Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy-Reference-Cited by-同舟云学术

Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy

Published:2023-02-02 Issue:14 Volume:19 Page:4510-4519
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Chen Michael S.¹^ORCID,Lee Joonho²^ORCID,Ye Hong-Zhou²^ORCID,Berkelbach Timothy C.²³^ORCID,Reichman David R.²,Markland Thomas E.¹^ORCID

Affiliation:

1. Department of Chemistry, Stanford University, Stanford, California94305, United States

2. Department of Chemistry, Columbia University, New York, New York10027, United States

3. Center for Computational Quantum Physics, Flatiron Institute, New York, New York10010, United States

Funder

Division of Chemistry

Air Force Office of Scientific Research

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.2c01203

Reference64 articles.

1. The individual and collective effects of exact exchange and dispersion interactions on the ab initio structure of liquid water

2. Perspective: How good is DFT for water?

3. Bulk Liquid Water at Ambient Temperature and Pressure from MP2 Theory

4. How van der Waals interactions determine the unique properties of water

5. The Interplay of Structure and Dynamics in the Raman Spectrum of Liquid Water over the Full Frequency and Temperature Range

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