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Nature Communications|October 12, 2023
Universal machine learning for the response of atomistic systems to external fieldsYaolong Zhang, Bin Jiang
Physical Chemistry Chemical Physics : PCCP|November 25, 2020
Accelerating atomistic simulations with piecewise machine-learned <i>ab Initio</i> potentials at a classical force field-like costYaolong Zhang, Ce Hu, Bin Jiang
The Journal of Physical Chemistry Letters|September 21, 2019
Strong Vibrational Relaxation of NO Scattered from Au(111): Importance of the Adiabatic Potential Energy SurfaceRongrong Yin, Yaolong Zhang, Bin Jiang
Journal of Chemical Theory and Computation|January 3, 2025
Efficient Sampling for Machine Learning Electron Density and Its Response in Real SpaceChaoqiang Feng, Yaolong Zhang, Bin Jiang
Physical Review Letters|October 22, 2021
Physically Motivated Recursively Embedded Atom Neural Networks: Incorporating Local Completeness and NonlocalityYaolong Zhang, Junfan Xia, Bin Jiang
The Journal of Physical Chemistry Letters|February 26, 2019
Bridging the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surfaces Using Neural NetworksYaolong Zhang, Xueyao Zhou, Bin Jiang
The Journal of Physical Chemistry Letters|August 10, 2019
Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired RepresentationYaolong Zhang, Ce Hu, Bin Jiang
Journal of Chemical Theory and Computation|January 8, 2025
SchrödingerNet: A Universal Neural Network Solver for the Schrödinger EquationYaolong Zhang, Bin Jiang, Hua Guo
The Journal of Physical Chemistry. A|November 9, 2023
Accuracy Assessment of Atomistic Neural Network Potentials: The Impact of Cutoff Radius and Message PassingJunfan Xia, Yaolong Zhang, Bin Jiang
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