AI 中文总结
本研究开发了一种排列不变性机器学习框架，可高效预测面心立方高熵合金中不同变形及化学环境下的空位形成能，为多尺度合金模型提供了高效途径。
AI 中文摘要
空位形成能决定了高熵合金（HEAs）的扩散、辐照损伤、相稳定性及动态失效，然而其对局部化学环境和机械变形的强依赖性，使得原子级计算在大规模研究中成本过高。本文中，我们开发了一种基于原子信息的排列不变性机器学习框架，用于从局部原子环境预测面心立方（FCC）高熵合金中与应变相关的空位形成能。该模型采用以空位为中心的表示，由客观几何描述符结合局部变形梯度的不变量构成，能够在统一框架中学习化学无序与有限变形的耦合效应。原子级模拟显示，体积变形是控制空位形成能平均变化的主导因素，而剪切变形的影响相对较小。同时，在相同宏观载荷下仍存在显著的位点间变异性，表明局部化学环境决定了空位能量学的统计分布，超出了物种平均趋势的范畴。所提出的框架可准确预测不同变形状态下的空位形成能，同时评估速度比直接原子级模拟快数个数量级。这些结果为将应力相关的缺陷能量学纳入化学复杂合金的扩散、辐照损伤及动态失效的多尺度模型提供了高效途径。
英文摘要
Vacancy formation energies govern diffusion, irradiation damage, phase stability, and dynamic failure in high-entropy alloys (HEAs), yet their strong dependence on local chemical environments and mechanical deformation makes atomistic calculations prohibitively expensive for large-scale studies. Here, we develop an atomistically informed permutation invariant machine learning framework for predicting strain dependent vacancy formation energies in FCC HEAs from local atomic environments. The model employs a vacancy-centered representation constructed from objective geometric descriptors together with invariants of the local deformation gradient, enabling the coupled effects of chemical disorder and finite deformation to be learned within a unified framework. Atomistic simulations reveal that volumetric deformation is the dominant factor controlling the average variation in vacancy formation energy, whereas shear deformation has a comparatively minor influence. At the same time, substantial site to site variability persists under identical macroscopic loading, demonstrating that local chemical environments govern the statistical distribution of vacancy energetics beyond species-averaged trends. The proposed framework accurately predicts vacancy formation energies across diverse deformation states while providing orders-of-magnitude faster evaluation than direct atomistic simulations. These results establish an efficient route for incorporating stress-dependent defect energetics into multiscale models of diffusion, irradiation damage, and dynamic failure in chemically complex alloys.
