activity
20202024
most citedA Perspective on Deep Learning for Molecular Modeling and Simulations

57 citations · 112 across the 5 of their papers we have counts for

collaborators

5 papers

physics.chem-ph2024

Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms using Molecular Configuration Transformer

Sihao Yuan, Xu Han, Jun Zhang +5

Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transitio…

physics.chem-ph2022

Enhanced sampling reveals the main pathway of an organic cascade reaction

Yi Sun, Xu Han, Lijiang Yang

Normal molecular dynamics simulations are usually unable to simulate chemical reactions due to the low probability of forming the transition state. Therefore, enhanced sampling met…

cond-mat.stat-mech2021★ 13 cited

Atomistic View of Homogeneous Nucleation of Water into Polymorphic Ices

Maodong Li, Jun Zhang, Niu Haiyang +6

Water is one of the most abundant substances on Earth, and ice, i.e., solid water, has more than 18 known phases. Normally ice in nature exists only as Ice Ih, Ice Ic, or a stackin…

physics.chem-ph2020★ 42 cited

Deep Reinforcement Learning of Transition States

Jun Zhang, Yao-Kun Lei, Zhen Zhang +5

Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach (RL) to automatically unravel chemical reaction mechanisms…

physics.comp-ph2020★ 57 cited

A Perspective on Deep Learning for Molecular Modeling and Simulations

Jun Zhang, Yao-Kun Lei, Zhen Zhang +6

Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vi…