14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
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.
Keywords
Cite
@article{arxiv.2306.06283,
title = {14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon},
author = {Kevin Maik Jablonka and Qianxiang Ai and Alexander Al-Feghali and Shruti Badhwar and Joshua D. Bocarsly and Andres M Bran and Stefan Bringuier and L. Catherine Brinson and Kamal Choudhary and Defne Circi and Sam Cox and Wibe A. de Jong and Matthew L. Evans and Nicolas Gastellu and Jerome Genzling and María Victoria Gil and Ankur K. Gupta and Zhi Hong and Alishba Imran and Sabine Kruschwitz and Anne Labarre and Jakub Lála and Tao Liu and Steven Ma and Sauradeep Majumdar and Garrett W. Merz and Nicolas Moitessier and Elias Moubarak and Beatriz Mouriño and Brenden Pelkie and Michael Pieler and Mayk Caldas Ramos and Bojana Ranković and Samuel G. Rodriques and Jacob N. Sanders and Philippe Schwaller and Marcus Schwarting and Jiale Shi and Berend Smit and Ben E. Smith and Joren Van Herck and Christoph Völker and Logan Ward and Sean Warren and Benjamin Weiser and Sylvester Zhang and Xiaoqi Zhang and Ghezal Ahmad Zia and Aristana Scourtas and KJ Schmidt and Ian Foster and Andrew D. White and Ben Blaiszik},
journal= {arXiv preprint arXiv:2306.06283},
year = {2023}
}