Google Research

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

  • John A Keith
  • Valentin Vassilev Galindo
  • Bingqing Cheng
  • Stefan Chmiela
  • Michael Gastegger
  • Klaus-Robert Müller
  • Alexandre Tkatchenko
Chemical Reviews, vol. 121 (16) (2021), 9816-9872, https://pubs.acs.org/doi/pdf/10.1021/acs.chemrev.1c00107

Abstract

Machine learning models are poised to make transformative impact in the 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, machine learning methods, and how insights involving both can be achieved. We then follow with a critical review of noteworthy applications that demonstrate how computational quantum chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

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