Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Author:
Affiliation:
1. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
2. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
Funder
Defense Advanced Research Projects Agency
American Association for the Advancement of Science
Basic Energy Sciences
Office of Naval Research
Division of Chemical, Bioengineering, Environmental, and Transport Systems
Burroughs Wellcome Fund
MIT Energy Initiative
Publisher
American Chemical Society (ACS)
Subject
General Materials Science,Physical and Theoretical Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.1c00631
Reference104 articles.
1. Quantum Chemistry in the Age of Machine Learning
2. Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design
3. Machine learning for molecular and materials science
4. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
5. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
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