Details
| Original language | English |
|---|---|
| Pages (from-to) | 431-441 |
| Number of pages | 11 |
| Journal | CARBON |
| Volume | 188 |
| Early online date | 8 Dec 2021 |
| Publication status | Published - Mar 2022 |
Abstract
Graphene-like lattices consisting of neighboring elements of boron, carbon and nitrogen are currently among the most attractive two-dimensional (2D) nanomaterials. Most recently, a novel graphene-like lattice with a BC2N stoichiometry has been grown over nickel catalyst via molecular precursor. Inspired by this experimental advance and exciting physics of h-BxCyNz lattices, herein extensive theoretical calculations are carried out to investigate physical properties of three different h-BC2N lattices. Density functional theory (DFT) results confirm direct-gap semiconducting electronic nature of the BC2N monolayers. In this work, state-of-the-art models based on the machine-learning interatomic potentials (MLIPs) are employed to elaborately explore the mechanical/failure and heat transport properties of various BC2N monolayers under ambient conditions. Outstanding accuracy of the developed MLIP-based classical models are confirmed by comparing the estimations with those by DFT. MLIP-based models are also found to outperform empirical interatomic potentials. It is shown that while the mechanical/failure responses are close for different BC2N lattices, the change of an atomic configuration can result in around four-fold differences in the lattice thermal conductivity. The obtained results confirm the robustness of MLIP-based models and moreover provide an extensive vision concerning the critical physical properties of the BC2N nanosheets and highlight their outstanding heat conduction, mechanical, and electronic characteristics.
Keywords
- h-BCN, Machine-learning, Mechanical/failure, Semiconductor, Thermal conductivity
ASJC Scopus subject areas
- Chemistry(all)
- General Chemistry
- Materials Science(all)
- General Materials Science
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In: CARBON, Vol. 188, 03.2022, p. 431-441.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A machine-learning-based investigation on the mechanical/failure response and thermal conductivity of semiconducting BC2N monolayers
AU - Mortazavi, Bohayra
AU - Novikov, Ivan S.
AU - Shapeev, Alexander V.
N1 - Funding Information: B. M. appreciates the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). B. M. is moreover thankful to the VEGAS cluster at Bauhaus University of Weimar for providing the computational resources and Dr. Chernenko for the support of this study. I. S. N. and A.V.S. are supported by the Russian Science Foundation (Grant No 18-13-00479, https://rscf.ru/project/18-13-00479/).
PY - 2022/3
Y1 - 2022/3
N2 - Graphene-like lattices consisting of neighboring elements of boron, carbon and nitrogen are currently among the most attractive two-dimensional (2D) nanomaterials. Most recently, a novel graphene-like lattice with a BC2N stoichiometry has been grown over nickel catalyst via molecular precursor. Inspired by this experimental advance and exciting physics of h-BxCyNz lattices, herein extensive theoretical calculations are carried out to investigate physical properties of three different h-BC2N lattices. Density functional theory (DFT) results confirm direct-gap semiconducting electronic nature of the BC2N monolayers. In this work, state-of-the-art models based on the machine-learning interatomic potentials (MLIPs) are employed to elaborately explore the mechanical/failure and heat transport properties of various BC2N monolayers under ambient conditions. Outstanding accuracy of the developed MLIP-based classical models are confirmed by comparing the estimations with those by DFT. MLIP-based models are also found to outperform empirical interatomic potentials. It is shown that while the mechanical/failure responses are close for different BC2N lattices, the change of an atomic configuration can result in around four-fold differences in the lattice thermal conductivity. The obtained results confirm the robustness of MLIP-based models and moreover provide an extensive vision concerning the critical physical properties of the BC2N nanosheets and highlight their outstanding heat conduction, mechanical, and electronic characteristics.
AB - Graphene-like lattices consisting of neighboring elements of boron, carbon and nitrogen are currently among the most attractive two-dimensional (2D) nanomaterials. Most recently, a novel graphene-like lattice with a BC2N stoichiometry has been grown over nickel catalyst via molecular precursor. Inspired by this experimental advance and exciting physics of h-BxCyNz lattices, herein extensive theoretical calculations are carried out to investigate physical properties of three different h-BC2N lattices. Density functional theory (DFT) results confirm direct-gap semiconducting electronic nature of the BC2N monolayers. In this work, state-of-the-art models based on the machine-learning interatomic potentials (MLIPs) are employed to elaborately explore the mechanical/failure and heat transport properties of various BC2N monolayers under ambient conditions. Outstanding accuracy of the developed MLIP-based classical models are confirmed by comparing the estimations with those by DFT. MLIP-based models are also found to outperform empirical interatomic potentials. It is shown that while the mechanical/failure responses are close for different BC2N lattices, the change of an atomic configuration can result in around four-fold differences in the lattice thermal conductivity. The obtained results confirm the robustness of MLIP-based models and moreover provide an extensive vision concerning the critical physical properties of the BC2N nanosheets and highlight their outstanding heat conduction, mechanical, and electronic characteristics.
KW - h-BCN
KW - Machine-learning
KW - Mechanical/failure
KW - Semiconductor
KW - Thermal conductivity
UR - http://www.scopus.com/inward/record.url?scp=85120954363&partnerID=8YFLogxK
U2 - 10.1016/j.carbon.2021.12.039
DO - 10.1016/j.carbon.2021.12.039
M3 - Article
AN - SCOPUS:85120954363
VL - 188
SP - 431
EP - 441
JO - CARBON
JF - CARBON
SN - 0008-6223
ER -