Molecular insight into the GaP(110)-water interface using machine learning accelerated molecular dynamics

Abstract

GaP has been shown to be a promising photoelectrocatalyst for selective CO2 reduction to methanol. Due to the relevance of the interface structure to important processes such as electron/proton transfer, a detailed understanding of the GaP(110)-water interfacial structure is of great importance. Ab initio molecular dynamics (AIMD) can be used for obtaining the microscopic information of the interfacial structure. However, the GaP(110)-water interface cannot converge to an equilibrated structure at the time scale of the AIMD simulation. In this work, we perform the machine learning accelerated molecular dynamics (MLMD) to overcome the difficulty of insufficient sampling by AIMD. With the help of MLMD, we unravel the microscopic information of the structure of the GaP(110)-water interface, and obtain a deeper understanding of the mechanisms of proton transfer at the GaP(110)-water interface, which will pave the way for gaining valuable insights into photoelectrocatalytic mechanisms and improving the performance of photoelectrochemical cells.