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

^a Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland
E-mail: mail@kjablonka.com

^b Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

^c Department of Chemistry, McGill University, Montreal, Quebec, Canada

^d Reincarnate Inc., USA

^e Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK

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

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

^h Independent Researcher, San Diego, CA, USA

ⁱ Mechanical Engineering and Materials Science, Duke University, USA

^j Material Measurement Laboratory, National Institute of Standards and Technology, Maryland, USA

^k Department of Chemical Engineering, University of Rochester, USA

^l Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

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

ⁿ Matgenix SRL, 185 Rue Armand Bury, 6534 Gozée, Belgium

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

^p Department of Computer Science, University of Chicago, Chicago, Illinois 60637, USA

^q Computer Science, University of California, Berkeley, CA 94704, USA

^r Bundesanstalt für Materialforschung und -prüfung, Unter den Eichen 87, 12205 Berlin, Germany

^s Francis Crick Institute, 1 Midland Rd, London NW1 1AT, UK

^t American Family Insurance Data Science Institute, University of Wisconsin–Madison, Madison, WI 53706, USA

^u Department of Chemical Engineering, University of Washington, Seattle, WA 98105, USA

^v OpenBioML.org, UK

^w Stability.AI, UK

^x Department of Chemistry and Biochemistry, University of California, Los Angeles, CA 90095, USA

^y Department of Computer Science, University of Chicago, Chicago, IL 60490, USA

^z Data Science and Learning Division, Argonne National Lab, USA

^aa Globus, University of Chicago, Data Science and Learning Division, Argonne National Lab, USA
E-mail: blaiszik@uchicago.edu

^ab Department of Computer Science, University of Chicago, Data Science and Learning Division, Argonne National Lab, USA

Abstract

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.

This article is part of the themed collection: Digital Discovery – Editors Choice Collection 2023

Digital Discovery

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

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

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