Frontiers of Data and Computing ›› 2023, Vol. 5 ›› Issue (2): 37-49.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.02.003

doi: 10.11871/jfdc.issn.2096-742X.2023.02.003

• Special Issue: AI for Science • Previous Articles     Next Articles

Research on Quantum Proton Coupled Charge Transfer Process Based on Deep Neural Network

TU Youyou(),ZHENG Qijing*(),ZHAO Jin*()   

  1. Department of Physics and ICQD/Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, China
  • Received:2023-02-15 Online:2023-04-20 Published:2023-04-24
  • Contact: ZHENG Qijing,ZHAO Jin;;


[Objective] Understanding the physical mechanism of ultrafast charge transfer at the molecule/solid interfaces plays a crucial role in improving the energy conversion efficiency of solar cells and photocatalytic reactions. [Scope of the literature] At many molecules/solid interfaces containing hydrogen, molecules adsorption forms a certain structure, then molecular hydrogen ions can form the hydrogen bond (H-bond) network on the surface. Because of the very light mass of hydrogen, and its frequent existence in the form of proton in molecules, one can often observe the charge transfer process in which protons couple with electrons at the molecule/solid interface, and the nuclear quantum effects (NQEs) of proton cannot be ignored in these processes. Recently, we developed a computational method RP-NAMD combining path-integral molecular dynamics (PIMD) and nonadiabatic molecular dynamics (NAMD), which can effectively study the proton-charge coupled charge transfer process. However, the huge amount of computation required by PIMD/RPMD limits the application of these methods in real systems. [Methods] In this paper, we use the deep learning method to greatly reduce the computational cost of PIMD/RPMD, so that RP-NAMD can be applied to larger systems and can be used to study dynamical processes at a longer time scale. To validate this new method, we chose the CH3OH/anatase TiO2 interface as a practical system and investigated the important role of nuclear quantum effects in the interfacial proton-coupled charge transfer process. [Conclusions] In short, based on deep learning, this paper provides a practical calculation method that can be used to study the coupling between nuclear quantum effects and carrier dynamics.

Key words: deep learning, path-integral molecular dynamics, proton-coupled charge transfer