Machine Learning Potential for Copper Hydride Clusters: A Neutron Diffraction-Independent Approach for Locating Hydrogen Positions

C Cong Fang (State Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao New Energy Shandong Laboratory, Qingdao Institute of Bioenergy and Bioprocess Technology) Z Zhuang Wang (State Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao New Energy Shandong Laboratory, Qingdao Institute of Bioenergy and Bioprocess Technology) R Ruixian Guo (State Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao New Energy Shandong Laboratory, Qingdao Institute of Bioenergy and Bioprocess Technology) Y Yuxiao Ding (State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Lanzhou Institute of Chemical Physics) S Sicong Ma (State Key Laboratory of Green Pesticide, College of Chemistry) X Xiaoyan Sun

Article Details

Volume / Issue Vol. 147, Issue 12
Published March 26, 2025
Pages 10750-10757
ISSN 0002-7863
Publisher American Chemical Society

Journal Info

Journal of the American Chemical Society

American Chemical Society

ISSN: 0002-7863 Physical Sciences

Authors (6)

C

Cong Fang

State Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao New Energy Shandong Laboratory, Qingdao Institute of Bioenergy and Bioprocess Technology

Z

Zhuang Wang

State Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao New Energy Shandong Laboratory, Qingdao Institute of Bioenergy and Bioprocess Technology

R

Ruixian Guo

State Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao New Energy Shandong Laboratory, Qingdao Institute of Bioenergy and Bioprocess Technology

Y

Yuxiao Ding

State Key Laboratory of Low Carbon Catalysis and Carbon Dioxide Utilization, Lanzhou Institute of Chemical Physics

S

Sicong Ma

State Key Laboratory of Green Pesticide, College of Chemistry

X

Xiaoyan Sun