A review of machine learning potentials and their applications to molecular simulation

Molecular dynamics simulation has become an important tool for the research and development of chemical engineering processes and technologies. However, the insufficient accuracy of classical molecular dynamics simulations and the high computational cost of ab initio molecular dynamics simulations have restricted the widespread applications of molecular simulation technology. The emergence and development of machine learning technology has led to the rapid development of molecular simulation based on machine learning potentials, which offers an efficient way to achieve a greatly improved accuracy at a lower computing loading, thereby bolstering the potential of molecular simulations in practical applications. This review started by an overview of the development of machine learning potentials with emphasis on the construction methods and principles of machine learning potential models. The techniques associated with machine learning potentials including dataset construction, model training, model transfer and application were detailed. The strengths and weaknesses of different types of machine learning models were also discussed, followed by the prospects for the development and applications machine learning potentials. © 2024 Materials China. All rights reserved.