72 citations · 73 across the 5 of their papers we have counts for
5 papers
Machine learning accelerated finite-field simulations for electrochemical interfaces
Chaoqiang Feng, Bin Jiang
Electrochemical interfaces are of fundamental importance in electrocatalysis, batteries, and metal corrosion. Finite-field methods are one of most reliable approaches for modeling…
Mechanistic Insights into Non-Adiabatic Interband Transitions on a Semiconductor Surface Induced by Hydrogen Atom Collisions
Lingjun Zhu, Qijing Zheng, Yingqi Wang +6
To understand the recently observed mysterious non-adiabatic energy transfer for hyperthermal H atom scattering from a semiconductor surface, Ge(111)c(2*8), we present a mixed quan…
First-Principles Modeling of Equilibration Dynamics of Hyperthermal Products of Surface Reactions Using Scalable Neural Network Potential
Qidong Lin, Bin Jiang
Equilibration dynamics of hot oxygen atoms following O2 dissociation on Pd(100) and Pd(111) surfaces are investigated by molecular dynamics simulations based on a scalable neural n…
Universal Machine Learning for the Response of Atomistic Systems to External Fields
Yaolong Zhang, Bin Jiang
Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change t…
REANN: A PyTorch-based End-to-End Multi-functional Deep Neural Network Package for Molecular, Reactive and Periodic Systems
Yaolong Zhang, Junfan Xia, Bin Jiang
In this work, we present a general purpose deep neural network package for representing energies, forces, dipole moments, and polarizabilities of atomistic systems. This so-called…