Annual Review of Materials Research Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery Changwon Suh, 1 Clyde Fare, 2 James A. Warren, 3 and Edward O. Pyzer-Knapp 2 1 Nexight Group, Silver Spring, Maryland 20910, USA 2 IBM Research, Daresbury WA4 4AD, United Kingdom; email: epyzerk3@uk.ibm.com 3 Material Measurement Laboratory,National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA Annu. Rev. Mater. Res. 2020. 50:1–25 First published as a Review in Advance on April 21, 2020 The Annual Review of Materials Research is online at matsci.annualreviews.org https://doi.org/10.1146/annurev-matsci-082019- 105100 Copyright © 2020 by Annual Reviews. All rights reserved Keywords materials genome, materials discovery, artifcial intelligence, machine learning, data Abstract Machine learning, applied to chemical and materials data, is transforming the feld of materials discovery and design, yet signifcant work is still re- quired to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning tech- nologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data- driven approaches to materials discovery and design are standard practice. 1 Annu. Rev. Mater. Res. 2020.50:1-25. Downloaded from www.annualreviews.org Access provided by 34.228.24.229 on 07/22/20. For personal use only.