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9 results on '"Pelkie, Brenden"'

1. 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

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, KJ, Foster, Ian, White, Andrew D., and Blaiszik, Ben

Subjects
Condensed Matter - Materials Science, Computer Science - Machine Learning, Physics - Chemical Physics
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.

Published
2023
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2. Machine learning the quantum flux-flux correlation function for catalytic surface reactions

Author

Pelkie, Brenden G. and Valleau, Stephanie

Subjects
Physics - Chemical Physics, Physics - Computational Physics
Abstract

A dataset of fully quantum flux-flux correlation functions and reaction rate constants was constructed for organic heterogeneous catalytic surface reactions. Gaussian process regressors were successfully fitted to training data to predict previously unseen test set reaction rate constant products and Cauchy fits of the flux-flux correlation function. The optimal regressor prediction mean absolute percent errors were on the order of 0.5% for test set reaction rate constant products and 1.0% for test set flux-flux correlation functions. The Gaussian process regressors were accurate both when looking at kinetics at new temperatures and reactivity of previously unseen reactions and provide a significant speedup respect to the computationally demanding time propagation of the flux-flux correlation function.

Published
2022

3. C-HACK: A Data Science Tutorial and Hackathon for Undergraduate Students in Chemical Engineering

Author

Komp, Evan A., Pelkie, Brenden, Janulaitis, Nida, Abel, Michael, Castillo, Ivan, Chiang, Leo H., Peng, You, Beck, David C., and Valleau, Stéphanie

Abstract

We present a two-week active learning chemical engineering hackathon event specifically designed to teach undergraduate chemical engineering students of any skill level data science through Python and directly apply this knowledge to a real problem provided by industry. The event is free and optional to the students. We use self-evaluation surveys to measure improvements in both data science knowledge and team soft skills, finding that participants report a substantial increase in self-confidence with Python, a popular coding language with support for data science tools. The reported improvement is found to be statistically significant, to a greater extent for women who have traditionally been undersupported in computing.

Published
2023
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4. 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Author

National Science Foundation (US), Swiss National Science Foundation, Ministerio de Ciencia e Innovación (España), Gil Matellanes, María Victoria [0000-0002-2258-3011], 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 Matellanes, 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, National Science Foundation (US), Swiss National Science Foundation, Ministerio de Ciencia e Innovación (España), Gil Matellanes, María Victoria [0000-0002-2258-3011], 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 Matellanes, 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, and Blaiszik, Ben

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.

Published
2023

6. 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Author

Jablonka, Kevin Maik, primary, Ai, Qianxiang, additional, Al-Feghali, Alexander, additional, Badhwar, Shruti, additional, Bocarsly, Joshua D., additional, Bran, Andres M., additional, Bringuier, Stefan, additional, Brinson, L. Catherine, additional, Choudhary, Kamal, additional, Circi, Defne, additional, Cox, Sam, additional, de Jong, Wibe A., additional, Evans, Matthew L., additional, Gastellu, Nicolas, additional, Genzling, Jerome, additional, Gil, María Victoria, additional, Gupta, Ankur K., additional, Hong, Zhi, additional, Imran, Alishba, additional, Kruschwitz, Sabine, additional, Labarre, Anne, additional, Lála, Jakub, additional, Liu, Tao, additional, Ma, Steven, additional, Majumdar, Sauradeep, additional, Merz, Garrett W., additional, Moitessier, Nicolas, additional, Moubarak, Elias, additional, Mouriño, Beatriz, additional, Pelkie, Brenden, additional, Pieler, Michael, additional, Ramos, Mayk Caldas, additional, Ranković, Bojana, additional, Rodriques, Samuel G., additional, Sanders, Jacob N., additional, Schwaller, Philippe, additional, Schwarting, Marcus, additional, Shi, Jiale, additional, Smit, Berend, additional, Smith, Ben E., additional, Van Herck, Joren, additional, Völker, Christoph, additional, Ward, Logan, additional, Warren, Sean, additional, Weiser, Benjamin, additional, Zhang, Sylvester, additional, Zhang, Xiaoqi, additional, Zia, Ghezal Ahmad, additional, Scourtas, Aristana, additional, Schmidt, K. J., additional, Foster, Ian, additional, White, Andrew D., additional, and Blaiszik, Ben, additional

Published
2023
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9. Extracting structured data from organic synthesis procedures using a fine-tuned large language model.

Author

Ai Q, Meng F, Shi J, Pelkie B, and Coley CW

Abstract

The popularity of data-driven approaches and machine learning (ML) techniques in the field of organic chemistry and its various subfields has increased the value of structured reaction data. Most data in chemistry is represented by unstructured text, and despite the vastness of the organic chemistry literature (papers, patents), manual conversion from unstructured text to structured data remains a largely manual endeavor. Software tools for this task would facilitate downstream applications such as reaction prediction and condition recommendation. In this study, we fine-tune a large language model (LLM) to extract reaction information from organic synthesis procedure text into structured data following the Open Reaction Database (ORD) schema, a comprehensive data structure designed for organic reactions. The fine-tuned model produces syntactically correct ORD records with an average accuracy of 91.25% for ORD "messages" ( e.g. , full compound, workups, or condition definitions) and 92.25% for individual data fields ( e.g. , compound identifiers, mass quantities), with the ability to recognize compound-referencing tokens and to infer reaction roles. We investigate its failure modes and evaluate performance on specific subtasks such as reaction role classification., Competing Interests: There are no conflicts of interest to declare., (This journal is © The Royal Society of Chemistry.)

Published
2024
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