学術雑誌論文

学術雑誌論文

Acta Materialia. 2024, 281, p. 120408

A highly transferable and efficient machine learning interatomic potentials study of α-Fe–C binary system

Elsevier B.V.

英語

2024-12-01

https://doi.org/10.1016/j.actamat.2024.120408

Meng F.S., Shinzato S., Zhang S., et al. A highly transferable and efficient machine learning interatomic potentials study of α-Fe–C binary system. Acta Materialia 281, 120408 (2024); https://doi.org/10.1016/j.actamat.2024.120408.

Machine learning interatomic potentials (MLIPs) for α-iron and carbon binary system have been constructed aiming for understanding the mechanical behavior of Fe–C steel and carbides. The MLIPs were trained using an extensive reference database produced by spin polarized density functional theory (DFT) calculations. The MLIPs reach the DFT accuracies in many important properties which are frequently engaged in Fe and Fe–C studies, including kinetics and thermodynamics of C in α-Fe with vacancy, grain boundary, and screw dislocation, and basic properties of cementite and cementite–ferrite interfaces. In conjunction with these MLIPs, the impact of C atoms on the mobility of screw dislocation at finite temperature, and the C-decorated core configuration of screw dislocation were investigated, and a uniaxial tensile test on a model with multiple types of defects was conducted.

This article is licensed under a Creative Commons Attribution 4.0 International License.

98584

A highly transferable and efficient machine learning interatomic potentials study of α-Fe–C binary system

Elsevier B.V.

英語

2024-12-01

Acta Materialia

281

120408

https://doi.org/10.1016/j.actamat.2024.120408

https://hdl.handle.net/11094/98584