A Status Report on Gold Standard Machine-Learned Potentials for Water

Owing to the central importance of water to life as well as its unusual properties, potentials for water have been the subject of extensive research over the past 50 years. Recently, five potentials based on different machine learning approaches have been reported that are at or near the gold standard CCSD(T) level of theory. The development of such high-level potentials enables efficient and accurate simulations of water systems using classical and quantum dynamical approaches. This Perspective serves as a status report of these potentials, focusing on their methodology and applications to water systems across different phases. Their performances on the energies of gas phase water clusters, as well as condensed phase structural and dynamical properties, are discussed.

Automated summary

Due to the importance and unique properties of water, extensive research has been conducted on its potentials over the past 50 years. Recently, five potentials based on different machine learning approaches have been developed that are comparable to the gold standard CCSD(T) theory. These high-level potentials enable efficient and accurate simulation of water systems using classical and quantum dynamical approaches. This Perspective provides a status report on these potentials, focusing on their methodology and applications to water systems in different phases, including gas and condensed phases.