Coupled Cluster Molecular Dynamics of Condensed Phase Systems Enabled by Machine Learning Potentials: Liquid Water Benchmark

被引：63

作者：

Daru, Janos ^{[1
]}

Forbert, Harald ^{[2
]}

Behler, Joerg ^{[3
,4
,5
]}

Marx, Dominik ^{[1
]}

机构：

[1] Ruhr Univ Bochum, Lehrstuhl Theoret Chem, D-44780 Bochum, Germany

[2] Ruhr Univ Bochum, Ctr Solvat Sci ZEMOS, D-44780 Bochum, Germany

[3] Univ Gottingen, Inst Phys Chem, Theoret Chem, Tammannstr 6, D-37077 Gottingen, Germany

[4] Ruhr Univ Bochum, Lehrstuhl Theoret Chem II, D-44780 Bochum, Germany

[5] Res Alliance Ruhr, Res Ctr Chem Sci & Sustainabil, D-44780 Bochum, Germany

来源：

PHYSICAL REVIEW LETTERS | 2022年 / 129卷 / 22期

关键词：

DENSITY-FUNCTIONAL THEORY; SELF-DIFFUSION; NMR RELAXATION; APPROXIMATIONS; REORIENTATION; SIMULATIONS; DEPENDENCE; ACCURACY; SPECTRUM; PROTON;

D O I：

10.1103/PhysRevLett.129.226001

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Coupled cluster theory is a general and systematic electronic structure method, but in particular the highly accurate "gold standard" coupled cluster singles, doubles and perturbative triples, CCSD(T), can only be applied to small systems. To overcome this limitation, we introduce a framework to transfer CCSD(T) accuracy of finite molecular clusters to extended condensed phase systems using a high-dimensional neural network potential. This approach, which is automated, allows one to perform high-quality coupled cluster molecular dynamics, CCMD, as we demonstrate for liquid water including nuclear quantum effects. The machine learning strategy is very efficient, generic, can be systematically improved, and is applicable to a variety of complex systems.

引用

页数：6

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