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

Journal Article · · Carbon
 [1];  [2];  [2];  [2];  [3];  [2];  [4];  [4];  [5]
  1. Fudan Univ., Shanghai (China). Shanghai Ultra-Precision Optical Manufacturing Engineering Center; Ames Lab., and Iowa State Univ., Ames, IA (United States)
  2. Fudan Univ., Shanghai (China). Shanghai Ultra-Precision Optical Manufacturing Engineering Center
  3. Yantai Univ. (China)
  4. Ames Lab., and Iowa State Univ., Ames, IA (United States)
  5. 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)
+ Show Author Affiliations
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.
Research Organization:
Ames Laboratory (AMES), Ames, IA (United States)
Sponsoring Organization:
National Natural Science Foundation of China (NSFC); Shanghai Municipal Science and Technology Commission; USDOE Office of Science (SC), Basic Energy Sciences (BES)
Grant/Contract Number:
AC02-07CH11358
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, Journal Name: Carbon Vol. 186; ISSN 0008-6223
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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36 MATERIALS SCIENCE