Machine Learning for Chemical Reactions.
By Markus Meuwly. Chemical Reviews, 2021
350 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/2a41589895b84f6225bba43d928355eb2fd52c1d
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In Computational Theory and Mathematics
In Materials Chemistry
In Molecular Biology
Citations in the corpus
- Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning (2023, background): “[38] Importantly, the influence of steric and electronic effects on the model performance for C-H activation reactions and their application to regioselectivity for molecules with multiple aromatic ring systems remains unexplored.”