Extending machine learning beyond interatomic potentials for predicting molecular properties-Reference-Cited by-同舟云学术

Extending machine learning beyond interatomic potentials for predicting molecular properties

Published:2022-08-25 Issue:9 Volume:6 Page:653-672
ISSN:2397-3358
Container-title:Nature Reviews Chemistry
language:en
Short-container-title:Nat Rev Chem

Author:

Fedik Nikita^ORCID,Zubatyuk Roman^ORCID,Kulichenko Maksim^ORCID,Lubbers Nicholas^ORCID,Smith Justin S.^ORCID,Nebgen Benjamin^ORCID,Messerly Richard^ORCID,Li Ying Wai^ORCID,Boldyrev Alexander I.^ORCID,Barros Kipton^ORCID,Isayev Olexandr^ORCID,Tretiak Sergei^ORCID

Publisher

Springer Science and Business Media LLC

Subject

General Chemical Engineering,General Chemistry

Link

https://www.nature.com/articles/s41570-022-00416-3.pdf

Reference181 articles.

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3. Mardirossian, N. & Head-Gordon, M. Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals. Mol. Phys. 115, 2315–2372 (2017).

4. Thiel, W. Semiempirical quantum–chemical methods. WIREs Comput. Mol. Sci. 4, 145–157 (2014).

5. Ratcliff, L. E. et al. Challenges in large scale quantum mechanical calculations. WIREs Comput. Mol. Sci. 7, e1290 (2017).

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