14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon-Reference-Cited by-同舟云学术

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Published:2023 Issue:5 Volume:2 Page:1233-1250
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Jablonka Kevin Maik¹^ORCID,Ai Qianxiang²^ORCID,Al-Feghali Alexander³^ORCID,Badhwar Shruti⁴^ORCID,Bocarsly Joshua D.⁵^ORCID,Bran Andres M.⁶⁷^ORCID,Bringuier Stefan⁸^ORCID,Brinson L. Catherine⁹^ORCID,Choudhary Kamal¹⁰^ORCID,Circi Defne⁹^ORCID,Cox Sam¹¹^ORCID,de Jong Wibe A.¹²^ORCID,Evans Matthew L.¹³¹⁴^ORCID,Gastellu Nicolas³^ORCID,Genzling Jerome³^ORCID,Gil María Victoria¹⁵^ORCID,Gupta Ankur K.¹²^ORCID,Hong Zhi¹⁶^ORCID,Imran Alishba¹⁷,Kruschwitz Sabine¹⁸^ORCID,Labarre Anne³^ORCID,Lála Jakub¹⁹^ORCID,Liu Tao³^ORCID,Ma Steven³^ORCID,Majumdar Sauradeep¹^ORCID,Merz Garrett W.²⁰^ORCID,Moitessier Nicolas³^ORCID,Moubarak Elias¹^ORCID,Mouriño Beatriz¹^ORCID,Pelkie Brenden²¹^ORCID,Pieler Michael²²²³^ORCID,Ramos Mayk Caldas¹¹^ORCID,Ranković Bojana⁶⁷^ORCID,Rodriques Samuel G.¹⁹^ORCID,Sanders Jacob N.²⁴^ORCID,Schwaller Philippe⁶⁷^ORCID,Schwarting Marcus²⁵,Shi Jiale²^ORCID,Smit Berend¹^ORCID,Smith Ben E.⁵^ORCID,Van Herck Joren¹^ORCID,Völker Christoph¹⁸^ORCID,Ward Logan²⁶^ORCID,Warren Sean³^ORCID,Weiser Benjamin³^ORCID,Zhang Sylvester³^ORCID,Zhang Xiaoqi¹^ORCID,Zia Ghezal Ahmad¹⁸^ORCID,Scourtas Aristana²⁷^ORCID,Schmidt K. J.²⁷^ORCID,Foster Ian²⁸^ORCID,White Andrew D.¹¹^ORCID,Blaiszik Ben²⁷^ORCID

Affiliation:

1. Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland

2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

3. Department of Chemistry, McGill University, Montreal, Quebec, Canada

4. Reincarnate Inc., USA

5. Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK

6. Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

7. National Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

8. Independent Researcher, San Diego, CA, USA

9. Mechanical Engineering and Materials Science, Duke University, USA

10. Material Measurement Laboratory, National Institute of Standards and Technology, Maryland, 20899, USA

11. Department of Chemical Engineering, University of Rochester, USA

12. Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

13. Institut de la Matière Condensée et des Nanosciences (IMCN), UCLouvain, Chemin des Étoiles 8, Louvain-la-Neuve, 1348, Belgium

14. Matgenix SRL, 185 Rue Armand Bury, 6534 Gozée, Belgium

15. Instituto de Ciencia y Tecnología del Carbono (INCAR), CSIC, Francisco Pintado Fe 26, 33011 Oviedo, Spain

16. Department of Computer Science, University of Chicago, Chicago, Illinois 60637, USA

17. Computer Science, University of California, Berkeley, CA 94704, USA

18. Bundesanstalt für Materialforschung und -prüfung, Unter den Eichen 87, 12205 Berlin, Germany

19. Francis Crick Institute, 1 Midland Rd, London NW1 1AT, UK

20. American Family Insurance Data Science Institute, University of Wisconsin–Madison, Madison, WI 53706, USA

21. Department of Chemical Engineering, University of Washington, Seattle, WA 98105, USA

22. OpenBioML.org, UK

23. Stability.AI, UK

24. Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA

25. Department of Computer Science, University of Chicago, Chicago, IL 60490, USA

26. Data Science and Learning Division, Argonne National Lab, USA

27. Globus, University of Chicago, Data Science and Learning Division, Argonne National Lab, USA

28. Department of Computer Science, University of Chicago, Data Science and Learning Division, Argonne National Lab, USA

Abstract

We report the findings of a hackathon focused on exploring the diverse applications of large language models in molecular and materials science.

Funder

National Institute of General Medical Sciences

High Energy Physics

NCCR Catalysis

Agencia Estatal de Investigación

National Institutes of Health

Horizon 2020 Framework Programme

Office of Science

Center for Hierarchical Materials Design

Consejo Superior de Investigaciones Científicas

National Science Foundation

U.S. Department of Commerce

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Grantham Foundation for the Protection of the Environment

Engineering and Physical Sciences Research Council

U.S. Department of Energy

Francis Crick Institute

Cancer Research UK

Medical Research Council

Wellcome Trust

H2020 Marie Skłodowska-Curie Actions

European Commission

Esperantic Studies Foundation

Fédération Wallonie-Bruxelles

National Institute of Standards and Technology

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00113J

Reference108 articles.

1. Machine learning for molecular and materials science

2. The Role of Machine Learning in the Understanding and Design of Materials

3. Opportunities and Challenges for Machine Learning in Materials Science

4. Machine learning in materials informatics: recent applications and prospects

5. Recent advances and applications of machine learning in solid-state materials science

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