to accurately reflect thermodynamic and kinetic behavior. However, identifying suitable CVs remains one of the field’s most challenging tasks, typically requiring domain expertise and iterative refinement [5, 6].<br>\r\nThis complexity has fueled growing interest in machine learning (ML) techniques, which are now transforming how MD simulations are analyzed, interpreted, and even conducted. ML methods have been applied to automate CV discovery, perform dimensionality reduction, build thermodynamic and kinetic models, and enhance sampling efficiency [7]. These models often employ artificial neural networks or graph neural networks to map high-dimensional molecular configurations—such as Cartesian coordinates or molecular descriptors—into low-dimensional representations suitable for analysis [8].<br>\r\nDepending on the structure and type of data, ML algorithms can be broadly categorized into supervised, unsupervised, and reinforcement learning paradigms [9]. Supervised learning uses labeled input-output pairs to predict properties such as molecular energies or binding affinities [10], while unsupervised learning enables the identification of latent features, such as CVs, directly from data [11].<br>\r\nA cornerstone of modern ML-driven simulation is the development of symmetry-aware molecular representations. The predictive power of ML models hinges on encoding physical symmetries—like rotation and translation—directly into the model. E(3)-equivariant neural networks have emerged as powerful tools for this purpose, significantly improving data efficiency and generalization in learning potential energy surfaces [12]. Ongoing research continues to explore the optimal balance between enforcing strict symmetry and retaining model flexibility.<br>\r\nMeanwhile, breakthroughs in structural prediction—most notably the advent of AlphaFold 3—have revolutionized how researchers obtain initial molecular configurations. AlphaFold now provides remarkably accurate models of not only proteins but also their complexes with nucleic acids, ions, and small-molecule ligands [13]. However, these are static snapshots. They cannot capture dynamic behaviors, allosteric transitions, or binding kinetics—areas where physics-based simulations remain indispensable. Initial benchmarks suggest that even state-of-the-art predictors still fall short in modeling protein dynamics and ranking ligand binding affinities, further emphasizing the role of MD [14].<br>\r\nTo address the dimensionality and sampling bottlenecks, unsupervised ML approaches such as time-lagged autoencoders have reframed CV identification as a data-driven task. More recently, generative models—including diffusion models and variational autoencoders—have emerged as a new frontier. These models can learn the full conformational landscape of biomolecules and enable enhanced sampling, in some cases eliminating the need for predefined CVs altogether [15].<br>\r\nOnce accurate structural models and CVs are established, ML can significantly improve the estimation of thermodynamic and kinetic properties. In drug discovery, for instance, predicting protein–ligand binding affinity remains a central challenge. ML potentials trained on quantum mechanical data can be combined with enhanced sampling to yield highly accurate free energy landscapes and binding kinetics—results previously unattainable due to computational limitations [16]. However, challenges in data quality, model interpretability, and transferability remain critical areas of ongoing investigation [17].<br>\r\nFinally, ML is driving a renaissance in CG modeling. Deep neural networks can now learn many-body CG potentials directly from all-atom simulations, capturing emergent properties and enhancing transferability [18]. These models open the door to longer, larger-scale simulations with greater physical accuracy.<br>\r\nIn this rapidly evolving context, it becomes imperative to critically assess both the promise and limitations of ML in biomolecular simulation. The excitement surrounding these developments must be tempered by careful validation and benchmarking. This workshop thus serves as a timely opportunity—especially for early-career researchers—to explore these cutting-edge methods, engage in constructive dialogue, and chart new directions in the application of machine learning to molecular dynamics and drug discovery.<br>\r\n <br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.98.146401\" target=\"_blank\">[1] J. Behler, M. Parrinello, Phys. Rev. Lett., <strong>98</strong>, 146401 (2007)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.sbi.2024.102972\" target=\"_blank\">[2] P. Sahrmann, G. Voth, Current Opinion in Structural Biology, <strong>90</strong>, 102972 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jcim.2c01127\" target=\"_blank\">[3] K. Kříž, L. Schmidt, A. Andersson, M. Walz, D. van der Spoel, J. Chem. Inf. Model., <strong>63</strong>, 412-431 (2023)</a><br>\r\n<a href=\"https://doi.org/10.3389/fmolb.2022.899805\" target=\"_blank\">[4] K. Ahmad, A. Rizzi, R. Capelli, D. Mandelli, W. Lyu, P. Carloni, Front. Mol. Biosci., <strong>9</strong>, (2022)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-physchem-083122-125941\" target=\"_blank\">[5] S. Mehdi, Z. Smith, L. Herron, Z. Zou, P. Tiwary, Annual Review of Physical Chemistry, <strong>75</strong>, 347-370 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1101/2025.04.07.647682\" target=\"_blank\">[6] H. Zheng, H. Lin, A. Alade, J. Chen, E. Monroy, M. Zhang, J. Wang, AlphaFold3 in Drug Discovery: A Comprehensive Assessment of Capabilities, Limitations, and Applications, 2025</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-024-07487-w\" target=\"_blank\">[7] J. Abramson, J. Adler, J. Dunger, R. Evans, T. Green, A. Pritzel, O. Ronneberger, L. Willmore, A. Ballard, J. Bambrick, S. Bodenstein, D. Evans, C. Hung, M. O’Neill, D. Reiman, K. Tunyasuvunakool, Z. Wu, A. Žemgulytė, E. Arvaniti, C. Beattie, O. Bertolli, A. Bridgland, A. Cherepanov, M. Congreve, A. Cowen-Rivers, A. Cowie, M. Figurnov, F. Fuchs, H. Gladman, R. Jain, Y. Khan, C. Low, K. Perlin, A. Potapenko, P. Savy, S. Singh, A. Stecula, A. Thillaisundaram, C. Tong, S. Yakneen, E. Zhong, M. Zielinski, A. Žídek, V. Bapst, P. Kohli, M. Jaderberg, D. Hassabis, J. Jumper, Nature, <strong>630</strong>, 493-500 (2024)</a><br>\r\n[8] Fabian B. Fuchs, Daniel E. Worrall, Volker Fischer, Max Welling, NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems, Article No.: 166, Pages 1970 - 1981 (2020)<br>\r\n<a href=\"https://doi.org/10.1080/00268976.2020.1737742\" target=\"_blank\">[9] H. Sidky, W. Chen, A. Ferguson, Molecular Physics, <strong>118</strong>, (2020)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.sbi.2019.12.016\" target=\"_blank\">[10] Y. Wang, J. Lamim Ribeiro, P. Tiwary, Current Opinion in Structural Biology, <strong>61</strong>, 139-145 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-018-0337-2\" target=\"_blank\">[11] K. Butler, D. Davies, H. Cartwright, O. Isayev, A. Walsh, Nature, <strong>559</strong>, 547-555 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-physchem-042018-052331\" target=\"_blank\">[12] F. Noé, A. Tkatchenko, K. Müller, C. Clementi, Annu. Rev. Phys. Chem., <strong>71</strong>, 361-390 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1080/23746149.2021.2006080\" target=\"_blank\">[13] S. Kaptan, I. Vattulainen, Advances in Physics: X, <strong>7</strong>, (2022)</a><br>\r\n<a href=\"https://doi.org/10.1002/wcms.1455\" target=\"_blank\">[14] V. Limongelli, WIREs. Comput. Mol. Sci., <strong>10</strong>, (2020)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.chemrev.0c01195\" target=\"_blank\">[15] A. Glielmo, B. Husic, A. Rodriguez, C. Clementi, F. Noé, A. Laio, Chem. Rev., <strong>121</strong>, 9722-9758 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.sbi.2018.11.005\" target=\"_blank\">[16] A. Pak, G. Voth, Current Opinion in Structural Biology, <strong>52</strong>, 119-126 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1021/jacs.6b05602\" target=\"_blank\">[17] M. Lelimousin, V. Limongelli, M. Sansom, J. Am. Chem. Soc., <strong>138</strong>, 10611-10622 (2016)</a><br>\r\n<a href=\"https://doi.org/10.3390/e16010163\" target=\"_blank\">[18] C. Abrams, G. Bussi, Entropy, <strong>16</strong>, 163-199 (2013)</a>\r\n</p><div class=\"active tab-pane\"> </div>", "image_description": "", "creation_date": "2026-01-26T16:07:22", "last_modification_date": "2026-01-26T16:45:31", "link_label": "From Data to Dynamics: Machine Learning in Statistical Mechanics and Molecular Simulations", "link_url": "https://www.cecam.org/workshop-details/from-data-to-dynamics-machine-learning-in-statistical-mechanics-and-molecular-simulations-1487", "canceled": "False", "cancel_reason": "", "place_and_room": "Aula Magna, USI Lugano", "url_place_and_room": "https://www.desk.usi.ch/en/lugano-campus-map-access-facilities", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Daniele Angioletti, </strong>Università della Svizzera Italiana (USI) ; <strong>Vincenzo Maria D'Amore, </strong>University of Naples \"Federico II\" ; <strong>Marco De Vivo, </strong>Istituto Italiano di Tecnologia ; <strong>Francesco Saverio Di Leva, </strong>University of Naples Federico II ; <strong>Vittorio Limongelli, </strong>Università della Svizzera Italiana USI Lugano ; <strong>Gregory Voth, </strong>University of Chicago", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119454/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70956, "title": "G protein-coupled receptors functional dynamics revealed by experimental and computational structural data", "slug": "g-protein-coupled-receptors-functional-dynamics-re", "event_url": "https://memento.epfl.ch/event/g-protein-coupled-receptors-functional-dynamics-re", "visual_url": "https://memento.epfl.ch/image/32345/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32345/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32345/max-size.jpg", "lang": "en", "start_date": "2026-10-07", "end_date": "2026-10-09", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/g-protein-coupled-receptors-functional-dynamics-revealed-by-experimental-and-computational-structural-data-1488\">https://www.cecam.org/workshop-details/g-protein-coupled-receptors-functional-dynamics-revealed-by-experimental-and-computational-structural-data-1488</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nG protein-coupled receptors (GPCRs) represent a vast and diverse class of transmembrane proteins that orchestrate a wide range of physiological processes by responding to both endogenous and exogenous ligands [1,2]. These receptors are essential to critical functions such as metabolism, immune regulation, neuronal signaling, and sensory perception - including vision and olfaction. Due to their physiological relevance and membrane accessibility, GPCRs are the targets of approximately 34% of all prescribed medications, accounting for nearly 27% of the global pharmaceutical market [3]. <br>\r\nDespite their pharmaceutical importance, key aspects of GPCR function remain elusive. The canonical activation model posits that agonist binding to the extracellular orthosteric site triggers allosteric changes - most notably, the outward displacement of transmembrane helices 5 (TM5) and 6 (TM6) on the intracellular side - ultimately leading to receptor activation [2-4]. However, recent evidence suggests a more nuanced mechanism. In several GPCRs, activation appears to involve cooperative engagement between the agonist and the G protein. For example, the G protein may disrupt an \"inactivating ionic lock\" - a salt bridge between TM3 and TM6 - while the agonist stabilizes the active conformation. In some receptors, this is complemented by the formation of an “activating ionic lock” between TM5 and TM6 [5-8]. These dual contributions are considered thermodynamically essential for full activation [7].<br>\r\nAdding further complexity, GPCR activity is regulated by conformational microswitches and finely tuned intra-protein interaction networks. These dynamic rearrangements are difficult to capture and often elude direct correlation with functional outcomes. Moreover, allosteric ligands - which bind sites distinct from the orthosteric pocket - are being increasingly identified [9-12], along with small molecules capable of biased signaling, i.e., preferential activation of specific intracellular pathways [11-13, 16, 17]. These findings reveal a rich and underexplored conformational landscape that governs GPCR signaling. In addition, native membrane components—such as lipids and interacting proteins, including GPCR oligomers—are known to significantly modulate receptor function [11, 18-22].<br>\r\nTo disentangle these intricacies, computational modeling has become indispensable, offering atomistic insight into GPCR conformational dynamics and mechanistic understanding [1-2, 7, 11, 14, 16–21, 23]. Nevertheless, key questions remain - particularly regarding the structural basis of biased signaling, strategies for leveraging allosteric networks in pharmacology, and the modulatory role of the lipid environment. Addressing these gaps is crucial for both fundamental biology and the rational design of next-generation GPCR-targeting drugs with improved selectivity and safety profiles. <br>\r\nThese scientific challenges form the foundation of our upcoming workshop, which will focus on the latest experimental and computational approaches for studying the functional dynamics of GPCRs. Given the profound health, economic, and societal implications of modulating these receptors with precision, we aim to strengthen the interdisciplinary nature of the event by increasing the representation of experimental research and integrating cutting-edge artificial intelligence applications into the program.<br>\r\nBuilding upon the success of the 2022 and 2024 editions - which led to new collaborations and a landmark publication in <em>Nature Reviews Drug Discovery</em> [24] - our goal is to further enhance communication and collaboration between experimentalists and theoreticians. The workshop will serve as a reference point for young scientists and students, offering a platform to interact with leading international experts. We are confident that this initiative will foster insightful discussions and contribute meaningfully to advancing the field of GPCR pharmacology.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1038/nrd.2017.229\" target=\"_blank\">[1] J. Smith, R. Lefkowitz, S. Rajagopal, Nat. Rev. Drug. Discov., <strong>17</strong>, 243-260 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41573-024-01083-3\" target=\"_blank\">[2] P. Conflitti, E. Lyman, M. Sansom, P. Hildebrand, H. Gutiérrez-de-Terán, P. Carloni, T. Ansell, S. Yuan, P. Barth, A. Robinson, C. Tate, D. Gloriam, S. Grzesiek, M. Eddy, S. Prosser, V. Limongelli, Nat. Rev. Drug. Discov., <strong>24</strong>, 251-275 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41589-024-01682-6\" target=\"_blank\">[3] L. Picard, A. Orazietti, D. Tran, A. Tucs, S. Hagimoto, Z. Qi, S. Huang, K. Tsuda, A. Kitao, A. Sljoka, R. Prosser, Nat. Chem. Biol., <strong>21</strong>, 71-79 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.drudis.2020.10.006\" target=\"_blank\">[4] B. Huang, C. St. Onge, H. Ma, Y. Zhang, Drug Discovery Today, <strong>26</strong>, 189-199 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-023-42082-z\" target=\"_blank\">[5] D. Di Marino, P. Conflitti, S. Motta, V. Limongelli, Nat. Commun., <strong>14</strong>, 6439 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.ceb.2018.10.007\" target=\"_blank\">[6] G. Milligan, R. Ward, S. Marsango, Current Opinion in Cell Biology, <strong>57</strong>, 40-47 (2019)</a><br>\r\n<a href=\"https://doi.org/10.7554/elife.73901\" target=\"_blank\">[7] S. Huang, O. Almurad, R. Pejana, Z. Morrison, A. Pandey, L. Picard, M. Nitz, A. Sljoka, R. Prosser, eLife, <strong>11</strong>, (2022)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-pharmtox-010919-023411\" target=\"_blank\">[8] A. Duncan, W. Song, M. Sansom, Annu. Rev. Pharmacol. Toxicol., <strong>60</strong>, 31-50 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-025-60003-0\" target=\"_blank\">[9] A. Morales-Pastor, T. Miljuš, M. Dieguez-Eceolaza, T. Stępniewski, V. Ledesma-Martin, F. Heydenreich, T. Flock, B. Plouffe, C. Le Gouill, J. Duchaine, D. Sykes, C. Nicholson, E. Koers, W. Guba, A. Rufer, U. Grether, M. Bouvier, D. Veprintsev, J. Selent, Nat. Commun., <strong>16</strong>, 5265 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-022-05588-y\" target=\"_blank\">[10] A. Faouzi, H. Wang, S. Zaidi, J. DiBerto, T. Che, Q. Qu, M. Robertson, M. Madasu, A. El Daibani, B. Varga, T. Zhang, C. Ruiz, S. Liu, J. Xu, K. Appourchaux, S. Slocum, S. Eans, M. Cameron, R. Al-Hasani, Y. Pan, B. Roth, J. McLaughlin, G. Skiniotis, V. Katritch, B. Kobilka, S. Majumdar, Nature, <strong>613</strong>, 767-774 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-022-31652-2\" target=\"_blank\">[11] M. Wall, E. Hill, R. Huckstepp, K. Barkan, G. Deganutti, M. Leuenberger, B. Preti, I. Winfield, S. Carvalho, A. Suchankova, H. Wei, D. Safitri, X. Huang, W. Imlach, C. La Mache, E. Dean, C. Hume, S. Hayward, J. Oliver, F. Zhao, D. Spanswick, C. Reynolds, M. Lochner, G. Ladds, B. Frenguelli, Nat. Commun., <strong>13</strong>, 4150 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41580-018-0049-3\" target=\"_blank\">[12] D. Wootten, A. Christopoulos, M. Marti-Solano, M. Babu, P. Sexton, Nat. Rev. Mol. Cell. Biol., <strong>19</strong>, 638-653 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41594-017-0011-7\" target=\"_blank\">[13] D. Hilger, M. Masureel, B. Kobilka, Nat. Struct. Mol. Biol., <strong>25</strong>, 4-12 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-025-57034-y\" target=\"_blank\">[14] D. Aranda-García, T. Stepniewski, M. Torrens-Fontanals, A. García-Recio, M. Lopez-Balastegui, B. Medel-Lacruz, A. Morales-Pastor, A. Peralta-García, M. Dieguez-Eceolaza, D. Sotillo-Nuñez, T. Ding, M. Drabek, C. Jacquemard, J. Jakowiecki, W. Jespers, M. Jiménez-Rosés, V. Jun-Yu-Lim, A. Nicoli, U. Orzel, A. Shahraki, J. Tiemann, V. Ledesma-Martin, F. Nerín-Fonz, S. Suárez-Dou, O. Canal, G. Pándy-Szekeres, J. Mao, D. Gloriam, E. Kellenberger, D. Latek, R. Guixà-González, H. Gutiérrez-de-Terán, I. Tikhonova, P. Hildebrand, M. Filizola, M. Babu, A. Di Pizio, S. Filipek, P. Kolb, A. Cordomi, T. Giorgino, M. Marti-Solano, J. Selent, Nat. Commun., <strong>16</strong>, 2020 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-018-0259-z\" target=\"_blank\">[15] D. Thal, A. Glukhova, P. Sexton, A. Christopoulos, Nature, <strong>559</strong>, 45-53 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.tips.2020.12.005\" target=\"_blank\">[16] L. Slosky, M. Caron, L. Barak, Trends in Pharmacological Sciences, <strong>42</strong>, 283-299 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.apsb.2023.07.020\" target=\"_blank\">[17] C. Zhu, X. Lan, Z. Wei, J. Yu, J. Zhang, Acta Pharmaceutica Sinica B, <strong>14</strong>, 67-86 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.chempr.2024.08.004\" target=\"_blank\">[18] V. D’Amore, P. Conflitti, L. Marinelli, V. Limongelli, Chem, <strong>10</strong>, 3678-3698 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41557-023-01238-6\" target=\"_blank\">[19] A. Mafi, S. Kim, W. Goddard, Nat. Chem., <strong>15</strong>, 1127-1137 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41594-024-01334-2\" target=\"_blank\">[20] H. Batebi, G. Pérez-Hernández, S. Rahman, B. Lan, A. Kamprad, M. Shi, D. Speck, J. Tiemann, R. Guixà-González, F. Reinhardt, P. Stadler, M. Papasergi-Scott, G. Skiniotis, P. Scheerer, B. Kobilka, J. Mathiesen, X. Liu, P. Hildebrand, Nat. Struct. Mol. Biol., <strong>31</strong>, 1692-1701 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.cell.2015.04.043\" target=\"_blank\">[21] A. Manglik, T. Kim, M. Masureel, C. Altenbach, Z. Yang, D. Hilger, M. Lerch, T. Kobilka, F. Thian, W. Hubbell, R. Prosser, B. Kobilka, Cell, <strong>161</strong>, 1101-1111 (2015)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.cell.2020.03.003\" target=\"_blank\">[22] M. Congreve, C. de Graaf, N. Swain, C. Tate, Cell, <strong>181</strong>, 81-91 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41573-025-01139-y\" target=\"_blank\">[23] J. Lorente, A. Sokolov, G. Ferguson, H. Schiöth, A. Hauser, D. Gloriam, Nat. Rev. Drug. Discov., <strong>24</strong>, 458-479 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1111/bph.16495\" target=\"_blank\">[24] M. Lopez‐Balastegui, T. Stepniewski, M. Kogut‐Günthel, A. Di Pizio, M. Rosenkilde, J. Mao, J. Selent, British. J. Pharmacology., <strong>182</strong>, 3211-3224 (2024)</a>\r\n</p><div class=\"active tab-pane\"> </div>", "image_description": "", "creation_date": "2026-01-26T16:00:31", "last_modification_date": "2026-01-26T16:45:08", "link_label": "G protein-coupled receptors functional dynamics revealed by experimental and computational structura", "link_url": "https://www.cecam.org/workshop-details/g-protein-coupled-receptors-functional-dynamics-revealed-by-experimental-and-computational-structural-data-1488", "canceled": "False", "cancel_reason": "", "place_and_room": "Aula Magna, USI Lugano", "url_place_and_room": "https://www.desk.usi.ch/en/lugano-campus-map-access-facilities", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Vittorio Limongelli</strong>, Università della Svizzera Italiana USI Lugano ; <strong>Scott Prosser</strong>, University of Toronto ; <strong>Stefano Raniolo</strong>, Università della Svizzera Italiana ; <strong>Jana Selent</strong>, Hospital Del Mar Medical Research Institute", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119453/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70952, "title": "Multi-scale and multi-purpose simulations of DNA: the importance of data", "slug": "multi-scale-and-multi-purpose-simulations-of-dna-t", "event_url": "https://memento.epfl.ch/event/multi-scale-and-multi-purpose-simulations-of-dna-t", "visual_url": "https://memento.epfl.ch/image/32340/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32340/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32340/max-size.jpg", "lang": "en", "start_date": "2026-08-26", "end_date": "2026-08-28", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/multi-scale-and-multi-purpose-simulations-of-dna-the-importance-of-data-1484\">https://www.cecam.org/workshop-details/multi-scale-and-multi-purpose-simulations-of-dna-the-importance-of-data-1484</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nDNA is a dramatic example of a multiscale system, where Å-scale details impact the global properties of a meter-long fiber and where femtosecond processes can impact on the entire genome years later. This implies that any theoretical study on DNA should take into consideration the vast variety of space and time scales, making it necessary the adoption of multi-physics approaches, covering the entire range of theoretical methods from quantum chemistry to rough mesoscopic models. Within this scenario the importance of data to bias simulations and as a reference to calibrate low resolution methods (Dans et al. 2017; Neguembor et al. 2022; Schultz et al. 2025).<br>\r\nLarge efforts have been made to develop accurate low level DFT and semiempirical methods that can be data-providers for a new generation of force-field, as well as integrated in QM/MM packages for an efficient representation of DNA reactivity (Aranda et al. 2019). Atomistic force-field have gained accuracy, showing good ability to reproduce unusual forms of DNA and long segments of DNA in the context of chromatin (Collepardo-Guevara et al. 2015; Genna et al. 2025) and providing very useful data for the calibration of lower level coarse-grained or mesoscopic methods(De Pablo 2011; Farré-Gil et al. 2024) ,which have gained sequence specificity, scalability and computational efficiency, allowing to simulate kilo-to-megabase fragments of DNA. Very remarkable efforts have been made to move up these methods to represent chromatin, which requires the introduction of biases derived from experimental data (MNAseq, chromosome conformation capture, and even static or dynamic pictures obtained by ultra-resolution microscopy, and others (Buitrago et al. 2019; Neguembor et al. 2022; Li and Schlick 2024)). This has opened the possibility to recover dynamic “base-pair” resolution pictures of chromatin and study aspects from local and global chromatin rearrangements to inter-play between effector proteins and nucleosomes, the impact of lesions in chromatin structure, and even the role of phase separation in defining local chromatin arrangements (Joseph et al. 2021; Liu et al. 2025; Park et al. 2025).<br>\r\nAs the target systems move from the small atomistic detail to the entire chromatin fiber, the community is broken into different sub-communities. This generates a risk of disconnection, which would lead to a waste of effort reformulating solutions to already solved problems, or ignoring the characteristic that a method should have to maintain coherence with more accurate models, or to scale to represent systems of real biological interest. This will be the main objective of this meeting, which will join a variety of sub-communities with a common interest: the DNA.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1038/s41929-019-0290-y\" target=\"_blank\">[1] J. Aranda, M. Terrazas, H. Gómez, N. Villegas, M. Orozco, Nat. Catal., <strong>2</strong>, 544-552 (2019)</a><br>\r\n<a href=\"https://doi.org/10.1093/nar/gkz759\" target=\"_blank\">[2] D. Buitrago, L. Codó, R. Illa, P. de Jorge, F. Battistini, O. Flores, G. Bayarri, R. Royo, M. Del Pino, S. Heath, A. Hospital, J. Gelpí, I. Heath, M. Orozco, Nucleic Acids Research, <strong>47</strong>, 9511-9523 (2019)</a><br>\r\n<a href=\"https://doi.org/10.1021/jacs.5b04086\" target=\"_blank\">[3] R. Collepardo-Guevara, G. Portella, M. Vendruscolo, D. Frenkel, T. Schlick, M. Orozco, J. Am. Chem. Soc., <strong>137</strong>, 10205-10215 (2015)</a><br>\r\n<a href=\"https://doi.org/10.1093/nar/gkw1355\" target=\"_blank\">[4] P. Dans, I. Ivani, A. Hospital, G. Portella, C. González, M. Orozco, Nucleic. Acids. Res., gkw1355 (2017)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-physchem-032210-103458\" target=\"_blank\">[5] J. de Pablo, Annu. Rev. Phys. Chem., <strong>62</strong>, 555-574 (2011)</a><br>\r\n<a href=\"https://doi.org/10.1093/nar/gkae444\" target=\"_blank\">[6] D. Farré-Gil, J. Arcon, C. Laughton, M. Orozco, Nucleic Acids Research, <strong>52</strong>, 6791-6801 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1093/nar/gkaf170\" target=\"_blank\">[7] V. Genna, G. Portella, A. Sala, M. Terrazas, I. Serrano-Chacón, J. González, N. Villegas, L. Mateo, C. Castellazzi, M. Labrador, A. Aviño, A. Hospital, A. Gandioso, P. Aloy, I. Brun-Heath, C. Gonzalez, R. Eritja, M. Orozco, Nucleic Acids Research, <strong>53</strong>, (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s43588-021-00155-3\" target=\"_blank\">[8] J. Joseph, A. Reinhardt, A. Aguirre, P. Chew, K. Russell, J. Espinosa, A. Garaizar, R. Collepardo-Guevara, Nat. Comput. Sci., <strong>1</strong>, 732-743 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1093/nar/gkad1121\" target=\"_blank\">[9] Z. Li, T. Schlick, Nucleic Acids Research, <strong>52</strong>, 583-599 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.biochem.4c00737\" target=\"_blank\">[10] S. Liu, C. Wang, B. Zhang, Biochemistry, <strong>64</strong>, 1750-1761 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41594-022-00839-y\" target=\"_blank\">[11] M. Neguembor, J. Arcon, D. Buitrago, R. Lema, J. Walther, X. Garate, L. Martin, P. Romero, J. AlHaj Abed, M. Gut, J. Blanc, M. Lakadamyali, C. Wu, I. Brun Heath, M. Orozco, P. Dans, M. Cosma, Nat. Struct. Mol. Biol., <strong>29</strong>, 1011-1023 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-025-08971-7\" target=\"_blank\">[12] S. Park, R. Merino-Urteaga, V. Karwacki-Neisius, G. Carrizo, A. Athreya, A. Marin-Gonzalez, N. Benning, J. Park, M. Mitchener, N. Bhanu, B. Garcia, B. Zhang, T. Muir, E. Pearce, T. Ha, Nature, (2025)</a><br>\r\n<a href=\"https://doi.org/10.1002/wcms.70024\" target=\"_blank\">[13] E. Schultz, J. Kaplan, Y. Wu, S. Kyhl, R. Willett, J. de Pablo, WIREs. Comput. Mol. Sci., <strong>15</strong>, (2025)</a></p>", "image_description": "", "creation_date": "2026-01-26T15:07:17", "last_modification_date": "2026-02-09T10:46:40", "link_label": "Multi-scale and multi-purpose simulations of DNA: the importance of data", "link_url": "https://www.cecam.org/workshop-details/multi-scale-and-multi-purpose-simulations-of-dna-the-importance-of-data-1484", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Juan J De Pablo</strong>, University of Chicago ; <strong>Adam Hospital</strong>, IRB Barcelona ; <strong>Modesto Orozco</strong>, IRB Barcelona", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119444/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70954, "title": "SpectroDynamics 2026: Connecting Computational Spectroscopic Methods Across the Electromagnetic Spectrum", "slug": "spectrodynamics-2026-connecting-computational-sp-2", "event_url": "https://memento.epfl.ch/event/spectrodynamics-2026-connecting-computational-sp-2", "visual_url": "https://memento.epfl.ch/image/32342/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32342/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32342/max-size.jpg", "lang": "en", "start_date": "2026-09-07", "end_date": "2026-09-11", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/spectrodynamics-2026-connecting-computational-spectroscopic-methods-across-the-electromagnetic-spectrum-1489\">https://www.cecam.org/workshop-details/spectrodynamics-2026-connecting-computational-spectroscopic-methods-across-the-electromagnetic-spectrum-1489</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nLight provides one of the most detailed windows into molecules and matter. Modern light sources allow the probing of equilibrium and non-equilibrium phenomena with Å‐level spatial resolution and femto‐ to attosecond temporal precision. Advances in ultrafast laser technology, together with the rise of X-ray free‐electron lasers and next-generation synchrotron sources, have repeatedly pushed the boundaries of spectroscopic methods from low‐frequency collective modes in biomolecules to electronic and core‐level dynamics. An extensive toolbox of linear and multidimensional spectroscopic techniques now spans the entire electromagnetic spectrum. Terahertz (THz) pulses can coherently drive intermolecular and lattice vibrations in solids and soft matter [1], Mid‐IR and Raman methods map vibrational energy (re)distribution in liquids and vibrational signatures of individual modes in complex molecules [2]. Visible spectroscopy tracks ultrafast charge dynamics in chromophores [3] and photochemical molecular pathways [4], while X-ray sources from free-electron lasers and high-harmonic generation setups enabled time-resolved X-ray diffraction of gas‐phase [5] and condensed systems [6].<br>\r\nDespite sharing common scientific goals, the respective communities have traditionally operated in relative disconnection from each other, relying on different approximations, targeting different observables, and employing distinct numerical implementations. This disconnection manifests, among other symptoms, in the fact that schools, conferences, and workshops are often dedicated to a specific frequency window (e.g. IR spectroscopy) or to simulation methods targeting a class of specific processes (e.g. vibrational dynamics). Opportunities for dialogue and the building of a shared language are lacking. In fact, while preparing this proposal, it became evident that even foundational terms like ab initio or quantum dynamics carry different meanings across communities.<br>\r\nTo address this fragmentation, the proposed CECAM school brings together researchers from diverse backgrounds to foster mutual understanding and build lasting conceptual bridges. Over five days, participants will engage with both the theoretical foundations and practical implementations of spectroscopies across different communities. We will highlight the fact that despite their apparent differences, all spectroscopic methods can be traced back to a common starting point: a light–matter Hamiltonian that includes the quantum description of electronic, nuclear, and photonic degrees of freedom. From this unified framework, we will explore how different approximations—introduced at various stages—lead to the distinct theoretical approaches adopted in each field.<br>\r\nThe first part of the school will focus on approaches that solve the exact quantum molecular dynamics in reduced dimensionality. Within this framework, molecules are treated fully quantum-mechanically, while light is treated classically as an external perturbation within the dipole approximation. From the matter perspective, this means that the full electron + nuclear wavefunction is accessible, offering a great level of detail and information, and the accurate treatment of non-Born-Oppenheimer dynamics. From the light perspective, this means that spectroscopic signals are conveniently calculated via the response function approach (RFA) [7], which is however only valid in the weak field limit. Recently, the RFA has been used to design and simulate several spectroscopic signals of femtosecond molecular photochemistry using novel X-ray pulse sources [8], including stimulated X-ray Raman [9], transient X-ray absorption and transmission [10], and many others [11].<br>\r\nIn the second part, we will shift the focus to longer time scales with more degrees of freedom and study larger molecules in explicit environments (solvent, substrate, etc). In these cases, it is common practice to apply the Born-Oppenheimer approximation and take the classical limit for the nuclei, while keeping the electrons quantum, leading to (finite temperature) molecular dynamics (MD) approaches. To make these simulations computationally tractable, while retaining an explicit description of the electrons, electron–electron interactions are typically simplified using ground-state density functional theory (DFT). This approach, commonly referred to as ab initio molecular dynamics (AIMD), enables the simulation of vibrational spectroscopies such as infrared (IR) and Raman [12,13], as well as surface-specific techniques like sum-frequency generation (SFG) [14,15]. To access larger system sizes and longer simulation timescales, forces can be derived from classical interatomic potentials, facilitating the convergence of multidimensional spectroscopic observables such as THz-Raman spectra [16]. Alternatively, forces can be learned directly from first-principles data using machine-learning (ML) models, enabling ML-driven molecular dynamics and spectroscopy [17-21]. Through path integral techniques, the quantum nature of the nuclei can be recovered, which is particularly important for systems containing light atoms, such as hydrogen [22-24].<br>\r\nThe third part of the school will explore what happens when the primary interest shifts from vibrational to electronic dynamics. In this context, the electron dynamics at the DFT level can be incorporated by considering its time-dependent version (TDDFT), where the exchange-correlation functionals are usually adiabatic. With this method, UV-visible absorption [25], circular dichroism [26], inelastic X-ray scattering, and electron energy loss [27], and other spectroscopies can be computed. Finally, there are situations in which strong light-matter coupling demands an explicit treatment of the photons [28]. These can be reintroduced either by dressing the Kohn-Sham Hamiltonian with electron-photon exchange-correlation potentials (known as quantum-electrodynamics DFT, or QEDFT) [29] or by a semiclassical treatment of the photons solving Maxwell’s equations (the Maxwell-TDDFT method)[30]. These methods enable the calculation of spectra in cavities or arbitrary electromagnetic environments [31], and can account for polaritonic phenomena, radiative lifetimes, superradiance, and many more.<br>\r\nThis school brings together leading experts from exact quantum dynamics, ab initio MD, ML‐enabled simulations, and Maxwell–TDDFT to forge a common language and cross‐fertilize ideas. Lectures will cover both the fundamental principles and the latest advances in each area, highlighting current applications and open challenges. Complementing the lectures, hands-on tutorials will reinforce foundational concepts and provide important hands-on experience on several popular computational approaches (see hands-on section below).<br>\r\nBy spanning the electromagnetic spectrum and the hierarchy of theoretical methods, this school will equip PhD students and postdocs with a unified, multi‐scale, and inter-community perspective on quantum dynamics and spectroscopy. Participants will leave with both a solid grounding in foundational techniques and direct experience of the latest computational frontiers, ready to tackle open challenges in molecular and materials science.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1063/1.4901216\" target=\"_blank\">[1] P. Hamm, The Journal of Chemical Physics, <strong>141</strong>, (2014)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jctc.3c00967\" target=\"_blank\">[2] M. Svendsen, K. Thygesen, A. Rubio, J. Flick, J. Chem. Theory Comput., <strong>20</strong>, 926-936 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevb.111.085114\" target=\"_blank\">[3] F. Bonafé, E. Albar, S. Ohlmann, V. Kosheleva, C. Bustamante, F. Troisi, A. Rubio, H. Appel, Phys. Rev. B, <strong>111</strong>, 085114 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1103/physreva.90.012508\" target=\"_blank\">[4] M. Ruggenthaler, J. Flick, C. Pellegrini, H. Appel, I. Tokatly, A. Rubio, Phys. Rev. A, <strong>90</strong>, 012508 (2014)</a><br>\r\n<a href=\"https://doi.org/10.1021/acsphotonics.9b00768\" target=\"_blank\">[5] J. Flick, D. Welakuh, M. Ruggenthaler, H. Appel, A. Rubio, ACS Photonics, <strong>6</strong>, 2757-2778 (2019)</a><br>\r\n<a href=\"https://doi.org/10.1063/1.3503594\" target=\"_blank\">[6] A. Sakko, A. Rubio, M. Hakala, K. Hämäläinen, The Journal of Chemical Physics, <strong>133</strong>, (2010)</a><br>\r\n<a href=\"https://doi.org/10.1039/b903200b\" target=\"_blank\">[7] D. Varsano, L. Espinosa-Leal, X. Andrade, M. Marques, R. di Felice, A. Rubio, Phys. Chem. Chem. Phys., <strong>11</strong>, 4481 (2009)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevb.54.4484\" target=\"_blank\">[8] K. Yabana, G. Bertsch, Phys. Rev. B, <strong>54</strong>, 4484-4487 (1996)</a><br>\r\n<a href=\"https://doi.org/10.1039/c9fd00056a\" target=\"_blank\">[9] Y. Litman, J. Behler, M. Rossi, Faraday Discuss., <strong>221</strong>, 526-546 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-physchem-090722-124705\" target=\"_blank\">[10] S. Althorpe, Annual Review of Physical Chemistry, <strong>75</strong>, 397-420 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.chemrev.5b00674\" target=\"_blank\">[11] M. Ceriotti, W. Fang, P. Kusalik, R. McKenzie, A. Michaelides, M. Morales, T. Markland, Chem. Rev., <strong>116</strong>, 7529-7550 (2016)</a><br>\r\n<a href=\"https://doi.org/10.1039/c7sc02267k\" target=\"_blank\">[12] M. Gastegger, J. Behler, P. Marquetand, Chem. Sci., <strong>8</strong>, 6924-6935 (2017)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpca.1c10417\" target=\"_blank\">[13] R. Han, R. Ketkaew, S. Luber, J. Phys. Chem. A, <strong>126</strong>, 801-812 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpclett.3c00398\" target=\"_blank\">[14] K. Inoue, Y. Litman, D. Wilkins, Y. Nagata, M. Okuno, J. Phys. Chem. Lett., <strong>14</strong>, 3063-3068 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpclett.8b00133\" target=\"_blank\">[15] T. Morawietz, O. Marsalek, S. Pattenaude, L. Streacker, D. Ben-Amotz, T. Markland, J. Phys. Chem. Lett., <strong>9</strong>, 851-857 (2018)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpclett.3c01989\" target=\"_blank\">[16] Y. Litman, J. Lan, Y. Nagata, D. Wilkins, J. Phys. Chem. Lett., <strong>14</strong>, 8175-8182 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1364/aop.8.000401\" target=\"_blank\">[17] D. Nicoletti, A. Cavalleri, Adv. Opt. Photon., <strong>8</strong>, 401 (2016)</a><br>\r\n<a href=\"https://doi.org/10.1063/1.4931106\" target=\"_blank\">[18] T. Ohto, K. Usui, T. Hasegawa, M. Bonn, Y. Nagata, The Journal of Chemical Physics, <strong>143</strong>, (2015)</a><br>\r\n<a href=\"https://doi.org/10.1021/jz301858g\" target=\"_blank\">[19] M. Sulpizi, M. Salanne, M. Sprik, M. Gaigeot, J. Phys. Chem. Lett., <strong>4</strong>, 83-87 (2012)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpclett.7b00391\" target=\"_blank\">[20] O. Marsalek, T. Markland, J. Phys. Chem. Lett., <strong>8</strong>, 1545-1551 (2017)</a><br>\r\n<a href=\"https://doi.org/10.1021/ct2000952\" target=\"_blank\">[21] C. Zhang, D. Donadio, F. Gygi, G. Galli, J. Chem. Theory Comput., <strong>7</strong>, 1443-1449 (2011)</a><br>\r\n<a href=\"https://doi.org/10.1146/annurev-physchem-062322-051532\" target=\"_blank\">[22] D. Keefer, S. Cavaletto, J. Rouxel, M. Garavelli, H. Yong, S. Mukamel, Annu. Rev. Phys. Chem., <strong>74</strong>, 73-97 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jctc.3c00062\" target=\"_blank\">[23] S. Cavaletto, Y. Nam, J. Rouxel, D. Keefer, H. Yong, S. Mukamel, J. Chem. Theory Comput., <strong>19</strong>, 2327-2339 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1073/pnas.2015988117\" target=\"_blank\">[24] D. Keefer, T. Schnappinger, R. de Vivie-Riedle, S. Mukamel, Proc. Natl. Acad. Sci. U.S.A., <strong>117</strong>, 24069-24075 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.chemrev.7b00081\" target=\"_blank\">[25] M. Kowalewski, B. Fingerhut, K. Dorfman, K. Bennett, S. Mukamel, Chem. Rev., <strong>117</strong>, 12165-12226 (2017)</a><br>\r\n[26] Shaul Mukamel, Principles of nonlinear optical spectroscopy, Oxford University Press, New York 1995<br>\r\n<a href=\"https://doi.org/10.1038/s41586-020-2417-3\" target=\"_blank\">[27] J. Kim, S. Nozawa, H. Kim, E. Choi, T. Sato, T. Kim, K. Kim, H. Ki, J. Kim, M. Choi, Y. Lee, J. Heo, K. Oang, K. Ichiyanagi, R. Fukaya, J. Lee, J. Park, I. Eom, S. Chun, S. Kim, M. Kim, T. Katayama, T. Togashi, S. Owada, M. Yabashi, S. Lee, S. Lee, C. Ahn, D. Ahn, J. Moon, S. Choi, J. Kim, T. Joo, J. Kim, S. Adachi, H. Ihee, Nature, <strong>582</strong>, 520-524 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.114.255501\" target=\"_blank\">[28] M. Minitti, J. Budarz, A. Kirrander, J. Robinson, D. Ratner, T. Lane, D. Zhu, J. Glownia, M. Kozina, H. Lemke, M. Sikorski, Y. Feng, S. Nelson, K. Saita, B. Stankus, T. Northey, J. Hastings, P. Weber, Phys. Rev. Lett., <strong>114</strong>, 255501 (2015)</a><br>\r\n<a href=\"https://doi.org/10.1038/nature09346\" target=\"_blank\">[29] D. Polli, P. Altoè, O. Weingart, K. Spillane, C. Manzoni, D. Brida, G. Tomasello, G. Orlandi, P. Kukura, R. Mathies, M. Garavelli, G. Cerullo, Nature, <strong>467</strong>, 440-443 (2010)</a><br>\r\n<a href=\"https://doi.org/10.1039/d2fd00014h\" target=\"_blank\">[30] D. Brey, R. Binder, R. Martinazzo, I. Burghardt, Faraday Discuss., <strong>237</strong>, 148-167 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.chemrev.9b00813\" target=\"_blank\">[31] C. Baiz, B. Błasiak, J. Bredenbeck, M. Cho, J. Choi, S. Corcelli, A. Dijkstra, C. Feng, S. Garrett-Roe, N. Ge, M. Hanson-Heine, J. Hirst, T. Jansen, K. Kwac, K. Kubarych, C. Londergan, H. Maekawa, M. Reppert, S. Saito, S. Roy, J. Skinner, G. Stock, J. Straub, M. Thielges, K. Tominaga, A. Tokmakoff, H. Torii, L. Wang, L. Webb, M. Zanni, Chem. Rev., <strong>120</strong>, 7152-7218 (2020)</a></p>", "image_description": "", "creation_date": "2026-01-26T15:20:44", "last_modification_date": "2026-01-26T16:44:05", "link_label": "SpectroDynamics 2026: Connecting Computational Spectroscopic Methods Across the Electromagnetic Spec", "link_url": "https://www.cecam.org/workshop-details/spectrodynamics-2026-connecting-computational-spectroscopic-methods-across-the-electromagnetic-spectrum-1489", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Franco Bonafé</strong>, Max Planck Institute for the Structure and Dynamics of Matter ; <strong>Daniel Keefer,</strong> Max Planck Institute for Polymer Research ; <strong>Yair Litman</strong>, Max Planck Institute for Polymer Research", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119447/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70950, "title": "Theoretical Realisation of Quantum Phenomena In Computational Materials Discovery", "slug": "theoretical-realisation-of-quantum-phenomena-in--2", "event_url": "https://memento.epfl.ch/event/theoretical-realisation-of-quantum-phenomena-in--2", "visual_url": "https://memento.epfl.ch/image/32338/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32338/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32338/max-size.jpg", "lang": "en", "start_date": "2026-06-22", "end_date": "2026-06-24", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/theoretical-realisation-of-quantum-phenomena-in-computational-materials-discovery-1485\">https://www.cecam.org/workshop-details/theoretical-realisation-of-quantum-phenomena-in-computational-materials-discovery-1485</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nQuantum phenomena in materials underpin a range of emerging technologies, including spin-based quantum technologies, efficient energy transport materials and ultra-narrow bandwidth lasers.<sup>1,2,3</sup> Emergent behaviour such as quantum magnetism, superconductivity and superradiance<sup>4</sup> arise from the complex interplay between electronic and structural properties; electronic features including strong electron correlation, spin-orbit coupling and reduced dimensionality can lead to phenomena such as unconventional superconductivity and room-temperature spin coherences, whilst structural factors such as crystal symmetry, doping concentrations and Moiré twist patterns are pivotal in shaping these quantum characteristics.<sup>5,6</sup> Computational quantum materials discovery requires both highly advanced theoretical models of the electronic structure and high-throughput approaches for identifying stable crystal structures and predicting their properties.<sup>3,7</sup><br>\r\nStrongly correlated electrons, ubiquitous in quantum materials, challenge conventional density functional theory (DFT). Quantum embedding methods, such as Density Matrix Embedding Theory (DMET) and Quantum Defect Embedding Theory (QDET), are powerful tools for describing strongly correlated electronic states in materials. QDET solves an effective Hamiltonian for a strongly-correlated subset of DFT orbitals using full configuration interaction, parameterized via a Green's function approach.<sup>8</sup> DMET, however, maps the solid-state problem onto a self-consistent quantum impurity coupled to a mean-field bath, with the impurity solved by high-level methods.<sup>9</sup> The application of these advanced techniques is rapidly growing, from analysing superconducting cuprates to describing quantum spin defects in semiconductors.<sup>8,9</sup><br>\r\nModel Hamiltonians, such as the multi-band Hubbard model, are increasingly used to describe the low-energy physics of quantum materials.<sup>10</sup> While the constrained random phase approximation is the traditional choice for parametrising these models,<sup>11</sup> the newly developed moment-conserved RPA may offer superior accuracy by conserving instantaneous two-point correlation functions.<sup>12,13</sup> Powerful numerical techniques like Determinant Quantum Monte Carlo have recently been pioneered for solving the model Hamiltonian and predicting quantum phenomena such as pairing susceptibilities.<sup>14</sup><br>\r\nSuch theoretical methods are also essential for computational discovery of spin defects in semiconductors, a promising platform for room-temperature qubits.<sup>3,15</sup> Advanced theoretical treatments are essential to predict defect electronic, magnetic, and optical properties, incorporating effects like spin-orbit and spin-phonon coupling which determine spin coherence and optical manipulation characteristics. The current state-of-the-art combines DFT studies of semiconductor bulk properties with ab initio treatments of the defect; quantum embedding methods are emerging as a promising alternative.<sup>16,17</sup><br>\r\nGiven the immense diversity of materials, high-throughput screening is a cornerstone of modern materials discovery. DFT, particularly with state-of-the-art approximations like r2SCAN+rVV10, remains the workhorse for reliably determining material structures; such calculations often offer critical insight into both a systems stability and electronic structure.<sup>7,18,19,20</sup> Machine learning (ML) is transforming materials discovery by slashing the computational cost of such calculations, allowing a wider exploration of composition space.<sup>21,22</sup><br>\r\nComputational quantum materials modelling is advancing rapidly, however reconciling methods treating strongly correlated electrons with computational workflows employed in modern materials discovery remains relatively unexploited. The synergy of advanced theory, high-performance computing and ML has the potential to drive breakthroughs in quantum materials discovery and accelerate development of emerging technologies, from novel qubit platforms to room-temperature superconductors.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.132.076401\" target=\"_blank\">[1] C. Scott, G. Booth, Phys. Rev. Lett., <strong>132</strong>, 076401 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41524-025-01554-0\" target=\"_blank\">[2] X. Jiang, W. Wang, S. Tian, H. Wang, T. Lookman, Y. Su, npj. Comput. Mater., <strong>11</strong>, 79 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1016/j.triboint.2024.110438\" target=\"_blank\">[3] S. Giaremis, M. Righi, Tribology International, <strong>204</strong>, 110438 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41524-024-01437-w\" target=\"_blank\">[4] Z. Zhu, J. Park, H. Sahasrabuddhe, A. Ganose, R. Chang, J. Lawson, A. Jain, npj. Comput. Mater., <strong>10</strong>, 258 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1002/jcc.26353\" target=\"_blank\">[5] R. Nelson, C. Ertural, J. George, V. Deringer, G. Hautier, R. Dronskowski, J. Comput. Chem., <strong>41</strong>, 1931-1940 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1021/acsmaterialsau.2c00059\" target=\"_blank\">[6] M. Kothakonda, A. Kaplan, E. Isaacs, C. Bartel, J. Furness, J. Ning, C. Wolverton, J. Perdew, J. Sun, ACS Mater. Au, <strong>3</strong>, 102-111 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41524-025-01547-z\" target=\"_blank\">[7] V. Briganti, A. Lunghi, npj. Comput. Mater., <strong>11</strong>, 62 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpclett.5c00355\" target=\"_blank\">[8] A. Kundu, F. Martinelli, G. Galli, J. Phys. Chem. Lett., <strong>16</strong>, 1973-1979 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1557/s43577-023-00659-5\" target=\"_blank\">[9] A. Gali, A. Schleife, A. Heinrich, A. Laucht, B. Schuler, C. Chakraborty, C. Anderson, C. Déprez, J. McCallum, L. Bassett, M. Friesen, M. Flatté, P. Maurer, S. Coppersmith, T. Zhong, V. Begum-Hudde, Y. Ping, MRS Bulletin, <strong>49</strong>, 256-276 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1073/pnas.2408717121\" target=\"_blank\">[10] P. Mai, B. Cohen-Stead, T. Maier, S. Johnston, Proc. Natl. Acad. Sci. U.S.A., <strong>121</strong>, (2024)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevb.108.064511\" target=\"_blank\">[11] C. Pellegrini, C. Kukkonen, A. Sanna, Phys. Rev. B, <strong>108</strong>, 064511 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1186/s40712-024-00202-7\" target=\"_blank\">[12] R. Goyal, S. Maharaj, P. Kumar, M. Chandrasekhar, J Mater. Sci: Mater Eng., <strong>20</strong>, 4 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41524-024-01314-6\" target=\"_blank\">[13] Y. Chang, E. van Loon, B. Eskridge, B. Busemeyer, M. Morales, C. Dreyer, A. Millis, S. Zhang, T. Wehling, L. Wagner, M. Rösner, npj. Comput. Mater., <strong>10</strong>, 129 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevx.15.021049\" target=\"_blank\">[14] H. Padma, J. Thomas, S. TenHuisen, W. He, Z. Guan, J. Li, B. Lee, Y. Wang, S. Lee, Z. Mao, H. Jang, V. Bisogni, J. Pelliciari, M. Dean, S. Johnston, M. Mitrano, Phys. Rev. X, <strong>15</strong>, 021049 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41467-025-56883-x\" target=\"_blank\">[15] Z. Cui, J. Yang, J. Tölle, H. Ye, S. Yuan, H. Zhai, G. Park, R. Kim, X. Zhang, L. Lin, T. Berkelbach, G. Chan, Nat. Commun., <strong>16</strong>, 1845 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1021/acs.jpclett.5c00287\" target=\"_blank\">[16] L. Otis, Y. Jin, V. Yu, S. Chen, L. Gagliardi, G. Galli, J. Phys. Chem. Lett., <strong>16</strong>, 3092-3099 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1039/d5dd00019j\" target=\"_blank\">[17] A. Ganose, H. Sahasrabuddhe, M. Asta, K. Beck, T. Biswas, A. Bonkowski, J. Bustamante, X. Chen, Y. Chiang, D. Chrzan, J. Clary, O. Cohen, C. Ertural, M. Gallant, J. George, S. Gerits, R. Goodall, R. Guha, G. Hautier, M. Horton, T. Inizan, A. Kaplan, R. Kingsbury, M. Kuner, B. Li, X. Linn, M. McDermott, R. Mohanakrishnan, A. Naik, J. Neaton, S. Parmar, K. Persson, G. Petretto, T. Purcell, F. Ricci, B. Rich, J. Riebesell, G. Rignanese, A. Rosen, M. Scheffler, J. Schmidt, J. Shen, A. Sobolev, R. Sundararaman, C. Tezak, V. Trinquet, J. Varley, D. Vigil-Fowler, D. Wang, D. Waroquiers, M. Wen, H. Yang, H. Zheng, J. Zheng, Z. Zhu, A. Jain, Digital Discovery, (2025)</a><br>\r\n<a href=\"https://doi.org/10.1002/adma.202106909\" target=\"_blank\">[18] W. Ko, Z. Gai, A. Puretzky, L. Liang, T. Berlijn, J. Hachtel, K. Xiao, P. Ganesh, M. Yoon, A. Li, Advanced Materials, <strong>35</strong>, (2022)</a><br>\r\n<a href=\"https://doi.org/10.1126/science.adg0014\" target=\"_blank\">[19] L. Du, M. Molas, Z. Huang, G. Zhang, F. Wang, Z. Sun, Science, <strong>379</strong>, (2023)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41586-023-07001-8\" target=\"_blank\">[20] C. Zhu, S. Boehme, L. Feld, A. Moskalenko, D. Dirin, R. Mahrt, T. Stöferle, M. Bodnarchuk, A. Efros, P. Sercel, M. Kovalenko, G. Rainò, Nature, <strong>626</strong>, 535-541 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1515/nanoph-2022-0723\" target=\"_blank\">[21] Á. Gali, Nanophotonics, <strong>12</strong>, 359-397 (2023)</a><br>\r\n<a href=\"https://doi.org/10.3389/fmats.2024.1343005\" target=\"_blank\">[22] V. Harris, P. Andalib, Front. Mater., <strong>11</strong>, (2024)</a></p>", "image_description": "", "creation_date": "2026-01-26T14:46:04", "last_modification_date": "2026-01-26T16:42:30", "link_label": "Theoretical Realisation of Quantum Phenomena In Computational Materials Discovery", "link_url": "https://www.cecam.org/workshop-details/theoretical-realisation-of-quantum-phenomena-in-computational-materials-discovery-1485", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Petros-Panagis Filippatos, </strong>University of Nottingham ; <strong>Katherine Inzani, </strong>University of Nottingham ; <strong>Tom Irons, </strong>University of Nottingham ; <strong>Connor Williamson, </strong>University of Nottingham", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119440/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 70951, "title": "Toward Intelligent Behavior in Macroscopic Active Matter", "slug": "toward-intelligent-behavior-in-macroscopic-active", "event_url": "https://memento.epfl.ch/event/toward-intelligent-behavior-in-macroscopic-active", "visual_url": "https://memento.epfl.ch/image/32339/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32339/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32339/max-size.jpg", "lang": "en", "start_date": "2026-07-06", "end_date": "2026-07-10", "start_time": null, "end_time": null, "description": "<p>You can apply to participate and find all the relevant information (speakers, abstracts, program,...) on the event website: <a href=\"https://www.cecam.org/workshop-details/toward-intelligent-behavior-in-macroscopic-active-matter-1481\">https://www.cecam.org/workshop-details/toward-intelligent-behavior-in-macroscopic-active-matter-1481</a>.<br>\r\n<br>\r\nRegistration is required to attend the full event, take part in the social activities and present a poster at the poster session (if any). However, the EPFL community is welcome to attend specific lectures without registration if the topic is of interest to their research. Do not hesitate to contact the <a href=\"mailto:[email protected]\">CECAM Event Manager</a> if you have any question.<br>\r\n<br>\r\n<strong>Description</strong><br>\r\n<br>\r\nActive matter has emerged as a central framework for understanding systems composed of self-driven units across scales, ranging from molecular motors and cytoskeletal filaments to animal groups and robotic swarms. Initially, many foundational models focused on macroscopic agents – such as flocks, swarms, and driven granular particles – where simple interaction rules give rise to rich collective phenomena. However, over the past two decades, much of the focus has shifted toward microscopic and mesoscopic active systems, especially in soft and biological matter, supported by the technological development of high-resolution imaging, force measurement, and microfabrication. These advances have driven a more refined theoretical understanding, connecting microscopic dynamics with hydrodynamic and continuum-scale descriptions, and have found applications in biophysics, material science, and cellular biology. <br>\r\nIn parallel, yet often semi-independently, active matter concepts have flourished in ecological and robotic systems. In these domains, the agents – be they insects, birds, autonomous vehicles, or soft robots – not only self-propel and interact, but also sense their environments, make decisions, and adapt their behavior. These systems extend the classical framework of active matter by incorporating elements of intelligence, information processing, and environmental feedback. Notably, such systems can operate far from equilibrium and exhibit coordinated behavior that seems tuned for functional outcomes – navigation, foraging, or collective decision-making.<br>\r\nThese trends point toward a convergence: macroscopic active matter systems capable of intelligent, adaptive, or programmable behavior. This includes both natural systems (e.g., flocking insects, social insects, animal herds) and artificial systems (e.g., modular robots, programmable matter, active granular agents). The interplay of self-propulsion, interaction rules, information exchange, learning or memory, and system-level feedback opens exciting new directions for both fundamental science and applications. Recent efforts in this space combine techniques from statistical physics, nonlinear dynamics, robotics, and machine learning.<br>\r\nHowever, the communities working on these different aspects of active matter – soft matter physicists, ecologists, roboticists, and complexity scientists – remain fragmented, with limited opportunity for sustained dialogue. Bridging these communities is essential to develop a shared language, identify unifying principles, and guide the development of new experimental platforms and theoretical frameworks.<br>\r\n<br>\r\n<strong>References</strong><br>\r\n<br>\r\n<a href=\"https://doi.org/10.1038/s41586-024-08514-6\" target=\"_blank\">[1] F. Gu, B. Guiselin, N. Bain, I. Zuriguel, D. Bartolo, Nature, <strong>638</strong>, 112-119 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1126/scirobotics.aav7874\" target=\"_blank\">[2] A. Rafsanjani, K. Bertoldi, A. Studart, Sci. Robot., <strong>4</strong>, (2019)</a><br>\r\n<a href=\"https://doi.org/10.34133/cbsystems.0301\" target=\"_blank\">[3] J. Tirado, A. Parvaresh, B. Seyidoğlu, D. Bedford, J. Jørgensen, A. Rafsanjani, Cyborg. Bionic. Syst., <strong>6</strong>, (2025)</a><br>\r\n<a href=\"https://doi.org/10.1038/s42254-021-00406-2\" target=\"_blank\">[4] J. O’Byrne, Y. Kafri, J. Tailleur, F. van Wijland, Nat. Rev. Phys., <strong>4</strong>, 167-183 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41567-022-01704-x\" target=\"_blank\">[5] P. Baconnier, D. Shohat, C. López, C. Coulais, V. Démery, G. Düring, O. Dauchot, Nat. Phys., <strong>18</strong>, 1234-1239 (2022)</a><br>\r\n<a href=\"https://doi.org/10.1038/s41567-023-02028-0\" target=\"_blank\">[6] A. Cavagna, L. Di Carlo, I. Giardina, T. Grigera, S. Melillo, L. Parisi, G. Pisegna, M. Scandolo, Nat. Phys., <strong>19</strong>, 1043-1049 (2023)</a><br>\r\n<a href=\"https://doi.org/10.1155/2013/987549\" target=\"_blank\">[7] M. Bischof, E. Del Giudice, Molecular Biology International, <strong>2013</strong>, 1-19 (2013)</a><br>\r\n<a href=\"https://doi.org/10.1098/rstb.2019.0377\" target=\"_blank\">[8] A. Deutsch, P. Friedl, L. Preziosi, G. Theraulaz, Phil. Trans. R. Soc. B, <strong>375</strong>, 20190377 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1038/ncomms5688\" target=\"_blank\">[9] N. Kumar, H. Soni, S. Ramaswamy, A. Sood, Nat. Commun., <strong>5</strong>, 4688 (2014)</a><br>\r\n<a href=\"https://doi.org/10.1111/j.1756-8765.2009.01028.x\" target=\"_blank\">[10] M. Moussaid, S. Garnier, G. Theraulaz, D. Helbing, Topics in Cognitive Science, <strong>1</strong>, 469-497 (2009)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevx.15.021050\" target=\"_blank\">[11] R. Bebon, J. Robinson, T. Speck, Phys. Rev. X, <strong>15</strong>, 021050 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1126/scirobotics.abo6140\" target=\"_blank\">[12] M. Ben Zion, J. Fersula, N. Bredeche, O. Dauchot, Sci. Robot., <strong>8</strong>, (2023)</a><br>\r\n<a href=\"https://doi.org/10.1103/physreve.110.014606\" target=\"_blank\">[13] J. Fersula, N. Bredeche, O. Dauchot, Phys. Rev. E, <strong>110</strong>, 014606 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1038/s42005-024-01540-w\" target=\"_blank\">[14] L. Caprini, A. Ldov, R. Gupta, H. Ellenberg, R. Wittmann, H. Löwen, C. Scholz, Commun. Phys., <strong>7</strong>, 52 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1098/rspb.2021.0275\" target=\"_blank\">[15] T. Lengronne, D. Mlynski, S. Patalano, R. James, L. Keller, S. Sumner, Proc. R. Soc. B., <strong>288</strong>, rspb.2021.0275 (2021)</a><br>\r\n<a href=\"https://doi.org/10.1103/physrevlett.75.1226\" target=\"_blank\">[16] T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, O. Shochet, Phys. Rev. Lett., <strong>75</strong>, 1226-1229 (1995)</a><br>\r\n<a href=\"https://doi.org/10.1360/nso/20240005\" target=\"_blank\">[17] L. Ning, H. Zhu, J. Yang, Q. Zhang, P. Liu, R. Ni, N. Zheng, NSO., <strong>3</strong>, 20240005 (2024)</a><br>\r\n<a href=\"https://doi.org/10.1088/1361-648x/adebd3\" target=\"_blank\">[18] G. Volpe, N. Araújo, M. Guix, M. Miodownik, N. Martin, L. Alvarez, J. Simmchen, R. Leonardo, N. Pellicciotta, Q. Martinet, J. Palacci, W. Ng, D. Saxena, R. Sapienza, S. Nadine, J. Mano, R. Mahdavi, C. Beck Adiels, J. Forth, C. Santangelo, S. Palagi, J. Seok, V. Webster-Wood, S. Wang, L. Yao, A. Aghakhani, T. Barois, H. Kellay, C. Coulais, M. van Hecke, C. Pierce, T. Wang, B. Chong, D. Goldman, A. Reina, V. Trianni, G. Volpe, R. Beckett, S. Nair, R. Armstrong, J. Phys.: Condens. Matter, <strong>37</strong>, 333501 (2025)</a><br>\r\n<a href=\"https://doi.org/10.1088/1361-648x/ab6348\" target=\"_blank\">[19] G. Gompper, R. Winkler, T. Speck, A. Solon, C. Nardini, F. Peruani, H. Löwen, R. Golestanian, U. Kaupp, L. Alvarez, T. Kiørboe, E. Lauga, W. Poon, A. DeSimone, S. Muiños-Landin, A. Fischer, N. Söker, F. Cichos, R. Kapral, P. Gaspard, M. Ripoll, F. Sagues, A. Doostmohammadi, J. Yeomans, I. Aranson, C. Bechinger, H. Stark, C. Hemelrijk, F. Nedelec, T. Sarkar, T. Aryaksama, M. Lacroix, G. Duclos, V. Yashunsky, P. Silberzan, M. Arroyo, S. Kale, J. Phys.: Condens. Matter, <strong>32</strong>, 193001 (2020)</a><br>\r\n<a href=\"https://doi.org/10.1038/529016a\" target=\"_blank\">[20] G. Popkin, Nature, <strong>529</strong>, 16-18 (2016)</a></p>", "image_description": "", "creation_date": "2026-01-26T14:57:51", "last_modification_date": "2026-01-26T16:42:52", "link_label": "Toward Intelligent Behavior in Macroscopic Active Matter", "link_url": "https://www.cecam.org/workshop-details/toward-intelligent-behavior-in-macroscopic-active-matter-1481", "canceled": "False", "cancel_reason": "", "place_and_room": "BCH 2103", "url_place_and_room": "https://plan.epfl.ch/?room==BCH%202103", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "<strong>Wylie Ahmed, </strong>CNRS ; <strong>Laura Alvarez, </strong>University of Bordeaux ; <strong>Lorenzo Caprini, </strong>Heinrich-Heine University of Duesseldorf ; <strong>Matteo Paoluzzi, </strong>Sapienza University of Rome", "contact": "<a href=\"mailto:[email protected]\"><strong>Cornelia Bujenita</strong></a>, CECAM Events and Operations Manager", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 1, "fr_label": "Sur inscription", "en_label": "Registration required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119442/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/1/?format=api", "https://memento.epfl.ch/api/v1/mementos/5/?format=api", "https://memento.epfl.ch/api/v1/mementos/6/?format=api", "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/27/?format=api" ] }, { "id": 71122, "title": "IMX Colloquium - Instructive soft biomaterials for tissue response by design: steering events at the biointerface", "slug": "imx-colloquium-instructive-soft-biomaterials-for-t", "event_url": "https://memento.epfl.ch/event/imx-colloquium-instructive-soft-biomaterials-for-t", "visual_url": "https://memento.epfl.ch/image/32494/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32494/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32494/max-size.jpg", "lang": "en", "start_date": "2026-05-04", "end_date": "2026-05-04", "start_time": "13:15:00", "end_time": "14:15:00", "description": "", "image_description": "", "creation_date": "2026-02-12T08:53:46", "last_modification_date": "2026-02-24T08:31:46", "link_label": "Website", "link_url": "https://www.empa.ch/web/s404/dr.-katharina-maniura", "canceled": "False", "cancel_reason": "", "place_and_room": "MXF 1", "url_place_and_room": "https://plan.epfl.ch/?room==MXF%201", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "Prof. Katharina Maniura, EMPA", "organizer": "Prof. Gregor Jotzu, Prof. Fabien Sorin & Prof. Esther Amstad", "contact": "Prof. Gregor Jotzu, Prof. Fabien Sorin & Prof. Esther Amstad", "is_internal": "False", "theme": "", "vulgarization": { "id": 1, "fr_label": "Tout public", "en_label": "General public" }, "registration": { "id": 3, "fr_label": "Entrée libre", "en_label": "Free" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/119705/", "category": { "id": 1, "code": "CONF", "fr_label": "Conférences - Séminaires", "en_label": "Conferences - Seminars", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/8/?format=api", "https://memento.epfl.ch/api/v1/mementos/314/?format=api" ] } ] }
