activity
20192021
most citedPhysically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and Nonlocality

98 citations · 162 across the 3 of their papers we have counts for

collaborators

10 papers

physics.chem-ph2021

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…

physics.chem-ph202198 cited

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…

cond-mat.mtrl-sci2020

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…

physics.comp-ph2020

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…

physics.chem-ph2020

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…

physics.chem-ph2020

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)…