Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy
Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy
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DOI：
10.1021/acs.jctc.2c01203
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发表时间：
2023-02-02
影响因子：
5.5
通讯作者：
Markland, Thomas E.
中科院分区：
文献类型：
--
作者：
Chen, Michael S.;Lee, Joonho;Markland, Thomas E.
从第一性原理获得无序凝聚相系统的原子结构和动力学仍然是化学理论的前沿挑战之一。在这里，我们利用了周期性电子结构的最新进展，并提供了一种数据高效的方法来使用AFQMC、CCSD和CCSD(T)从非常少的(
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 (