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

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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Bibliographic Details
Main Authors: Jablonka, Kevin Maik, Ai, Qianxiang, Al-Feghali, Alexander, Badhwar, Shruti, Bocarsly, Joshua D, Bran, Andres M, Bringuier, Stefan, Brinson, L Catherine, Choudhary, Kamal, Circi, Defne, Cox, Sam, de Jong, Wibe A, Evans, Matthew L, Gastellu, Nicolas, Genzling, Jerome, Gil Matellanes, María Victoria, Gupta, Ankur K, Hong, Zhi, Imran, Alishba, Kruschwitz, Sabine, Labarre, Anne, Lála, Jakub, Liu, Tao, Ma, Steven, Majumdar, Sauradeep, Merz, Garrett W, Moitessier, Nicolas, Moubarak, Elias, Mouriño, Beatriz, Pelkie, Brenden, Pieler, Michael, Ramos, Mayk Caldas, Ranković, Bojana, Rodriques, Samuel G, Sanders, Jacob N, Schwaller, Philippe, Schwarting, Marcus, Shi, Jiale, Smit, Berend, Smith, Ben E, Van Herck, Joren, Völker, Christoph, Ward, Logan, Warren, Sean, Weiser, Benjamin, Zhang, Sylvester, Zhang, Xiaoqi, Zia, Ghezal Ahmad, Scourtas, Aristana, Schmidt, K J, Foster, Ian, White, Andrew D, Blaiszik, Ben
Other Authors: National Science Foundation (US)
Format: artículo biblioteca
Language:English
Published: Royal Society of Chemistry (UK) 2023-10-09
Subjects:Ensure access to affordable, reliable, sustainable and modern energy for all, Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation,
Online Access:http://hdl.handle.net/10261/350193
http://dx.doi.org/10.13039/501100004837
http://dx.doi.org/10.13039/100000001
https://api.elsevier.com/content/abstract/scopus_id/85170516562
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Summary: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.