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