Opportunities and Challenges for Machine Learning in Materials Science

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.

References in corpus (11)

• A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
• DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
• Machine Learning Unifies the Modelling of Materials and Molecules
• Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
• Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
• Understanding molecular representations in machine learning: The role of uniqueness and target similarity
• Design and Analysis of Machine Learning Exchange-Correlation Functionals via Rotationally Invariant Convolutional Descriptors
• Robust FCC solute diffusion predictions from ab-initio machine learning methods
• IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
• Exploring effective charge in electromigration using machine learning
• Assessing Graph-based Deep Learning Models for Predicting Flash Point