An overview about neural networks potentials in molecular dynamics simulation

Ab-initio molecular dynamics (AIMD) is a key method for realistic simulation of complex atomistic systems and processes in nanoscale. In AIMD, finite-temperature dynamical trajectories are generated by using forces computed from electronic structure calculations. In systems with high numbers of components a typical AIMD run is computationally demanding. On the other hand, machine learning (ML) is a subfield of the artificial intelligence that consist in a set of algorithms that show learning by experience with the use of input and output data where algorithms are capable of analysing and predicting the future. At present, the main application of ML techniques in atomic simulations is the development of new interatomic potentials to correctly describe the potential energy surfaces (PES). This technique is in constant progress since its inception around 30 years ago. The ML potentials combine the advantages of classical and Ab-initio methods, that is, the efficiency of a simple functional form and the accuracy of first principles calculations. In this article we review the evolution of four generations of machine learning potentials and some of their most notable applications. This review focuses on MLPs based on neural networks. Also, we present a state of art of this topic and future trends. Finally, we report the results of a scientometric study (covering the period 1995-2023) about the impact of ML techniques applied to atomistic simulations, distribution of publications by geographical regions and hot topics investigated in the literature. Machine learning potentials based on neural network are one of the most popular approximations to study molecular systems by molecular dynamics; recent progresses include development of fundamental invariant neural network graph and neural networks force field models. A review about these topics aided by a scientometrics study was conducted. image