Warning! Note that elemental potentials taken from alloy descriptions may not work well for the pure species. This is particularly true if the elements were fit for compounds instead of being optimized separately. As with all interatomic potentials, please check to make sure that the performance is adequate for your problem.
Citation: F.-S. Meng, S. Shinzato, S. Zhang, K. Matsubara, J.-P. Du, P. Yu, W.-T. Geng, and S. Ogata (2024), "A highly transferable and efficient machine learning interatomic potentials study of α-Fe–C binary system", Acta Materialia, 281, 120408. DOI: 10.1016/j.actamat.2024.120408.
Abstract: 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.
Notes: This entry is for the BNNP potential in the reference that was trained using the n2p2 package. BNNP shows better overall accuracy, and DP shows advantages in the atomic stress computation. These potentials can be used to simulate 𝛼-Fe-C systems and pure 𝛼-Fe systems, but these potentials should not be used for pure C system.
See Computed Properties Notes: These files were provided by Fan-Shun Meng on October 22, 2024. Detailed instructions on using these potentials in MD simulations can be found at the link below. File(s):