100 citations · 142 across the 2 of their papers we have counts for
5 papers
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…
Homogeneous nucleation of ice
Haiyang Niu, Yi Isaac Yang, Michele Parrinello
Ice nucleation is a process of great relevance in physics, chemistry, technology and environmental sciences, much theoretical and experimental efforts have been devoted to its unde…
Combining Metadynamics and Integrated Tempering Sampling
Yi Isaac Yang, Haiyang Niu, Michele Parrinello
The simulation of rare events is one of the key problems in atomistic simulations. Towards its solution a plethora of methods have been proposed. Here we combine two such methods m…