Event List
retrieve:
Return the details about the given Event id.
list:
List all Event objects.
GET /api/v1/events/?format=api&offset=70&ordering=slug
{ "count": 238, "next": "https://memento.epfl.ch/api/v1/events/?format=api&limit=10&offset=80&ordering=slug", "previous": "https://memento.epfl.ch/api/v1/events/?format=api&limit=10&offset=60&ordering=slug", "results": [ { "id": 71324, "title": "EPFL Xplore - unveiling 2026", "slug": "epfl-xplore-unveiling-2026", "event_url": "https://memento.epfl.ch/event/epfl-xplore-unveiling-2026", "visual_url": "https://memento.epfl.ch/image/32679/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32679/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32679/max-size.jpg", "lang": "en", "start_date": "2026-05-06", "end_date": "2026-05-06", "start_time": "18:00:00", "end_time": "23:00:00", "description": "<p>Come discover the new rover prototype of the EPFL Xplore team at SPOT </p>", "image_description": "", "creation_date": "2026-03-09T10:01:50", "last_modification_date": "2026-03-09T10:51:28", "link_label": "Project page", "link_url": "https://make.epfl.ch/projects/2/make-epfl-xplore-2", "canceled": "False", "cancel_reason": "", "place_and_room": "DLLEL 0 28", "url_place_and_room": "https://plan.epfl.ch/?room==DLLEL%200%2028", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "Management of EPFL Xplore association, EPFL Space Center", "organizer": "EPFL Xplore association", "contact": "[email protected]", "is_internal": "False", "theme": "", "vulgarization": { "id": 2, "fr_label": "Public averti", "en_label": "Informed public" }, "registration": { "id": 2, "fr_label": "Sur invitation", "en_label": "Invitation required" }, "keywords": "", "file": null, "icalendar_url": "https://memento.epfl.ch/event/export/120008/", "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/10/?format=api", "https://memento.epfl.ch/api/v1/mementos/398/?format=api" ] }, { "id": 69714, "title": "ERC Advanced Grants 2026", "slug": "erc-advanced-grants-2026", "event_url": "https://memento.epfl.ch/event/erc-advanced-grants-2026", "visual_url": "https://memento.epfl.ch/image/31203/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/31203/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/31203/max-size.jpg", "lang": "en", "start_date": "2026-08-27", "end_date": "2026-08-27", "start_time": null, "end_time": null, "description": "<strong>Call currently closed</strong><br>\r\n<br>\r\n<strong>Aim</strong><br>\r\nSupport for excellent Principal Investigators at the career stage at which they are already established research leaders with a recognised track record of research achievements. Principal Investigators must demonstrate the ground-breaking nature, ambition, and feasibility of their research proposal.<br>\r\n An ERC Advanced Grant Principal Investigator is expected to be an active researcher and to have a track record of significant research achievements. The scientific excellence of the research project and the applicant’s track record are the only evaluation criteria.<br>\r\n<br>\r\n<strong>Lump-sum Funding & duration</strong><br>\r\nUp to EUR 2.5 mio. for a period of 5 years, including 25% overhead. For shorter project periods, the budget will be applied pro rata temporis. Additional funding up to EUR 1 mio., including 25% overhead, may be requested for well justified reasons; additional funding covers for example start-up costs for PIs moving to Switzerland as a consequence of receiving the ERC grant, purchase of major equipment, access to large facilities, other major experimental and field work costs including personnel costs. For applicants relocating to the EU or a Horizon Europe Associated country, additional funding for up to EUR 2 million can be requested.<br>\r\nThe budget submitted for evaluation must provide the details which allow the sound assessment of the requested resources. Payment of the lump sum contribution will be based on the work carried out and reported, irrespective of the actual costs incurred.<br>\r\n<br>\r\n<strong>New proposal format</strong><br>\r\nThe Extended<strong> </strong>Synopsis and the Scientific Proposal have been rethought in terms of focus and applicants are now requested to submit Part I of the Scientific Proposal (setting out the background, the objectives, and the overall research strategy) and Part II of the Scientific Proposal focusing primarily on the implementation). The evaluation elements have also been adapted to reflect the abovementioned changes. <a href=\"https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/wp-call/2026/wp_horizon-erc-2026_en.pdf\">See ERC work programme 2026</a><br>\r\n<br>\r\n<strong>Eligibility</strong><br>\r\nSupport for excellent Principal Investigators at the career stage at which they are already established research leaders with a recognized track record of research achievements. Principal Investigators must demonstrate the ground-breaking nature, ambition, and feasibility of their research proposal.<br>\r\n<br>\r\n<strong>Call opening</strong><br>\r\n28 May 2026<br>\r\n<br>\r\n<strong>Deadline for proposal submissions</strong><br>\r\n27 August 2026, 17:00 CET<br>\r\nSubmission via the <a href=\"https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/home\">European Funding & Tenders portal</a><br>\r\n<br>\r\n<strong>Support and information</strong>\r\n<ul>\r\n\t<li>The EPFL toolkit for the ERC AdG 2026 will be updated upon call opening.</li>\r\n\t<li>For proposal writing services and administrative assistance with the proposal preparation please contact <a href=\"mailto:[email protected]\">[email protected]</a>.</li>\r\n</ul>", "image_description": "", "creation_date": "2025-09-02T11:48:25", "last_modification_date": "2025-11-17T14:31:59", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [], "speaker": "", "organizer": "", "contact": "[email protected]", "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/117502/", "category": { "id": 16, "code": "PROP", "fr_label": "Appel à proposition", "en_label": "Call for proposal", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/140/?format=api" ] }, { "id": 70883, "title": "ERC Consolidator Grants", "slug": "erc-consolidator-grants", "event_url": "https://memento.epfl.ch/event/erc-consolidator-grants", "visual_url": "https://memento.epfl.ch/image/32273/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/32273/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/32273/max-size.jpg", "lang": "en", "start_date": "2027-01-30", "end_date": "2027-01-30", "start_time": null, "end_time": null, "description": "<strong>Call currently closed, next call opening foreseen for autumn 2026</strong><br>\r\n<br>\r\n<strong>Aim</strong><br>\r\nFor excellent scientists to consolidate their research career in the framework of an ambitious research project, allowing for major scientific advancements.<br>\r\n<br>\r\n<strong>Funding & duration</strong><br>\r\nMax. EUR 2 Mio for a period of max. 5 years (pro rata temporis for shorter project duration). However, an additional EUR 1 million can be requested if well justified and necessary for the project. For applicants relocating to the EU or a Horizon Europe Associated country, additional funding for up to EUR 2 million can be requested.<br>\r\n<br>\r\n<strong>Eligibility</strong>\r\n<ul>\r\n\t<li>ERC plans to extend the eligibility window to 5 – 15 years after the PhD defense date; final confirmation will be available upon publishing of the ERC workprogramme 2027 (latest in June 2026).</li>\r\n\t<li>Principal Investigators must commit a minimum of 40% of their working time to the project and a minimum of 50% of their working time in a European Member State or Associated Country.</li>\r\n\t<li>For applicants without professorship restrictions apply (information provided upon call opening).</li>\r\n\t<li>Applicants must submit an EPFL commitment letter together with their project-proposal.</li>\r\n\t<li>Proposals are submitted via the <a href=\"https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/home\">EU Funding & Tenders portal</a>.</li>\r\n</ul>\r\n<strong>Deadline for proposal submissions</strong><br>\r\nTBC<br>\r\n<br>\r\n<strong>Support and information</strong>\r\n\r\n<ul>\r\n\t<li>EPFL toolkit for the proposal preparation will be provided upon call opening.</li>\r\n\t<li>The Research Office will organize an information event.</li>\r\n\t<li>For proposal writing services and administrative assistance with the proposal preparation please contact <a href=\"mailto:[email protected]\">[email protected]</a>.</li>\r\n</ul>", "image_description": "", "creation_date": "2026-01-15T14:45:18", "last_modification_date": "2026-01-15T14:45:18", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [], "speaker": "", "organizer": "", "contact": "[email protected]", "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/119338/", "category": { "id": 16, "code": "PROP", "fr_label": "Appel à proposition", "en_label": "Call for proposal", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/140/?format=api" ] }, { "id": 70196, "title": "ERC Proof of Concept 2026", "slug": "erc-proof-of-concept-2026", "event_url": "https://memento.epfl.ch/event/erc-proof-of-concept-2026", "visual_url": "https://memento.epfl.ch/image/31651/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/31651/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/31651/max-size.jpg", "lang": "en", "start_date": "2026-09-17", "end_date": "2026-09-17", "start_time": null, "end_time": null, "description": "<strong>Call currently closed<br>\r\nAim</strong><br>\r\nFor Principal Investigators who have already received an ERC grant and want to explore the commercial and/or social potential of the results obtained within the original ERC grant.<br>\r\n<strong>Lump-sum Funding & duration</strong><br>\r\nEUR 150’000 for an overall project duration of 18 months.<br>\r\nERC expects projects to be completed in 12 months (this should be reflected in the gantt chart of the proposal) but signs contracts for 18 months (allowing for sufficient preparation time).<br>\r\n<strong>Eligibility</strong><br>\r\nPrincipal Investigators of an ongoing ERC grant can apply or Principal Investigators in a main grant that has ended after 1 January 2024 (for the call 2025), are eligible to apply.<br>\r\n<strong>Deadline for proposal submissions</strong><br>\r\nCall opening: delayed (was foreseen for Nov 2025)<br>\r\n1st cut-off: tbc<br>\r\n2nd cut-off: tbc<br>\r\nOnly one proposal can be submitted per call year.<br>\r\n<br>\r\n<strong>Support and information</strong>\r\n<ul>\r\n\t<li>The <a href=\"https://drive.google.com/drive/folders/1jRHoFVGKxOE-VWhpVwA-8Xh6o1TKakUw\">EPFL toolkit</a> provides useful information for the proposal preparation.</li>\r\n\t<li>For proposal writing services and administrative assistance with the proposal preparation please contact <a href=\"mailto:[email protected]\">[email protected]</a>.</li>\r\n</ul>\r\n<br>\r\n<br>\r\n ", "image_description": "", "creation_date": "2025-10-23T15:13:09", "last_modification_date": "2026-01-15T15:02:12", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [], "speaker": "", "organizer": "", "contact": "[email protected]", "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/118259/", "category": { "id": 16, "code": "PROP", "fr_label": "Appel à proposition", "en_label": "Call for proposal", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/140/?format=api" ] }, { "id": 68772, "title": "ERC Starting Grants", "slug": "erc-starting-grants", "event_url": "https://memento.epfl.ch/event/erc-starting-grants", "visual_url": "https://memento.epfl.ch/image/30325/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/30325/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/30325/max-size.jpg", "lang": "en", "start_date": "2027-10-14", "end_date": "2027-10-14", "start_time": null, "end_time": null, "description": "<strong>Call currently closed<br>\r\nAim</strong><br>\r\nFor talented early-career scientists who have already produced excellent supervised work and have already shown evidence for research independence, for example by having produced at least one important publication as main author or without the participation of their PhD supervisor.<br>\r\n<br>\r\n<strong>Funding & duration</strong><br>\r\nMax. EUR 1.5 Mio for a period of max. 5 years (including 25% overhead; pro rata temporis for shorter project duration). Additional funding up to EUR 1 mio. (including 25% overhead), may be requested for well justified reasons; additional funding covers for example start-up costs for PIs moving to Switzerland as a consequence of receiving the ERC grant, purchase of major equipment, access to large facilities, other major experimental and field work costs including personnel costs. For applicants relocating to the EU or a Horizon Europe Associated country, additional funding for up to EUR 2 million can be requested.<br>\r\n<br>\r\n<strong>Eligibility</strong>\r\n<ul>\r\n\t<li>ERC plans to extend the eligibility window to 0 - 10 years after the PhD defense date; final confirmation available upon publishing of the ERC workprogramme 2027 (latest in June 2026).</li>\r\n\t<li>Principal Investigators must commit a minimum of 50% of their working time to the project and a minimum of 50% of their working time in a European Member State or Associated Country.</li>\r\n\t<li>For applicants without professorship an internal preview by EPFL faculties might be required (information provided upon call opening).</li>\r\n\t<li>Applicants must submit an EPFL commitment letter together with their project-proposal.</li>\r\n\t<li>Proposals are submitted via the <a href=\"https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/home\">EU Funding & Tenders portal</a><a href=\"https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/ERC-2026-STG?order=DESC&pageNumber=1&pageSize=50&sortBy=relevance&keywords=ERC&isExactMatch=true&status=31094502&programmePart=43108406,43108473,43108514,43108394&programmePeriod=2021%20-%202027&frameworkProgramme=43108390\">.</a></li>\r\n</ul>\r\n<br>\r\n<strong>Deadline for proposal submissions</strong><br>\r\nTBC (around mid-September 2026)<br>\r\n<br>\r\n<strong>Support and information</strong>\r\n\r\n<ul>\r\n\t<li>The EPFL toolkit, including budget calculator and useful information for the proposal preparation will be provided upon call opening.</li>\r\n\t<li>An ERC StG info event will be organised.</li>\r\n\t<li>For proposal writing services and administrative assistance with the proposal preparation please contact <a href=\"mailto:[email protected]\">[email protected]</a>.</li>\r\n\t<li><a href=\"https://erc.europa.eu/apply-grant/starting-grant\">ERC StG webpage</a></li>\r\n</ul>", "image_description": "", "creation_date": "2025-03-26T14:02:46", "last_modification_date": "2026-01-15T15:09:02", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [], "speaker": "", "organizer": "", "contact": "[email protected]", "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/116131/", "category": { "id": 16, "code": "PROP", "fr_label": "Appel à proposition", "en_label": "Call for proposal", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/140/?format=api" ] }, { "id": 68778, "title": "ERC Synergy Grants", "slug": "erc-synergy-grants", "event_url": "https://memento.epfl.ch/event/erc-synergy-grants", "visual_url": "https://memento.epfl.ch/image/30331/200x112.jpg", "visual_large_url": "https://memento.epfl.ch/image/30331/720x405.jpg", "visual_maxsize_url": "https://memento.epfl.ch/image/30331/max-size.jpg", "lang": "en", "start_date": "2026-11-05", "end_date": "2026-11-05", "start_time": null, "end_time": null, "description": "<strong>Call closed<br>\r\n<br>\r\nAim</strong><br>\r\nFor a group of <strong>two to maximum four Principal Investigators (PIs) </strong>working together and bringing different skills and resources to tackle ambitious research problems. One will be designated as the corresponding PI (cPI). The synergistic element to solve important research challenges is the key element for successful project proposals.<br>\r\n<br>\r\n<strong>Funding</strong><br>\r\nMax. EUR 10 Mio for a period of max. 6 years (including 25% overhead; pro rata temporis for shorter project duration). However, an additional EUR 4 million can be made available to cover eligible “start-up” costs for researchers moving from a third country to the EU or an associated country and/or the purchase of major equipment and/or access to large facilities and/or other major experimental and field work costs, including personnel costs.<br>\r\n<br>\r\n<strong>Eligibility</strong><br>\r\nNo specific eligibility criteria regarding the academic training are foreseen for ERC Synergy Grants. PIs must present a competitive<strong> </strong>track record as appropriate to their career stage.<br>\r\n<br>\r\n<strong>Deadline for proposal submissions</strong><br>\r\nTBC<br>\r\nProposals are submitted via the <a href=\"https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/calls-for-proposals?isExactMatch=true&status=31094501,31094502,31094503&order=DESC&pageNumber=1&pageSize=50&sortBy=startDate\">EU Funding & Tenders portal</a><br>\r\n<br>\r\n<strong>Support and information</strong>\r\n<ul>\r\n\t<li>EPFL toolkit , with budget calculator and useful information for the proposal preparation will be provided upon call opening.</li>\r\n\t<li>For proposal writing services and administrative assistance with the proposal preparation please contact <a href=\"mailto:[email protected]\">[email protected]</a>.</li>\r\n\t<li><a href=\"https://erc.europa.eu/apply-grant/synergy-grant\">ERC SyG webpage</a></li>\r\n</ul>", "image_description": "", "creation_date": "2025-03-26T17:32:18", "last_modification_date": "2025-11-17T14:28:11", "link_label": "", "link_url": "", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [], "speaker": "", "organizer": "", "contact": "[email protected]", "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/116139/", "category": { "id": 16, "code": "PROP", "fr_label": "Appel à proposition", "en_label": "Call for proposal", "activated": true }, "academic_calendar_category": null, "domains": [], "mementos": [ "https://memento.epfl.ch/api/v1/mementos/140/?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": 71140, "title": "FameLab Switzerland 2026 competition", "slug": "famelab-switzerland-2026-competition", "event_url": "https://memento.epfl.ch/event/famelab-switzerland-2026-competition", "visual_url": null, "visual_large_url": null, "visual_maxsize_url": null, "lang": "en", "start_date": "2026-02-16", "end_date": "2026-04-30", "start_time": null, "end_time": null, "description": "<figure class=\"image\"><img alt=\"\" height=\"337\" src=\"//memento.epfl.ch/public/upload/fckeditorimage/c2/f6/c5e48d79.jpg\" width=\"600\">\r\n<figcaption>© 2026 EPFL</figcaption>\r\n</figure>\r\n<br>\r\n<strong>Calling all passionate science storytellers!</strong><br>\r\n<br>\r\nThe internationally acclaimed science communication competition <a href=\"chatgpt://generic-entity?number=0\"><strong>FameLab</strong></a> is coming to <a href=\"chatgpt://generic-entity?number=1\"><strong>Switzerland</strong></a> — and this is your opportunity to shine.<br>\r\n<br>\r\nFameLab invites scientists, researchers, and innovators to step into the spotlight and present a scientific concept of their choice in just <strong>three minutes</strong>. The challenge? Make it clear, captivating, and unforgettable for a general audience. No slides. No props. Just your voice, your passion, and your story.<br>\r\n<br>\r\n<strong>Why Participate?</strong>\r\n\r\n<ul>\r\n\t<li>Compete in the Swiss heats and connect with a dynamic community of science communicators</li>\r\n\t<li>Gain visibility and sharpen your public speaking skills</li>\r\n\t<li>Advance to the national final for a chance to become Switzerland’s FameLab champion</li>\r\n</ul>\r\n<br>\r\n<strong> The Grand Prize</strong><br>\r\n<br>\r\nThe Swiss winner will receive:\r\n<ul>\r\n\t<li>An exclusive ticket to the renowned <a href=\"chatgpt://generic-entity?number=2\"><strong>Cheltenham Science Festival</strong></a> in the <a href=\"chatgpt://generic-entity?number=3\"><strong>United Kingdom</strong></a></li>\r\n\t<li>Flights and accommodation fully covered</li>\r\n\t<li>Access to a high-level Masterclass in Science Communication</li>\r\n</ul>\r\n<br>\r\n<br>\r\nWhether you’re working in physics, biology, engineering, social sciences, or any other field — if you love science and want to share it with the world, this is your stage.<br>\r\n<br>\r\n<strong>Apply now:</strong> https://www.famelab.ch/apply<br>\r\nStep forward. Speak up. Spark curiosity.<br>\r\n<br>\r\nWe look forward to welcoming you on stage!<br>\r\n<br>\r\n<strong>The FameLab BSNL Team</strong>", "image_description": "Famelab Registration Flyer", "creation_date": "2026-02-16T14:47:41", "last_modification_date": "2026-02-16T14:53:32", "link_label": "Register Now", "link_url": "https://www.famelab.ch", "canceled": "False", "cancel_reason": "", "place_and_room": "", "url_place_and_room": "", "url_online_room": "", "spoken_languages": [ "https://memento.epfl.ch/api/v1/spoken_languages/2/?format=api" ], "speaker": "", "organizer": "BSNL Famelab Team", "contact": "[email protected] or [email protected]", "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": "Famelab, Science, communication, competition", "file": "https://memento.epfl.ch/public/upload/files/2026posterfinal.png", "icalendar_url": "https://memento.epfl.ch/event/export/119735/", "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/3/?format=api", "https://memento.epfl.ch/api/v1/mementos/4/?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/9/?format=api", "https://memento.epfl.ch/api/v1/mementos/10/?format=api", "https://memento.epfl.ch/api/v1/mementos/145/?format=api", "https://memento.epfl.ch/api/v1/mementos/21/?format=api", "https://memento.epfl.ch/api/v1/mementos/318/?format=api", "https://memento.epfl.ch/api/v1/mementos/111/?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. 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