Accelerating materials science with high-throughput computations and machine learning

Ong, Shyue Ping

doi:10.1016/j.commatsci.2019.01.013

Title: Accelerating materials science with high-throughput computations and machine learning

Journal Article · 01 February 2019 · Computational Materials Science

DOI: https://doi.org/10.1016/j.commatsci.2019.01.013 · OSTI ID:1528949

^[1]

University of California San Diego, La Jolla, CA (United States). Materials Virtual Laboratory

With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design. In this report we review our efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to (i) design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii) enhance the speed and accuracy in interpreting characterization spectra.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California San Diego, La Jolla, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF); Office of Naval Research (ONR)

Grant/Contract Number:: SC0012583; SC0012118

OSTI ID:: 1528949

Journal Information:: Computational Materials Science, Journal Name: Computational Materials Science Journal Issue: C Vol. 161; ISSN 0927-0256

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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