retrieve:
Return the details about the given Memento id.

list:
List all Memento objects.

GET /api/v1/mementos/27/events/?format=api&offset=40&ordering=-en_label
HTTP 200 OK
Allow: GET, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

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            "id": 71877,
            "title": "Aligning Models Throughout Training",
            "slug": "aligning-models-throughout-training",
            "event_url": "https://memento.epfl.ch/event/aligning-models-throughout-training",
            "visual_url": "https://memento.epfl.ch/image/33199/200x112.jpg",
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            "start_date": "2026-06-18",
            "end_date": "2026-06-18",
            "start_time": "13:00:00",
            "end_time": "15:00:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Martin Jaggi<br>\r\nThesis advisor: Prof. Robert West<br>\r\nCo-examiner: Prof. Nicolas Flammarion<br>\r\n<br>\r\n<u>Abstract</u><br>\r\ncoming soon<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\ncoming soon",
            "image_description": "",
            "creation_date": "2026-05-13T12:37:39",
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            "place_and_room": "BC 333",
            "url_place_and_room": "https://plan.epfl.ch/?room==BC%20333",
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            "speaker": "Raghav Singhal",
            "organizer": "",
            "contact": "[email protected]",
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        {
            "id": 71834,
            "title": "Language-Based Safety and Isolation Across System Composition Boundaries",
            "slug": "language-based-safety-and-isolation-across-system",
            "event_url": "https://memento.epfl.ch/event/language-based-safety-and-isolation-across-system",
            "visual_url": "https://memento.epfl.ch/image/33154/200x112.jpg",
            "visual_large_url": "https://memento.epfl.ch/image/33154/720x405.jpg",
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            "lang": "en",
            "start_date": "2026-06-19",
            "end_date": "2026-06-19",
            "start_time": "09:00:00",
            "end_time": "11:00:00",
            "description": "<u>DIC candidacy exam</u><br>\r\nExam president: Prof. Anne-Marie Kermarrec<br>\r\nThesis advisor: Prof. Mathias Payer<br>\r\nCo-examiner: Prof. Thomas Bourgeat<br>\r\n<br>\r\n<u>Abstract</u><br>\r\nThe OS kernel was exposed due to vulnerable drivers as one vulnerability could bring down the whole system. Since fixing all driver bugs is nearly impossible, isolation of drivers is necessary to be a fundamental mechanism for reducing the attack surface between system composition boundaries. Compared with hardware-based isolation(Xen, Nooks, etc) and software-based isolation (e.g. SFI), language-based isolation often gains fine-grained safety, such as type constraints and thread-safety properties. <br>\r\nSafeDrive was an early effort to protect Linux C drivers using type checks, memory-safety invariants, and recovery support, but it still had overhead and incomplete safety. RedLeaf, built in Rust completely, reduces runtime checks by relying on Rust's ownership system while enabling efficient communication through validated interfaces. As Rust enters mainline Linux, a new challenge appears: Rust components need to interact with legacy foreign-language code, where safety guarantees can break. Omniglot addresses this by enabling safe, zero-copy interaction between Rust and unmodified untrusted code using carefully designed memory isolation primitives.<br>\r\nGoing forward, my research will continue to focus on the intersection of operating systems and programming languages.<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\n<strong>Paper 1: </strong>SafeDrive: Safe and Recoverable Extensions Using Language-Based Techniques [OSDI'06]\r\n<ul>\r\n\t<li><a href=\"https://dl.acm.org/doi/10.5555/1298455.1298461\">https://dl.acm.org/doi/10.5555/1298455.1298461</a></li>\r\n</ul>\r\n<strong>Paper 2: </strong>RedLeaf: Isolation and Communication in a Safe Operating System [OSDI'20]\r\n\r\n<ul>\r\n\t<li><a href=\"https://www.usenix.org/conference/osdi20/presentation/narayanan-vikram\">https://www.usenix.org/conference/osdi20/presentation/narayanan-vikram</a></li>\r\n</ul>\r\n<strong>Paper 3: </strong>Building Bridges: Safe Interactions with Foreign Languages through Omniglot [OSDI'25]\r\n\r\n<ul>\r\n\t<li><a href=\"https://www.usenix.org/conference/osdi25/presentation/schuermann\">https://www.usenix.org/conference/osdi25/presentation/schuermann</a></li>\r\n</ul>",
            "image_description": "",
            "creation_date": "2026-05-08T11:09:55",
            "last_modification_date": "2026-05-08T11:10:13",
            "link_label": "",
            "link_url": "",
            "canceled": "False",
            "cancel_reason": "",
            "place_and_room": "BC 133",
            "url_place_and_room": "https://plan.epfl.ch/?room==BC%20133",
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            "spoken_languages": [],
            "speaker": "Yiwen Xu",
            "organizer": "",
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        {
            "id": 71931,
            "title": "Access Bit Scanning for Page Replacement Policies",
            "slug": "access-bit-scanning-for-page-replacement-policies",
            "event_url": "https://memento.epfl.ch/event/access-bit-scanning-for-page-replacement-policies",
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            "lang": "en",
            "start_date": "2026-06-22",
            "end_date": "2026-06-22",
            "start_time": "10:00:00",
            "end_time": "12:00:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Thomas Bourgeat<br>\r\nThesis advisor: Prof. Babak Falsafi<br>\r\nCo-examiner: Prof. Sanidhya Kashyap<br>\r\n<br>\r\n<u>Abstract</u><br>\r\nModern workloads with memory footprints ranging from hundreds of gigabytes to several terabytes have become widespread over the last decade. With memory accounting for nearly 50% of server costs in modern data centres, provisioning large DRAM capacities significantly increases total cost of ownership (TCO). Tiered memory architectures combine memories with different capacities, latencies, and cost characteristics to improve cost efficiency while satisfying application performance requirements. Application performance in these architectures is highly sensitive to page placement decisions. Consequently, making informed placement decisions requires precise tracking of data access patterns and accurate identification of hot pages.<br>\r\n<br>\r\nExisting operating systems commonly rely on hardware-maintained access bits in page table entries to estimate memory access behaviour and identify hot data regions. They periodically scan the application's entire virtual address space to construct workload access profiles. However, linear scanning of page table access bits does not scale for modern applications with terabyte-scale memory footprints. Since scanning overhead scales with the memory footprint, a single full scan can take several seconds for workloads with large memory footprints. Reducing scanning frequency lowers monitoring overhead but also decreases profiling accuracy and responsiveness to changing access patterns, resulting in a fundamental tradeoff between profiling accuracy and monitoring overhead. Therefore, we need scalable, low-overhead mechanisms for access monitoring in next-generation memory management systems.<br>\r\n<br>\r\nIn this talk, I will discuss recent approaches to scalable memory access profiling in large-memory systems. Specifically, I will present works that use techniques such as adaptive sampling, region-based monitoring, and hierarchical profiling of page table trees to reduce and bound scanning overhead. <br>\r\n<br>\r\n<u>Selected papers</u><br>\r\ncoming soon",
            "image_description": "",
            "creation_date": "2026-05-22T13:08:29",
            "last_modification_date": "2026-05-22T13:18:11",
            "link_label": "",
            "link_url": "",
            "canceled": "False",
            "cancel_reason": "",
            "place_and_room": "BC 010",
            "url_place_and_room": "https://plan.epfl.ch/?room==BC%20010",
            "url_online_room": "",
            "spoken_languages": [],
            "speaker": "<br>\r\nArunkrishna Annai Madalam Sivasubramanian  ",
            "organizer": "",
            "contact": "[email protected]",
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        {
            "id": 69172,
            "title": "Dynamic Decisions and Temporal Frictions in On-Demand Mobility",
            "slug": "dynamic-decisions-and-temporal-frictions-in-on-d-2",
            "event_url": "https://memento.epfl.ch/event/dynamic-decisions-and-temporal-frictions-in-on-d-2",
            "visual_url": "https://memento.epfl.ch/image/33200/200x112.jpg",
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            "lang": "en",
            "start_date": "2026-06-22",
            "end_date": "2026-06-22",
            "start_time": "10:30:00",
            "end_time": "11:30:00",
            "description": "<strong>ABSTRACT</strong><br>\r\nApp-based, on-demand transport systems have transformed urban mobility by enabling real-time coordination between vehicles and users. Yet behind this flexibility lies a fundamental tension: operational decisions that appear beneficial in the present can deteriorate the future state of the system. This talk explores these temporal frictions in assigned on-demand systems—where passengers and vehicles are matched before meeting—and how they shape the performance and reliability of shared mobility services.<br>\r\nFirst, we show how assignment decisions evolve through what we term the Increasing Gap Dynamics, a negative feedback cycle through which vehicles progressively concentrate in space, reducing system efficiency over time. We then examine how a tension between efficiency and reliability emerges: while highly dynamic operations can improve short-term efficiency and flexibility, they may also increase unreliability by exposing passengers to future, uncertain requests. We discuss how this tension manifests both in routing decisions and in the selection of pickup and dropoff locations, and show how carefully designed anticipatory techniques can help reconcile these competing objectives.<br>\r\n<br>\r\n <br>\r\n<strong>SHORT BIO</strong><br>\r\nDr Andrés Fielbaum is a Senior Lecturer in Transport Engineering at the University of Sydney and a DECRA Fellow of the Australian Research Council. His work lies at the intersection of transport systems and applied mathematics, with a focus on public transport, on-demand mobility, and developing analytical models to inform system design and operations. He completed his PhD in Systems Engineering at Universidad de Chile in 2019 and held a postdoctoral appointment at TU Delft between 2019-2023.<br>\r\n<br>\r\nDr Fielbaum has authored over 35 peer-reviewed journal articles in leading outlets and serves as an Associate Editor for Transportation Research Part B: Methodological,  and the Journal of Intelligent Transportation Systems.<br>\r\n<br>\r\n ",
            "image_description": "",
            "creation_date": "2025-05-15T16:13:27",
            "last_modification_date": "2026-05-13T08:28:12",
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            "place_and_room": "GC B1 10",
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            "speaker": "Dr Andrés Fielbaum",
            "organizer": "HOMES - Prof. Kenan Zhang",
            "contact": "Prof. Kenan Zhang<br>\r\nAdministrative: Mélanie Thuillard",
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            "theme": "",
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        {
            "id": 71835,
            "title": "Testing the Hypervisor Boundary: Semantic Oracles and Adversarial Input Generation for pKVM",
            "slug": "testing-the-hypervisor-boundary-semantic-oracles-a",
            "event_url": "https://memento.epfl.ch/event/testing-the-hypervisor-boundary-semantic-oracles-a",
            "visual_url": "https://memento.epfl.ch/image/33155/200x112.jpg",
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            "lang": "en",
            "start_date": "2026-06-22",
            "end_date": "2026-06-22",
            "start_time": "13:00:00",
            "end_time": "15:00:00",
            "description": "<u>EDIC candidacy exam</u><br>\r\nExam president: Prof. Katerina Argyraki<br>\r\nThesis advisor: Prof. Mathias Payer<br>\r\nCo-examiner: Prof. Thomas Bourgeat<br>\r\n<br>\r\n<u>Abstract</u><br>\r\nHypervisors underpin modern confidential computing stacks, yet their exposed interfaces - hypercalls, fault handlers, and device interactions - remain a large and under-tested attack surface. Existing approaches fall short: coverage-driven fuzzers (e.g., HYPERPILL) explore broadly but rely on crash oracles, missing non-crashing vulnerabilities, while specification-based systems (e.g., Ghost) detect semantic violations but constrain input generation and under explore adversarial cases.<br>\r\n<br>\r\nThis thesis focuses on systematic bug finding in pKVM by combining semantic oracles with coverage-guided, adversarial fuzzing. Executable interface specifications are used to flag behavioral inconsistencies, while mutation-based input generation deliberately produces both valid and model-violating inputs to reach boundary conditions and deep execution paths. The approach further incorporates EL2 coverage feedback and concurrent vCPU sequences to expose bugs such as silent state corruption, ownership violations, and TOCTOU races.<br>\r\n<br>\r\nThe goal is to uncover security-critical vulnerabilities that evade both crash-based fuzzing and model-constrained testing, particularly in unspecified or adversarial input classes. More broadly, the work aims to establish a practical methodology for bug-driven testing of production hypervisors.<br>\r\n<br>\r\n<u>Selected papers</u><br>\r\ncoming soon",
            "image_description": "",
            "creation_date": "2026-05-08T11:14:42",
            "last_modification_date": "2026-05-08T11:14:42",
            "link_label": "",
            "link_url": "",
            "canceled": "False",
            "cancel_reason": "",
            "place_and_room": "BC 133",
            "url_place_and_room": "https://plan.epfl.ch/?room==BC%20133",
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            "spoken_languages": [],
            "speaker": "Sofiia Saltovskaia",
            "organizer": "",
            "contact": "[email protected]",
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            "file": null,
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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",
            "visual_url": "https://memento.epfl.ch/image/32338/200x112.jpg",
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            "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>",
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