Machine Learning for Chemical Reactions

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...

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Bibliographic Details
Published inChemical reviews Vol. 121; no. 16; pp. 10218 - 10239
Main Author Meuwly, Markus
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 25.08.2021
Subjects
artificial intelligence
Bayesian analysis
Bayesian theory
Chemical reactions
combustion
fields
Machine learning
Neural networks
planning
Statistical inference
Online AccessGet full text
ISSN0009-2665
1520-6890
1520-6890
DOI10.1021/acs.chemrev.1c00033

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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.
AbstractList 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.
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.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.
Author Meuwly, Markus
AuthorAffiliation Department of Chemistry
Brown University
AuthorAffiliation_xml – name: Brown University
– name: Department of Chemistry
Author_xml – sequence: 1
  givenname: Markus
  orcidid: 0000-0001-7930-8806
  surname: Meuwly
  fullname: Meuwly, Markus
  email: m.meuwly@unibas.ch
  organization: Brown University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/34097378$$D View this record in MEDLINE/PubMed
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Snippet Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule...
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SubjectTerms artificial intelligence
Bayesian analysis
Bayesian theory
Chemical reactions
combustion
fields
Machine learning
Neural networks
planning
Statistical inference
Title Machine Learning for Chemical Reactions
URI http://dx.doi.org/10.1021/acs.chemrev.1c00033
https://www.ncbi.nlm.nih.gov/pubmed/34097378
https://www.proquest.com/docview/2568712489
https://www.proquest.com/docview/2539210113
https://www.proquest.com/docview/2636792118
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