6 papers
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…
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 wav…
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,…
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…