A transferable recommender approach for selecting the best density functional approximations in chemical discovery-Reference-Cited by-同舟云学术

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A transferable recommender approach for selecting the best density functional approximations in chemical discovery

Published:2022-12-22 Issue:1 Volume:3 Page:38-47
ISSN:2662-8457
Container-title:Nature Computational Science
language:en
Short-container-title:Nat Comput Sci

Author:

Duan Chenru^ORCID,Nandy Aditya,Meyer Ralf^ORCID,Arunachalam Naveen^ORCID,Kulik Heather J.^ORCID

Funder

United States Department of Defense | United States Navy | Office of Naval Research

National Science Foundation

U.S. Department of Energy

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Computer Science (miscellaneous)

Link

https://www.nature.com/articles/s43588-022-00384-0.pdf

Reference56 articles.

1. Coley, C. W., Eyke, N. S. & Jensen, K. F. Autonomous discovery in the chemical sciences part I: progress. Angew. Chem. Int. Ed. Engl. 59, 22858–22893 (2020).

2. Jain, A. et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).

3. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).

4. Carleo, G. et al. Machine learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019).

5. Nandy, A. et al. Computational discovery of transition-metal complexes: from high-throughput screening to machine learning. Chem. Rev. 121, 9927–10000 (2021).

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