Representations of Materials for Machine Learning

Damewood, James; Karaguesian, Jessica; Lunger, Jaclyn R.; Tan, Aik Rui; Xie, Mingrou; Peng, Jiayu; Gómez-Bombarelli, Rafael

doi:10.1146/annurev-matsci-080921-085947

Representations of Materials for Machine Learning

Journal Article · Mon Jul 03 00:00:00 EDT 2023 · Annual Review of Materials Research

DOI:https://doi.org/10.1146/annurev-matsci-080921-085947· OSTI ID:2422344

Damewood, James ^[1]; Karaguesian, Jessica ^[2]; Lunger, Jaclyn R. ^[2]; Tan, Aik Rui ^[2]; Xie, Mingrou ^[2]; Peng, Jiayu ^[2]; Gómez-Bombarelli, Rafael ^[2]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); OSTI
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning the relations between composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by an ML model. Data sets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and properties of interest. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs for ML models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus require further investigation.

View Accepted Manuscript (DOE)

Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Advanced Research Projects Agency - Energy (ARPA-E)

Grant/Contract Number:: AR0001220

OSTI ID:: 2422344

Journal Information:: Annual Review of Materials Research, Journal Name: Annual Review of Materials Research Journal Issue: 1 Vol. 53; ISSN 1531-7331

Publisher:: Annual ReviewsCopyright Statement

Country of Publication:: United States

Language:: English

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Graph neural network modeling of vacancy formation enthalpy for materials discovery and its application in solar thermochemical water splitting Witman, Matthew; Goyal, Anuj; Ogitsu, Tadashi https://doi.org/10.26434/chemrxiv-2022-frcns	posted_content	May 2022
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