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

Journal Article · · Journal of Chemical Theory and Computation
 [1];  [2];  [2];  [3];  [2];  [1]
  1. Department of Chemistry, Stanford University, Stanford, California94305, United States
  2. Department of Chemistry, Columbia University, New York, New York10027, United States
  3. Department of Chemistry, Columbia University, New York, New York10027, United States; Center for Computational Quantum Physics, Flatiron Institute, New York, New York10010, United States
+ Show Author Affiliations

Not provided.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC02-05CH11231
OSTI ID:
2422916
Journal Information:
Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Journal Issue: 14 Vol. 19; ISSN 1549-9618
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English

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