297 citations · 874 across the 18 of their papers we have counts for
19 papers
Mode-Specific Dynamics of Hydrogenation on Copper: The Hidden Role of Molecular Rotation
Junfan Xia, Zhikai Jiang, Yaolong Zhang +3
Catalytic hydrogenation of to formate on copper is a key elementary step for utilization. Previous experimental and theoretical studies suggested an Ele…
Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials
Yaolong Zhang, Hua Guo
Machine learned interatomic potentials, particularly equivariant message-passing (MP) models, have demonstrated high fidelity in representing first-principles data, revolutionizing…
The Evolution of Machine Learning Potentials for Molecules, Reactions and Materials
Junfan Xia, Yaolong Zhang, Bin Jiang
Recent years have witnessed the fast development of machine learning potentials (MLPs) and their widespread applications in chemistry, physics, and material science. By fitting dis…
Efficient Sampling for Machine Learning Electron Density and Its Response in Real Space
Chaoqiang Feng, Yaolong Zhang, Bin Jiang
Electron density is a fundamental quantity, which can in principle determine all ground state electronic properties of a given system. Although machine learning (ML) models for ele…
A Simple and Efficient Equivariant Message Passing Neural Network Model for Non-Local Potential Energy Surface
Yibin Wu, Junfan Xia, Yaolong Zhang +1
Machine learning potentials have become increasingly successful in atomistic simulations. Many of these potentials are based on an atomistic representation in a local environment,…
SchrödingerNet: A Universal Neural Network Solver for The Schrödinger Equation
Yaolong Zhang, Bin Jiang, Hua Guo
Recent advances in machine learning have facilitated numerically accurate solution of the electronic Schrödinger equation (SE) by integrating various neural network (NN)-based wave…
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
Wojciech G. Stark, Cas van der Oord, Ilyes Batatia +4
Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynami…
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…
Learning Dipole Moments and Polarizabilities
Yaolong Zhang, Jun Jiang, Bin Jiang
Machine learning of scalar molecular properties such as potential energy has enabled widespread applications. However, there are relatively few machine learning models targeting di…
Physically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and Nonlocality
Yaolong Zhang, Junfan Xia, Bin Jiang
Recent advances in machine-learned interatomic potentials largely benefit from the atomistic representation and locally invariant many-body descriptors. It was however recently arg…
Determining the effect of hot electron dissipation on molecular scattering experiments at metal surfaces
Connor L. Box, Yaolong Zhang, Rongrong Yin +2
Nonadiabatic effects that arise from the concerted motion of electrons and atoms at comparable energy and time scales are omnipresent in thermal and light-driven chemistry at metal…
Accelerating Atomistic Simulations with Piecewise Machine Learned Ab Initio Potentials at Classical Force Field-like Cost
Yaolong Zhang, Ce Hu, Bin Jiang
Machine learning methods have nowadays become easy-to-use tools for constructing high-dimensional interatomic potentials with ab initio accuracy. Although machine learned interatom…
Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties
Yaolong Zhang, Sheng Ye, Jinxiao Zhang +3
Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models target…
Automatically growing global reactive neural network potential energy surfaces: a trajectory free active learning strategy
Qidong Lin, Yaolong Zhang, Bin Zhao +1
An efficient and trajectory-free active learning method is proposed to automatically sample data points for constructing globally accurate reactive potential energy surfaces (PESs)…
Symmetry-Adapted High Dimensional Neural Network Representation of Electronic Friction Tensor of Adsorbates on Metals
Yaolong Zhang, Reinhard J. Maurer, Bin Jiang
Nonadiabatic effects in chemical reaction at metal surfaces, due to excitation of electron-hole pairs, stand at the frontier of the studies of gas-surface reaction dynamics. Howeve…
Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
Yaolong Zhang, Ce Hu, Bin Jiang
We propose a simple, but efficient and accurate machine learning (ML) model for developing high-dimensional potential energy surface. This so-called embedded atom neural network (E…
Strong Vibrational Relaxation of NO Scattered from Au(111): Importance of an Accurate Adiabatic Potential Energy Surface
Rongrong Yin, Yaolong Zhang, Bin Jiang
Experimental observations of multi-quantum relaxation of highly vibrationally excited NO scattering from Au(111) are a benchmark for the breakdown of Born-Oppenheimer approximation…
Adiabatic and Nonadiabatic Energy Dissipation during Scattering of Vibrationally Excited CO from Au(111)
Meng Huang, Xueyao Zhou, Yaolong Zhang +4
A high-dimensional potential energy surface (PES) for CO interaction with the Au(111) surface is developed using a machine-learning algorithm. Including both molecular and surface…