Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery (Journal Article) | DOE PAGES

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Citation Details

This content will become publicly available on May 20, 2022

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

Authors:: Duan, Chenru; Liu, Fang; Nandy, Aditya; Kulik, Heather J.

Award ID(s):: 1846426 1704266

Publication Date:: 2021-05-20

NSF-PAR ID:: 10231107

Journal Name:: The Journal of Physical Chemistry Letters

Volume:: 12

Issue:: 19

Page Range or eLocation-ID:: 4628 to 4637

ISSN:: 1948-7185

Sponsoring Org:: National Science Foundation

Free Publicly Accessible Full Text
This content will become publicly available on May 20, 2022
Publisher's Version of Record
https://doi.org/10.1021/acs.jpclett.1c00631