Mechanical properties of V-4Ti-4Cr alloy from molecular dynamics with a neural-network potential
Abstract
Machine-learning interatomic potentials enable large-scale atomistic simulations of vanadium alloys relevant to fusion applications, but reliable training and validation remain challenging in multicomponent systems. Here, we develop a descriptor-based DeepMD-DPA1 potential for the V-Ti-Cr system using a two-stage workflow: broad configuration sampling driven by the MatterSim foundation model followed by fine-tuning to density-functional-theory data computed with VASP. The model achieves root-mean-square errors of 9.2 meV/atom for energies and 0.23 eV/angstrom for force components. Using this potential in large-cell LAMMPS simulations, we compute Young's modulus, bulk modulus, and Poisson's ratio for V-4Ti-xCr and V-xTi-4Cr alloys at T = 300 K and T = 1073 K. We find that increasing Cr fraction increases the elastic moduli of the alloy, while increasing Ti fraction decreases them; all compositions are softer at 1073 K than at 300 K. Two-phase simulations give a melting temperature for pure V of 1950 K, in good agreement with experimental data.
Related
- Full-Potential Korringa Kohn Rostoker Study of Structural and Mechanical Properties in SiGeSn Alloys
- Thermo-elastic properties of hydrated epoxy-graphene nanocomposites from ensemble-based molecular dynamics simulations
- Molecular dynamics investigation of the structural and energetic properties of CeO 2 –MO x (M = Gd, La, Ce, Zr) nanoparticles
- Molecular dynamics simulations of DMSO-MeOH liquid mixtures. Effects of force fields on mixing properties
- Foundation Neural-Network Quantum States for Molecular Potential Energy Surfaces in Second Quantization
BibTeX
@misc{demyanov2026mechanical,
title = {{Mechanical properties of V-4Ti-4Cr alloy from molecular dynamics with a neural-network potential}},
author = {G. S. Demyanov and D. V. Minakov and S. B. Saltykov and P. R. Levashov and N. M. Chtchelkatchev},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.08291},
eprint = {2610.08291},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.08291},
}
RIS
TY - UNPB
TI - Mechanical properties of V-4Ti-4Cr alloy from molecular dynamics with a neural-network potential
AU - G. S. Demyanov
AU - D. V. Minakov
AU - S. B. Saltykov
AU - P. R. Levashov
AU - N. M. Chtchelkatchev
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2610.08291
UR - https://arxiv.org/abs/2610.08291
AB - Machine-learning interatomic potentials enable large-scale atomistic simulations of vanadium alloys relevant to fusion applications, but reliable training and validation remain challenging in multicomponent systems. Here, we develop a descriptor-based DeepMD-DPA1 potential for the V-Ti-Cr system using a two-stage workflow: broad configuration sampling driven by the MatterSim foundation model followed by fine-tuning to density-functional-theory data computed with VASP. The model achieves root-mean-square errors of 9.2 meV/atom for energies and 0.23 eV/angstrom for force components. Using this potential in large-cell LAMMPS simulations, we compute Young's modulus, bulk modulus, and Poisson's ratio for V-4Ti-xCr and V-xTi-4Cr alloys at T = 300 K and T = 1073 K. We find that increasing Cr fraction increases the elastic moduli of the alloy, while increasing Ti fraction decreases them; all compositions are softer at 1073 K than at 300 K. Two-phase simulations give a melting temperature for pure V of 1950 K, in good agreement with experimental data.
ER -