Machine Learning Approach for Prediction of Reaction Yield with Simulated Catalyst Parameters
Abstract
Prediction of reaction yields by machine learning approach is demonstrated in tungsten-catalyzed epoxidation of alkenes. The various electronic and vibrational parameters of the phosphonic acids are collected by DFT simulation, and chosen by LASSO as the essential parameters for prediction of the reaction yields. With the trained model, we can predict yields of the reaction with unverified phosphonic acids with an error of 26%.
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Alternative first-principles reactivity descriptors, such as those measuring the interaction between catalysts and reactants, have also been fruitfully employed with deep learning models to predict experimental catalytic activities.
“Alternative first-principles reactivity descriptors (87), such as those measuring the interaction between catalysts and reactants (88), have also been fruitfully employed with deep learning models to predict experimental catalytic activities.”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [87]
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References 41 · Cited by 67
- Authors
- Yada, A., Nagata, K., Ando, Y., Matsumura, T., Ichinoseki, S., & Sato, K.
- Affiliations
- Interdisciplinary Research Center for Catalytic Chemistry (JP)
- Artificial Intelligence Research Center (JP)
- National Institute of Advanced Industrial Science and Technology (JP)
- Corresponding author
- Yada, Akira
- Sato, Kazuhiko
- Venue
- Chemistry Letters
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-03-05
- Volume
- 47
- Issue
- 3
- Pages
- 284-287
- ISSN
- 0366-7022 (ISSN-L)
- 1348-0715
- OpenAlex
- W2787252987
- Cited by
- 67
- References
- 41
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 13 · 2023: 8 · 2022: 12 · 2021: 9 · 2020: 7 · 2019: 3 · 2018: 2
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Chemistry Letters (publishedVersion)
- Tokyo Tech Research Repository (Tokyo Institute of Technology) (submittedVersion)
- Funders
- Japan Society for the Promotion of Science (25330283)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- reaction yield prediction, tungsten catalysts, olefin epoxidation, phosphonic acid, electronic parameters, vibration parameters, LASSO feature selection, density functional theory simulation, machine learning
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