基于机器学习势的Cu-Cr合金单轴拉伸变形机制的分子动力学模拟
Molecular Dynamics Simulation of the Uniaxial Tensile Deformation Mechanism of Cu-Cr Alloy Based on Machine Learning Potential
摘要: 针对铜铬合金分子动力学传统经验势精度不足及第一性原理计算尺度受限的问题,文章基于十六元高熵合金神经演化势(UNEP-v1)框架,通过计算状态方程、声子色散、弹性常数及应力–应变响应,验证了该势函数的精度接近密度泛函理论参考值。在此基础上,沿[010]晶向对具有不同Cr含量的Cu-Cr合金进行了大规模单轴拉伸分子动力学模拟。结果表明,随着Cr含量的增加,合金的弹性模量与屈服强度显著提升。微观结构演化分析揭示,在塑性变形阶段,合金通过FCC相向BCC的局部相变来释放弹性应变能,且变形主要由1/6 [112]型肖克利不全位错的形核与滑移主导。Cr原子的引入显著钉扎并限制了位错的运动,从而大幅提高了驱动塑性变形所需的临界应力。研究不仅为Cu-Cr体系的大尺度原子模拟提供了可靠的势函数,还从原子尺度深刻阐明了Cr的固溶强化机制,为理解Cu-Cr合金的固溶强化阶段提供了基础物理图像。
Abstract: To address the insufficient accuracy of traditional empirical potentials in molecular dynamics simulations of Cu-Cr alloys and the spatiotemporal limitations of first-principles calculations, this study utilizes the sixteen-element high-entropy alloy neuroevolution potential (UNEP-v1) framework. By systematically calculating the equation of state, phonon spectrum, elastic constants, and stress-strain responses, the high consistency of the developed potential with density functional theory (DFT) reference values is rigorously validated. On this basis, large-scale uniaxial tensile molecular dynamics simulations are conducted on Cu-Cr alloys with various Cr concentrations along the [010] crystallographic direction. The results indicate that as the Cr concentration increases, both the elastic modulus and yield strength of the alloys are significantly enhanced. Microstructural evolution analyses reveal that during plastic deformation, the alloy releases elastic strain energy via local phase transformations from the FCC phase to a BCC phase, with the deformation predominantly governed by the nucleation and glide of 1/6 [112]-type Shockley partial dislocations. The introduction of Cr atoms effectively pins and restricts dislocation motion, thereby substantially increasing the critical stress required to drive plastic deformation. This study not only provides a reliable interatomic potential for large-scale atomistic simulations of Cu-Cr systems but also profoundly elucidates the solid solution strengthening mechanism of Cr at the atomic scale, providing a fundamental physical picture for understanding the solid solution strengthening stage of Cu-Cr alloys.
文章引用:王月昊. 基于机器学习势的Cu-Cr合金单轴拉伸变形机制的分子动力学模拟[J]. 材料科学, 2026, 16(7): 52-61. https://doi.org/10.12677/ms.2026.167155

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