Work

Deep reinforcement learning of transition states

Year: 2021

Type: article

Abstract: RL ‡ can automatically locate the transition states of chemical reactions through deep reinforcement learning of feedback from molecular simulations.

Source: Physical Chemistry Chemical Physics

DOI: 10.1039/d0cp06184k

Authors: Jun Zhang, Yao-Kun Lei, Zhen Zhang, Xu Han, Maodong Li, Lijiang Yang, Yi Isaac Yang, Yi Qin Gao

Institutions: Shenzhen Bay Laboratory, Peking University, Beijing National Laboratory for Molecular Sciences, Tangshan Normal University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China

Awards: G1041984945, G1943808332, G559444874, G6843369216, 细胞发育、分化和疾病发生过程中的染色质相分离与基因调控机制研究

OpenAlex ID: W3103339982

FWCI: 1.738

Cites: 34

Cited by: 44

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