The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Machine Learning for Chemical Reactions
Abstract
Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule reaction dynamics to computational platforms for reaction planning. ML-based techniques can be particularly relevant for problems involving both computation and experiments. For one, Bayesian inference is a powerful approach to develop models consistent with knowledge from experiments. Second, ML-based methods can also be used to handle problems that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation of reactive networks as they occur in combustion has become possible using machine-learned neural network potentials. This review provides an overview of the questions that can and have been addressed using machine learning techniques, and an outlook discusses challenges in this diverse and stimulating field. It is concluded that ML applied to chemistry problems as practiced and conceived today has the potential to transform the way with which the field approaches problems involving chemical reactions, in both research and academic teaching.
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Cites
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Metadata
- Authors
- Meuwly, M.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-06-07
- Volume
- 121
- Issue
- 16
- Pages
- 10218-10239
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W3173107846
- Cited by
- 437
- References
- 16
- FWCI
- 3.29
- Type
- journal article
- Language
- en
- Open access
- closed
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