发表机构
Joint Institute for High Temperatures; Moscow Institute of Physics and Technology; Institute for High Pressure Physics; Joint Institute for Nuclear Research（高温联合研究所; 莫斯科物理技术学院; 高压物理研究所; 苏联核子联合研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发了V-Ti-Cr系统的DeepMD-DPA1神经网络势，通过分子动力学计算了V-4Ti-xCr和V-xTi-4Cr合金的弹性模量，发现Cr增加模量而Ti降低模量，且高温下更软，熔点与实验吻合。
AI 中文摘要
机器学习原子间势能使得对聚变应用相关的钒合金进行大规模原子模拟成为可能，但在多组分系统中，可靠的训练和验证仍然具有挑战性。在此，我们采用两阶段工作流程，为V-Ti-Cr系统开发了一种基于描述符的DeepMD-DPA1势能：首先由MatterSim基础模型驱动广泛的构型采样，随后针对使用VASP计算的密度泛函理论数据进行微调。该模型在能量方面实现了9.2 meV/原子的均方根误差，在力分量方面实现了0.23 eV/埃的均方根误差。利用该势能在大型晶胞LAMMPS模拟中，我们计算了V-4Ti-xCr和V-xTi-4Cr合金在T = 300 K和T = 1073 K下的杨氏模量、体积模量和泊松比。我们发现，增加Cr含量会提高合金的弹性模量，而增加Ti含量则会降低弹性模量；所有成分在1073 K下均比在300 K下更软。两相模拟得出纯V的熔点为1950 K，与实验数据吻合良好。
英文摘要
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.
Comments7 pages, 5 figures, 1 table
