Article ; Online: An accurate interatomic potential for the TiAlNb ternary alloy developed by deep neural network learning method.
The Journal of chemical physics
2023 Volume 158, Issue 20
Abstract: The complex phase diagram and bonding nature of the TiAl system make it difficult to accurately describe its various properties and phases by traditional atomistic force fields. Here, we develop a machine learning interatomic potential with a deep neural ...
Abstract | The complex phase diagram and bonding nature of the TiAl system make it difficult to accurately describe its various properties and phases by traditional atomistic force fields. Here, we develop a machine learning interatomic potential with a deep neural network method for the TiAlNb ternary alloy based on a dataset built by first-principles calculations. The training set includes bulk elementary metals and intermetallic structures with slab and amorphous configurations. This potential is validated by comparing bulk properties-including lattice constant and elastic constants, surface energies, vacancy formation energies, and stacking fault energies-with their respective density functional theory values. Moreover, our potential could accurately predict the average formation energy and stacking fault energy of γ-TiAl doped with Nb. The tensile properties of γ-TiAl are simulated by our potential and verified by experiments. These results support the applicability of our potential under more practical conditions. |
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Language | English |
Publishing date | 2023-05-22 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 3113-6 |
ISSN | 1089-7690 ; 0021-9606 |
ISSN (online) | 1089-7690 |
ISSN | 0021-9606 |
DOI | 10.1063/5.0147720 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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