Accelerating the Prediction of Stable Materials with Machine Learning
Published In
Nature Computational Science
Document Type
Citation
Publication Date
11-9-2023
Abstract
Despite the rise in computing power, the large space of possible combinations of elements and crystal structure types makes large-scale high-throughput surveys of stable materials prohibitively expensive, especially for complex materials and materials subject to environmental conditions such as finite temperature. When physics-based computational methods and labor-intensive experiments are not feasible, machine learning (ML) methods can be a rapid and powerful alternative. Owing to a wealth of experimental and first-principles data as well as improved ML frameworks designed for materials modeling, ML is shown to be effective in predicting stability parameters and accelerating the discovery of new stable materials. In this Review, we summarize the most recent advancements in applying ML methodologies in predicting materials stability, focusing particularly on predictions of zero- and finite-temperature stability. We also highlight the need for more ML development in predictions of other thermodynamic knobs, such as pressure and surface/interfacial energy, which practically impact materials stability.
Rights
Copyright © 2023, Springer Nature America, Inc.
Locate the Document
DOI
10.1038/s43588-023-00536-w
Persistent Identifier
https://archives.pdx.edu/ds/psu/41332
Citation Details
Griesemer, S. D., Xia, Y., & Wolverton, C. (2023). Accelerating the prediction of stable materials with machine learning. Nature Computational Science, 3(11), 934–945.