retrieve:
Return the details about the given Event id.

list:
List all Event objects.

GET /api/v1/events/?format=api&offset=150&ordering=event__spoken_languages
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

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            "title": "lunch&LEARN:  Learning with AI: Designing AI Tutors that foster learning in robotics and CS courses",
            "slug": "lunchlearn-learning-with-ai-designing-ai-tutors-th",
            "event_url": "https://memento.epfl.ch/event/lunchlearn-learning-with-ai-designing-ai-tutors-th",
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            "start_date": "2026-06-09",
            "end_date": "2026-06-09",
            "start_time": "12:15:00",
            "end_time": "13:00:00",
            "description": "<strong>// NEW DATE // This event has been rescheduled from 17 March to 9 June 2026!</strong><br>\r\n<br>\r\nHow can AI tutors be designed to support learning rather than shortcutting it?<br>\r\n<br>\r\nIn this presentation, Jérôme Brender (EPFL) will examine how undergraduate students learn with AI tutors in robotics and computer science courses.<br>\r\n<br>\r\nAcross multiple design iterations, he investigated how features such as course-grounded retrieval (RAG), Socratic questioning, and real-time prompt feedback, and debate chatbot, shape students’ engagement, prompting behavior, and learning outcomes.<br>\r\n<br>\r\nHis work focuses on how AI tutors can help students better understand course concepts and become more reflective and effective users of AI tools. The findings provide insights into designing AI tutors that foster critical thinking and support sustainable learning practices.",
            "image_description": "",
            "creation_date": "2026-01-21T13:49:42",
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            "speaker": "<a href=\"https://people.epfl.ch/jerome.brender?lang=en\">Jérôme Brender</a>",
            "organizer": "Center LEARN",
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        {
            "id": 70922,
            "title": "Une éducation au réel. L'Atelier Cantàfora à l'EPFL / ARCHIZOOM",
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            "start_date": "2026-03-17",
            "end_date": "2026-06-05",
            "start_time": null,
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            "description": "<strong>UNE ÉDUCATION AU RÉEL <br>\r\nL’ATELIER CANTÀFORA<br>\r\n18.03-05.06.2026<br>\r\n<br>\r\nOpening! Tuesday 17 March 6.30 pm</strong><br>\r\n<br>\r\nThis exhibition explores the vast field of graphic representation in architecture through fifteen years of teaching architectural representation at EPFL at the turn of the 2000s. It presents around a hundred paintings on wood, didactic works produced between 1997 and 2007 in the teaching units of the painter Arduino Cantàfora. They suggest a possible way of making, between thought and <em>actio</em>, where drawing and painting structure a concept and become an essential language for expressing the founding idea of a project. Despite the transition to digital technology, the exhibition conveys the conviction that the artisanal culture of drawing and painting continues to play a fundamental and indispensable role in training architects.<br>\r\n<br>\r\n<em>An exhibition produced in collaboration with the LAPIS laboratory at the EPFL’s Institute of Architecture and Urban Planning.</em>",
            "image_description": "",
            "creation_date": "2026-01-20T19:05:53",
            "last_modification_date": "2026-01-26T08:24:30",
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            "organizer": "Archizoom",
            "contact": "Solène Hoffmann",
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            "keywords": "Architecture, dessin, figuration graphique, peinture",
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            "id": 70928,
            "title": "BMI Distinguished Seminar // Yvette Fisher: Flexibility of visual input to the Drosophila head direction network",
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            "event_url": "https://memento.epfl.ch/event/bmi-distinguished-seminar-yvette-fisher-flexibilit",
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            "start_date": "2026-06-24",
            "end_date": "2026-06-24",
            "start_time": "12:15:00",
            "end_time": "13:15:00",
            "description": "<p>Many plasticity rules rely on adjusting the strength of synapses between pairs of cells based on their coincident activity. We uncovered a new mechanism for coincidence detection in the Drosophila head direction network. To maintain an accurate sense of direction, head direction neurons that signal orientation during navigation must learn to anchor to relevant external sensory cues in novel environments. Yet the synaptic mechanism for this form of unsupervised learning is unknown in any organism. In Drosophila, GABAergic visual inputs converge onto head direction neurons, and these inhibitory synapses change strength with experience to learn the relationship between visual landmarks and head direction. However, how coincident pre- and postsynaptic activity is detected across this inhibitory synapse is not understood. We discovered that neurons which release the monoamine octopamine close a feedback loop that conveys postsynaptic head direction activity onto presynaptic terminals of visual inputs. This octopamine pathway is required for anchoring the head direction network to visual cues. Furthermore, pairing structured activation of octopamine neurons with a visual cue is sufficient to drive rapid plasticity, even without postsynaptic head direction cell activity. Previous work has extensively characterized coincidence detection mechanisms at excitatory synapses; our work defines a novel mechanism for coincidence detection at an inhibitory synapse, in which postsynaptic activity is relayed via a neuromodulatory neuron onto presynaptic terminals.<br>\r\n </p>",
            "image_description": "",
            "creation_date": "2026-01-21T16:26:07",
            "last_modification_date": "2026-02-16T13:34:30",
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            "link_url": "https://vcresearch.berkeley.edu/faculty/yvette-fisher",
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            "speaker": "Yvette Fisher, UC Berkeley",
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            "contact": "[email protected]",
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            "id": 70929,
            "title": "BMI Distinguished Seminar // Manish Saggar",
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            "lang": "en",
            "start_date": "2026-09-09",
            "end_date": "2026-09-09",
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            "end_time": "13:15:00",
            "description": "",
            "image_description": "",
            "creation_date": "2026-01-21T16:42:23",
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            "link_url": "https://profiles.stanford.edu/manish-saggar",
            "canceled": "False",
            "cancel_reason": "",
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            "speaker": "Manish Saggar, Stanford University",
            "organizer": "SV BMI Host: K. Hess Bellwald",
            "contact": "[email protected]",
            "is_internal": "False",
            "theme": "",
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            "icalendar_url": "https://memento.epfl.ch/event/export/119405/",
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        {
            "id": 70935,
            "title": "Genome-wide in vitro reconstitution to study nucleosome positioning and chromatin architecture",
            "slug": "genome-wide-in-vitro-reconstitution-to-study-nucle",
            "event_url": "https://memento.epfl.ch/event/genome-wide-in-vitro-reconstitution-to-study-nucle",
            "visual_url": "https://memento.epfl.ch/image/32323/200x112.jpg",
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            "lang": "en",
            "start_date": "2026-05-20",
            "end_date": "2026-05-20",
            "start_time": "17:00:00",
            "end_time": "18:30:00",
            "description": "<p>Access to genetic information within the cell nucleus is regulated by the distribution of nucleosomes, which are the basic unit of chromatin. Local access to specific genomic regions is facilitated by repositioning nucleosomes to enable transcription and other nuclear processes. Nucleosome positioning is primarily regulated by ATP-dependent chromatin remodeling enzymes (CRs) that belong to the Snf2-type helicase family. These enzymes disrupt histone-DNA contacts by consuming ATP. The functions of CRs can be redundant or essential, complicating their study in vivo. To address this, we employ a unique bottom-up approach, in which we reconstitute chromatin in vitro using purified proteins and a yeast genomic plasmid library. To elucidate the diverse remodeling functions of CRs, we add purified CRs in combination with various transcription factors to the in vitro reconstituted chromatin. The resulting changes in nucleosome positioning are monitored using MNase-seq. Depending on the type of CR used, we observe distinct nucleosome positioning patterns. Furthermore, we have expanded our in vitro reconstitution approach to explore the 3D genome organization of reconstituted chromatin, discovering a role for CRs in the 3D genome organization of <em>S. cerevisiae</em>.</p>",
            "image_description": "",
            "creation_date": "2026-01-23T09:41:37",
            "last_modification_date": "2026-03-10T12:18:20",
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            "canceled": "False",
            "cancel_reason": "",
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            "speaker": "<a href=\"https://www.uni-goettingen.de/en/689675.html\">Elisa Oberbeckmann</a> (University of Goettingen)",
            "organizer": "Professeur Beat Fierz",
            "contact": "Marie Munoz",
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        {
            "id": 70936,
            "title": "Probing and Modulating Transcription Factor–DNA Interactions via Chemically Modified Proteins",
            "slug": "probing-and-modulating-transcription-factordna-i-2",
            "event_url": "https://memento.epfl.ch/event/probing-and-modulating-transcription-factordna-i-2",
            "visual_url": "https://memento.epfl.ch/image/32324/200x112.jpg",
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            "lang": "en",
            "start_date": "2026-05-19",
            "end_date": "2026-05-19",
            "start_time": "16:15:00",
            "end_time": "17:45:00",
            "description": "<p>Chemical protein synthesis provides a powerful means to prepare novel, modified proteins with atomic-level precision, offering unprecedented opportunities to understand fundamental biological processes.<sup>1</sup> Of particular interest is gene expression, which is controlled through interactions between transcription factors (TFs) and DNA. This presentation will highlight the power of combining total synthesis and late-stage transformations to generate complex, modified proteins for deciphering the molecular roles of post-translational modifications (PTMs) in TFs regulation. Specific examples will focus on the synthesis of site-specifically phosphorylated and acetylated TFs, such as the Myc/Max system.<sup>2, 3</sup> Remarkably, these studies revealed that phosphorylation and acetylation patterns modulate Max–DNA interactions by altering DNA binding affinity and sequence specificity. Importantly, such mechanistic insights led to the development of novel bioactive miniproteins derived from Max (μMax), capable of inhibiting oncogene expression and cancer cell proliferation through antagonistic binding to target genes in cancer cells, paving the way for the development of new therapeutic proteins targeting oncogene expression.<sup>4-6</sup><br>\r\n<br>\r\n<br>\r\n1. O. Harel, M. Jbara, <em>Angew. Chem. Int. Ed., </em><strong>2023</strong>, <em>62</em>, e202217716<br>\r\n2. R. Nithun, Y. Yao, X. Lin, S. Habiballah, A. Afek, M. Jbara, <em>Angew. Chem. In. Ed., </em><strong>2023</strong>,<em> 62,</em> e202310913<br>\r\n3. R. Nithun, Y. Yao, O. Harel, S. Habiballah, A. Afek, M. Jbara, <em>ACS Central Science, </em><strong>2024</strong>, <em>10</em>, 1295–1303<br>\r\n4. X. Lin, S. Mandal, R. Nithun, R. Kolla, B. Bouri, H. Lashuel, M. Jbara, <em>JACS</em>, <strong>2024</strong>, 146, 25788<br>\r\n5. X. Lin, O. Harel, M. Jbara, <em>Angew. Chem. In. Ed.,</em> <strong>2024</strong>, <em>63,</em> e202317511<br>\r\n6. O. Harel, F. Nadal-Bufi, R. Nithun, Y. Yao, A. Afek, M. Vendrell, M. Jbara, <em>JACS</em>, <strong>2025</strong>, 147, 46, 42647</p>",
            "image_description": "",
            "creation_date": "2026-01-23T10:10:50",
            "last_modification_date": "2026-03-10T12:19:13",
            "link_label": "",
            "link_url": "",
            "canceled": "False",
            "cancel_reason": "",
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            "speaker": "<a href=\"https://www.jbaralab.sites.tau.ac.il/mj\">Dr Muhammad Jbara</a> (Tel Aviv University)",
            "organizer": "Professor Fierz Beat",
            "contact": "Marie Munoz",
            "is_internal": "False",
            "theme": "",
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        {
            "id": 70947,
            "title": "Des Cèdres à Dorigny, bâtir l'école d'architecture / ACM ARCHIZOOM",
            "slug": "des-cedres-a-dorigny-batir-l-ecole-d-architectur-2",
            "event_url": "https://memento.epfl.ch/event/des-cedres-a-dorigny-batir-l-ecole-d-architectur-2",
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            "lang": "en",
            "start_date": "2026-03-04",
            "end_date": "2026-09-29",
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            "description": "<strong>DES CÈDRES À DORIGNY, <br>\r\nBÂTIR L’ÉCOLE D’ARCHITECTURE<br>\r\n04.03-29.09.2026<br>\r\n<br>\r\nOpening! Tuesday 3 March 6.30pm</strong><br>\r\n<br>\r\nFocusing on architectural projects, both built and unrealised, kept in the archives of modern construction, the exhibition <em>Des Cèdres à Dorigny</em> tells the story of the birth and evolution of the Lausanne School of Architecture. From its beginnings within the EPUL to its integration into the EPFL campus, this historical journey puts into perspective the conditions of architectural education, its relationship with engineering, and the role of archives in building an institutional memory that sheds light on both the discipline of architecture and its teaching.<br>\r\n<br>\r\n<em>An exhibition produced in collaboration with the <a href=\"https://www.epfl.ch/schools/enac/acm/en/acm-en/\">Archives de la construction moderne</a> at EPFL</em>",
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            "title": "Complex Fluids at Interfaces: Structure, Stability, and Molecular Effects",
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            "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 ",
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        {
            "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",
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            "start_date": "2026-06-22",
            "end_date": "2026-06-24",
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            "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>",
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            "title": "Toward Intelligent Behavior in Macroscopic Active Matter",
            "slug": "toward-intelligent-behavior-in-macroscopic-active",
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            "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>",
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