Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

By John A. Keith, V. Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus‐Robert Müller , Alexandre Tkatchenko. Chemical Reviews, 2021

675 citations recorded by the source corpus.

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

Subfields: Catalysis, Computational Theory and Mathematics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/a453ce8a3de86a170c79a1082ef358c3adf4e612

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In Computational Theory and Mathematics

In Materials Chemistry

Citations in the corpus

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects (2021, background): “16 They allow to automatically learn the relation between chemical structure and forces from ab initio reference data.”
Higher-Order Explanations of Graph Neural Networks via Relevant Walks (2020, background): “In the field of machine learning for quantum chemistry [68], [69], [70], [71], [72], GNNs have been exhibiting state of the art performance for predicting molecular properties [3], [37],”
The design space of E(3)-equivariant atom-centred interatomic potentials (2022, neutral): “We denote the set of neighbors of an atom i , i.e., atoms with the cutoﬀ distance, by N ( i ).”