14 Examples Of How Llms Can Transform Materials Science And Chemistry: A Reflection On A Large Language Model Hackathon
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 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 Kj, Foster Ian, White Andrew D., Blaiszik Ben. Arxiv 2023
[Paper]
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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