Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) AccuracyClick to copy article linkArticle link copied!
- Michael S. ChenMichael S. ChenDepartment of Chemistry, Stanford University, Stanford, California94305, United StatesMore by Michael S. Chen
- Joonho LeeJoonho LeeDepartment of Chemistry, Columbia University, New York, New York10027, United StatesMore by Joonho Lee
- Hong-Zhou YeHong-Zhou YeDepartment of Chemistry, Columbia University, New York, New York10027, United StatesMore by Hong-Zhou Ye
- Timothy C. Berkelbach*Timothy C. Berkelbach*E-mail: [email protected]Department of Chemistry, Columbia University, New York, New York10027, United StatesCenter for Computational Quantum Physics, Flatiron Institute, New York, New York10010, United StatesMore by Timothy C. Berkelbach
- David R. Reichman*David R. Reichman*E-mail: [email protected]Department of Chemistry, Columbia University, New York, New York10027, United StatesMore by David R. Reichman
- Thomas E. Markland*Thomas E. Markland*E-mail: [email protected]Department of Chemistry, Stanford University, Stanford, California94305, United StatesMore by Thomas E. Markland
Abstract

Obtaining the atomistic structure and dynamics of disordered condensed-phase systems from first-principles remains one of the forefront challenges of chemical theory. Here we exploit recent advances in periodic electronic structure and provide a data-efficient approach to obtain machine-learned condensed-phase potential energy surfaces using AFQMC, CCSD, and CCSD(T) from a very small number (≤200) of energies by leveraging a transfer learning scheme starting from lower-tier electronic structure methods. We demonstrate the effectiveness of this approach for liquid water by performing both classical and path integral molecular dynamics simulations on these machine-learned potential energy surfaces. By doing this, we uncover the interplay of dynamical electron correlation and nuclear quantum effects across the entire liquid range of water while providing a general strategy for efficiently utilizing periodic correlated electronic structure methods to explore disordered condensed-phase systems.
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