Synergies Between Quantum Mechanics and Machine Learning in Reaction Prediction
Abstract
Machine learning (ML) and quantum mechanical (QM) methods can be used in two-way synergy to build chemical reaction expert systems. The proposed ML approach identifies electron sources and sinks among reactants and then ranks all source-sink pairs. This addresses a bottleneck of QM calculations by providing a prioritized list of mechanistic reaction steps. QM modeling can then be used to compute the transition states and activation energies of the top-ranked reactions, providing additional or improved examples of ranked source-sink pairs. Retraining the ML model closes the loop, producing more accurate predictions from a larger training set. The approach is demonstrated in detail using a small set of organic radical reactions.
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Outputs of theory-based models may be used as training samples in data science components, creating a two-way synergy between them.
“Further, the outputs of theory-based models may also be used as training samples in data science components [72], thus creating a two-way synergy between them.”
From Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2017) · cites this paper as [72]
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- Authors
- Sadowski, P., Fooshee, D., Subrahmanya, N., & Baldi, P.
- Affiliations
- University of California, Irvine (US)
- ExxonMobil (United States) (US)
- Corresponding author
- Sadowski, Peter
- Baldi, Pierre
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-10-25
- Volume
- 56
- Issue
- 11
- Pages
- 2125-2128
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2538573120
- Cited by
- 65
- References
- 41
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 4 · 2023: 9 · 2022: 4 · 2021: 8 · 2020: 13 · 2019: 7 · 2018: 7 · 2017: 4 · 2016: 1
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Defense Advanced Research Projects Agency
- 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 mechanism prediction, activation energy prediction
- MeSH terms
- Machine Learning, Models, Molecular, Molecular Conformation, Quantum Theory, Thermodynamics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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