196 citations · 482 across the 10 of their papers we have counts for
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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…
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)…
Ring Polymer Molecular Dynamics in Gas-Surface Reactions: Inclusion of Quantum Effects Made Simple
Qinghua Liu, Liang Zhang, Yongle Li +1
Accurately modeling gas-surface collision dynamics presents a great challenge for theory, especially in the low energy (or temperature) regime where quantum effects are important.…
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…