64 citations · 64 across the 3 of their papers we have counts for
4 papers
Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics
Junfan Xia, Bin Jiang
Machine learning potentials have achieved great success in accelerating atomistic simulations. Many of them relying on atom-centered local descriptors are natural for parallelizati…
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,…