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

Published

Author(s)

Kevin Maik Jablonka, Alexander Al-Feghali, Shruti Badhwar, Joshua Bocarsly, Stefan Bringuier, Kamal Choudhary, Defne Çirci, Samantha Cox, Matthew Evans, Nicolas Gastellu, Jerome Genzling, María Victoria Gil, Ankur Gupta, Wibe de Jong, Tao Liu, Sauradeep Majumdar, Garrett Merz, Nicolas Moitessier, Lynda Brinson, Beatriz Mouriño, Brenden Pelkie, Mayk Caldas Ramos, Bojana Ranković, Jacob Sanders, Ben Blaiszik, Andrew White, Ian Foster, Ghezal Ahmad Jan Zia

Abstract

Chemistry and materials science are complex. Recently, there have been great successes in addressing this complexity using data-driven or computational techniques. Yet, the necessity of input structured in very specific forms and the fact that there is an ever-growing number of tools creates usability and accessibility challenges. Coupled with the reality that much data in these disciplines is unstructured, the effectiveness of these tools is limited. Motivated by recent works that indicated that large language models (LLMs) might help address some of these issues, we organized a hackathon event on the applications of LLMs in chemistry, materials science, and beyond. 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.
Citation
Digital Discovery

Citation

Jablonka, K. , Al-Feghali, A. , Badhwar, S. , Bocarsly, J. , Bringuier, S. , Choudhary, K. , Çirci, D. , Cox, S. , Evans, M. , Gastellu, N. , Genzling, J. , Gil, M. , Gupta, A. , de Jong, W. , Liu, T. , Majumdar, S. , Merz, G. , Moitessier, N. , Brinson, L. , Mouriño, B. , Pelkie, B. , Ramos, M. , Ranković, B. , Sanders, J. , Blaiszik, B. , White, A. , Foster, I. and Zia, G. (2023), 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon, Digital Discovery, [online], https://doi.org/10.1039/d3dd00113j, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956194 (Accessed June 15, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created August 8, 2023, Updated September 26, 2023