Back to Search Start Over

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

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, 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
Publication Year :
2023

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2306.06283
Document Type :
Working Paper
Full Text :
https://doi.org/10.1039/D3DD00113J