A deep learning interatomic potential developed for atomistic simulation of carbon materials

Wang, Jinjin; Shen, Hong; Yang, Riyi; Xie, Kun; Zhang, Chao; Chen, Liangyao; Ho, Kai-Ming; Wang, Cai-Zhuang; Wang, Songyou

doi:10.1016/j.carbon.2021.09.062

Title: A deep learning interatomic potential developed for atomistic simulation of carbon materials

Journal Article · Fri Oct 01 00:00:00 EDT 2021 · Carbon

DOI:https://doi.org/10.1016/j.carbon.2021.09.062· OSTI ID:1833551

Wang, Jinjin ^[1]; Shen, Hong ^[2]; Yang, Riyi ^[2]; Xie, Kun ^[2];

^[3]; Chen, Liangyao ^[2]; Ho, Kai-Ming ^[4]; Wang, Cai-Zhuang ^[4];

^[5]

Fudan Univ., Shanghai (China). Shanghai Ultra-Precision Optical Manufacturing Engineering Center; Ames Lab., and Iowa State Univ., Ames, IA (United States)
Fudan Univ., Shanghai (China). Shanghai Ultra-Precision Optical Manufacturing Engineering Center
Yantai Univ. (China)
Ames Lab., and Iowa State Univ., Ames, IA (United States)
Fudan Univ., Shanghai (China). Shanghai Ultra-Precision Optical Manufacturing Engineering Center; Ames Lab., and Iowa State Univ., Ames, IA (United States); Key Laboratory for Information Science of Electromagnetic Waves, Shanghai (China)

Interatomic potentials based on neural-network machine learning method have attracted considerable attention in recent years owing to their outstanding ability to balance the accuracy and efficiency in atomistic simulations. In this work, a neural-network potential (NNP) for carbon is generated to simulate the structural properties of various carbon structures. The potential is trained using a database consisting of crystalline and liquid structures obtained by the first-principles density functional theory (DFT) calculations. The developed potential accurately predicts the energies and forces in crystalline and liquid carbon structures, the energetic stability of defected graphene, and the structures of amorphous carbon as the function of density. As a result, the excellent accuracy and transferability of the NNP provide a promising tool for accurate atomistic simulations of various carbon materials with faster speed and much lower cost.

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Research Organization:: Ames Lab., Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); National Natural Science Foundation of China (NSFC); Shanghai Municipal Science and Technology Commission

Grant/Contract Number:: AC02-07CH11358; 18JC1411500; 11374055; 61427815; 11874318

OSTI ID:: 1833551

Alternate ID(s):: OSTI ID: 1868716

Report Number(s):: IS-J 10,608; 11374055; 61427815; 18JC1411500; 19JC1416600; ZR2018MA043; 11874318; DE-AC02-07CH11358

Journal Information:: Carbon, Vol. 186; ISSN 0008-6223

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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