57 citations · 153 across the 9 of their papers we have counts for
9 papers
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…
Invertible Coarse Graining with Physics-Informed Generative Artificial Intelligence
Jun Zhang, Xiaohan Lin, Weinan E +1
Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales. Two specific challenges are commonly present in mu…
DSDP: A Blind Docking Strategy Accelerated by GPUs
YuPeng Huang, Hong Zhang, Siyuan Jiang +4
Virtual screening, including molecular docking, plays an essential role in drug discovery. Many traditional and machine-learning based methods are available to fulfil the docking t…
Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction
Jun Zhang, Sirui Liu, Mengyun Chen +12
Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and…
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…
Molecular CT: Unifying Geometry and Representation Learning for Molecules at Different Scales
Jun Zhang, Yao-Kun Lei, Yaqiang Zhou +2
Deep learning is changing many areas in molecular physics, and it has shown great potential to deliver new solutions to challenging molecular modeling problems. Along with this tre…
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…
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…
Learning Clustered Representation for Complex Free Energy Landscapes
Jun Zhang, Yao-Kun Lei, Xing Che +3
In this paper we first analyzed the inductive bias underlying the data scattered across complex free energy landscapes (FEL), and exploited it to train deep neural networks which y…