1. A deep learning interatomic potential developed for atomistic simulation of carbon materials
- Author
-
Cai-Zhuang Wang, Chao Zhang, Kai-Ming Ho, Hong Shen, Songyou Wang, Kun Xie, Liang-Yao Chen, Riyi Yang, and Jinjin Wang
- Subjects
Work (thermodynamics) ,Materials science ,business.industry ,Graphene ,Deep learning ,Stability (learning theory) ,chemistry.chemical_element ,Interatomic potential ,General Chemistry ,law.invention ,Amorphous carbon ,chemistry ,Chemical physics ,law ,General Materials Science ,Density functional theory ,Artificial intelligence ,business ,Carbon - Abstract
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. 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.
- Published
- 2022