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

TL;DR

This review discusses how integrating machine learning with computational chemistry can significantly enhance predictive capabilities and accelerate discoveries in chemical sciences, targeting researchers across both disciplines.

Contribution

It provides tutorials and a critical review of applications combining machine learning and computational chemistry for improved molecular and materials predictions.

Findings

Machine learning accelerates computational chemistry algorithms.

Combined approaches improve predictions in drug design and catalysis.

Integration enhances insights in molecular and materials modeling.

Abstract

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 then 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.