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

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Coupled Cluster Molecular Dynamics of Condensed Phase Systems Enabled by Machine Learning Potentials: Liquid Water Benchmark
Physical Review Letters ( IF 8.1 ) Pub Date : 2022-11-23 , DOI: 10.1103/physrevlett.129.226001
János Daru ₁ , Harald Forbert ₂ , Jörg Behler ₃ , Dominik Marx ₁

Affiliation

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.

中文翻译：

由机器学习潜力实现的凝聚相系统的耦合簇分子动力学：液态水基准

耦合团簇理论是一种通用的、系统的电子结构方法，但特别是高精度的“金标准”耦合团簇单、双和微扰三元组CCSD(T)，只能应用于小系统。为了克服这一限制，我们引入了一个框架，以使用高维神经网络势将有限分子簇的 CCSD(T) 精度转移到扩展凝聚相系统。这种自动化的方法允许人们执行高质量的耦合簇分子动力学，CCMD，正如我们对包括核量子效应在内的液态水所展示的那样。机器学习策略非常高效、通用，可以系统地改进，适用于各种复杂系统。

更新日期：2022-11-23

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