no code implementations • 31 Dec 2023 • Sihao Yuan, Xu Han, Jun Zhang, Zhaoxin Xie, Cheng Fan, Yunlong Xiao, Yi Qin Gao, Yi Isaac Yang
We applied this approach to study a Claisen rearrangement reaction and a Carbonyl insertion reaction catalyzed by Manganese.
2 code implementations • 20 Aug 2022 • Jun Zhang, Sirui Liu, Mengyun Chen, Haotian Chu, Min Wang, Zidong Wang, Jialiang Yu, Ningxi Ni, Fan Yu, Diqing Chen, Yi Isaac Yang, Boxin Xue, Lijiang Yang, YuAn Liu, Yi Qin Gao
Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development.
no code implementations • 22 Dec 2020 • Jun Zhang, Yao-Kun Lei, Yaqiang Zhou, Yi Isaac Yang, Yi Qin Gao
Deep learning is changing many areas in molecular physics, and it has shown great potential to deliver new solutions to challenging molecular modeling problems.
no code implementations • 13 Nov 2020 • Jun Zhang, Yao-Kun Lei, Zhen Zhang, Xu Han, Maodong Li, Lijiang Yang, Yi Isaac Yang, Yi Qin Gao
Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach (RL$^\ddag$) to automatically unravel chemical reaction mechanisms.
no code implementations • 25 Apr 2020 • Jun Zhang, Yao-Kun Lei, Zhen Zhang, Junhan Chang, Maodong Li, Xu Han, Lijiang Yang, Yi Isaac Yang, Yi Qin Gao
Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems.
no code implementations • 7 Jun 2019 • Jun Zhang, Yao-Kun Lei, Xing Che, Zhen Zhang, Yi Isaac Yang, Yi Qin Gao
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 yield reduced and clustered representation for the FEL.