Application of Machine-Learning Assisted Dynamics Simulations in Nano-Scale Catalysis

As one of the important simulation methods in computational catalysis, molecular dynamics (MD) simulation plays an important role in understanding the catalytic mechanisms and is critical to the design of efficient and stable catalysts. Classical MD simulation with empirical potentials has a high computational efficiency but a limited accuracy, particularly for systems involving chemical reactions, and the accurate first-principle methods suffer from heavy computational costs and become unaffordable in most cases. The existing emerging machine-learning force field (MLFF) method is proven with affordable computational cost and first-principle-level accuracy. MLFF-assisted MD simulation can offer an effective approach for dynamics simulation in nanoscale catalysis. This review represented the fundamental principle of two main MLFF methods, i.e., the Behler-Parrinello atom-centered neural network method and the embedded-network-based deep potential. The applications of MLFF-assisted dynamic studies related to nano-scale catalysis (i.e., structure reconstruction and reaction processes in catalysis) were described. In addition, some possible future challenges of MLFF methods in dynamics simulation were also given.