Track synthesis, materials and catalysis as it is published.
Search papers, labs, and topics across Lattice.
A descriptor-based DeepMD-DPA1 potential for V-Ti-Cr alloys was developed via a two-stage workflow, combining broad sampling from a foundation model with fine-tuning to DFT data, achieving low energy and force errors. This validated potential was then used in large-scale LAMMPS simulations to accurately predict the mechanical properties (Young's modulus, bulk modulus, Poisson's ratio) of V-4Ti-xCr and V-xTi-4Cr alloys at different temperatures. The study reveals that increasing Cr content stiffens the alloy, while increasing Ti content softens it, with all compositions exhibiting reduced stiffness at elevated temperatures, and also provides a validated melting temperature for pure V.
A neural-network potential accurately predicts V-Ti-Cr alloy mechanical properties and melting points, revealing distinct compositional effects on stiffness.
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.