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

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon. Jablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bran, J. D. B. A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D., Cox, S., de Jong, W. A., Evans, M. L., Gastellu, N., Genzling, J., Gil, M. V., Gupta, A. K., Hong, Z., Imran, A., Kruschwitz, S., Labarre, A., Lála, J., Liu, T., Ma, S., Majumdar, S., Merz, G. W., Moitessier, N., Moubarak, E., Mouriño, B., Pelkie, B., Pieler, M., Ramos, M. C., Ranković, B., Rodriques, S. G., Sanders, J. N., Schwaller, P., Schwarting, M., Shi, J., Smit, B., Smith, B. E., Van Heck, J., Völker, C., Ward, L., Warren, S., Weiser, B., Zhang, S., Zhang, X., Zia, G. A., Scourtas, A., Schmidt, K. J., Foster, I., White, A. D., & Blaiszik, B. June, 2023. arXiv:2306.06283 [cond-mat, physics:physics]

Paper doi abstract bibtex

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.

@misc{jablonka_14_2023,
	title = {14 {Examples} of {How} {LLMs} {Can} {Transform} {Materials} {Science} and {Chemistry}: {A} {Reflection} on a {Large} {Language} {Model} {Hackathon}},
	shorttitle = {14 {Examples} of {How} {LLMs} {Can} {Transform} {Materials} {Science} and {Chemistry}},
	url = {http://arxiv.org/abs/2306.06283},
	doi = {10.48550/arXiv.2306.06283},
	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.},
	urldate = {2023-06-13},
	publisher = {arXiv},
	author = {Jablonka, Kevin Maik and Ai, Qianxiang and Al-Feghali, Alexander and Badhwar, Shruti and Bran, Joshua D. Bocarsly Andres M. and Bringuier, Stefan and Brinson, L. Catherine and Choudhary, Kamal and Circi, Defne and Cox, Sam and de Jong, Wibe A. and Evans, Matthew L. and Gastellu, Nicolas and Genzling, Jerome and Gil, María Victoria and Gupta, Ankur K. and Hong, Zhi and Imran, Alishba and Kruschwitz, Sabine and Labarre, Anne and Lála, Jakub and Liu, Tao and Ma, Steven and Majumdar, Sauradeep and Merz, Garrett W. and Moitessier, Nicolas and Moubarak, Elias and Mouriño, Beatriz and Pelkie, Brenden and Pieler, Michael and Ramos, Mayk Caldas and Ranković, Bojana and Rodriques, Samuel G. and Sanders, Jacob N. and Schwaller, Philippe and Schwarting, Marcus and Shi, Jiale and Smit, Berend and Smith, Ben E. and Van Heck, Joren and Völker, Christoph and Ward, Logan and Warren, Sean and Weiser, Benjamin and Zhang, Sylvester and Zhang, Xiaoqi and Zia, Ghezal Ahmad and Scourtas, Aristana and Schmidt, K. J. and Foster, Ian and White, Andrew D. and Blaiszik, Ben},
	month = jun,
	year = {2023},
	note = {arXiv:2306.06283 [cond-mat, physics:physics]},
	keywords = {Computer Science - Machine Learning, Condensed Matter - Materials Science, Physics - Chemical Physics},
}

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