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14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

9 June 2023
K. Jablonka
Qianxiang Ai
Alexander H Al-Feghali
S. Badhwar
Joshua D. Bocarsly Andres M Bran
Andres M Bran
S. Bringuier
L. C. Brinson
K. Choudhary
Yuan Chiang
Sam Cox
W. D. Jong
Matthew L. Evans
Nicolas Gastellu
Jérôme Genzling
María Victoria Gil
Ankur Gupta
Zhi Hong
A. Imran
S. Kruschwitz
A. Labarre
Jakub Lála
Tao Liu
Steven Ma
Sauradeep Majumdar
G. Merz
N. Moitessier
E. Moubarak
B. Mouriño
B. Pelkie
Michael Pieler
Mayk Caldas Ramos
Bojana Ranković
Samuel G. Rodriques
J. N. Sanders
P. Schwaller
Marcus Schwarting
Jia-Xin Shi
B. Smit
Benn Smith
J. V. Heck
C. Volker
Logan T. Ward
S. Warren
B. Weiser
Sylvester Zhang
Xiaoqi Zhang
Ghezal Ahmad Jan Zia
Aristana Scourtas
K. J. Schmidt
Ian Foster
Andrew D. White
Ben Blaiszik
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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.

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