Perspective on integrating machine learning into computational chemistry and materials science

TL;DR

This paper reviews how machine learning is transforming computational chemistry and materials science, especially in constructing interatomic potentials and predicting quantum properties, impacting research workflows and education.

Contribution

It provides a comprehensive overview of ML integration in electronic structure theory and molecular simulation, highlighting current practices and future prospects.

Findings

ML is now central in high-dimensional interatomic potentials.

ML methods can accurately predict quantum mechanical properties.

The integration of ML influences research workflows and training in the field.

Abstract

Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties - be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a tighter and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.