Opportunities and Challenges for Machine Learning in Materials Science

TL;DR

This paper reviews how machine learning is transforming materials science by enabling new discoveries and simulations, discusses best practices for model accuracy and applicability, and highlights future opportunities and challenges in the field.

Contribution

It provides a comprehensive overview of recent machine learning impacts in materials science and discusses methods for assessing model accuracy and domain applicability.

Findings

Machine learning has significantly advanced materials discovery and molecular simulations.

Assessing model accuracy and domain applicability is crucial for reliable ML applications.

The review identifies key opportunities and challenges for integrating ML in materials research.

Abstract

Advances in machine learning have impacted myriad areas of materials science, ranging from the discovery of novel materials to the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the breadth of opportunities as well as best practices for their use. In this review, we address aspects of both problems by providing an overview of the areas where machine learning has recently had significant impact in materials science, and then provide a more detailed discussion on determining the accuracy and domain of applicability of some common types of machine learning models. Finally, we discuss some opportunities and challenges for the materials community to fully utilize the capabilities of machine learning.