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14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

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 hacka...

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Autores principales: 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, K. J., Foster, Ian, White, Andrew D., Blaiszik, Ben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: RSC 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10561547/
https://www.ncbi.nlm.nih.gov/pubmed/38013906
http://dx.doi.org/10.1039/d3dd00113j
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author 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, K. J.
Foster, Ian
White, Andrew D.
Blaiszik, Ben
author_facet 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, K. J.
Foster, Ian
White, Andrew D.
Blaiszik, Ben
author_sort Jablonka, Kevin Maik
collection PubMed
description 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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spelling pubmed-105615472023-10-10 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, K. J. Foster, Ian White, Andrew D. Blaiszik, Ben Digit Discov Chemistry 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. RSC 2023-08-08 /pmc/articles/PMC10561547/ /pubmed/38013906 http://dx.doi.org/10.1039/d3dd00113j Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/
spellingShingle Chemistry
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, K. J.
Foster, Ian
White, Andrew D.
Blaiszik, Ben
14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
title 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
title_full 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
title_fullStr 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
title_full_unstemmed 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
title_short 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon
title_sort 14 examples of how llms can transform materials science and chemistry: a reflection on a large language model hackathon
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10561547/
https://www.ncbi.nlm.nih.gov/pubmed/38013906
http://dx.doi.org/10.1039/d3dd00113j
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