GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
Journal article, 2022
Author
Zheyong Fan
Bohai University
Yanzhou Wang
Aalto University
Penghua Ying
Harbin Institute of Technology
Keke Song
University of Science and Technology Beijing
Junjie Wang
Nanjing University
Yong Wang
Nanjing University
Zezhu Zeng
The University of Hong Kong
Ke Xu
Xiamen University
Eric Lindgren
Chalmers, Physics, Condensed Matter and Materials Theory
Magnus Rahm
Chalmers, Physics, Condensed Matter and Materials Theory
Alexander J. Gabourie
Stanford University
Jiahui Liu
University of Science and Technology Beijing
Haikuan Dong
Bohai University
Jianyang Wu
Xiamen University
Yue Chen
The University of Hong Kong
Zheng Zhong
Harbin Institute of Technology
Jian Sun
Nanjing University
Paul Erhart
Chalmers, Physics, Condensed Matter and Materials Theory
Yanjing Su
University of Science and Technology Beijing
Tapio Ala-Nissila
Aalto University
Journal of Chemical Physics
0021-9606 (ISSN) 1089-7690 (eISSN)
Vol. 157 11 114801Phase behavior and electronic properties of mixed halide perovskites from atomic scale simulations
Swedish Research Council (VR) (2020-04935), 2020-12-01 -- 2024-11-30.
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Swedish Research Council (VR) (2021-05072), 2021-12-01 -- 2025-11-30.
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SwedNESS
Swedish Foundation for Strategic Research (SSF) (GSn15-0008), 2016-07-01 -- 2021-06-30.
Swedish Foundation for Strategic Research (SSF) (GSn15-0008), 2017-01-01 -- 2020-12-31.
Subject Categories
Computational Mathematics
Bioinformatics (Computational Biology)
Computer Science
Infrastructure
C3SE (Chalmers Centre for Computational Science and Engineering)
Areas of Advance
Materials Science
DOI
10.1063/5.0106617
PubMed
36137808