AI 中文总结
该研究开发结合分位数梯度提升与Born稳定性约束的物理信息机器学习框架，预测难熔高熵合金的力学性能，筛选出高性能候选合金并确立定量设计规则，可加速难熔合金的发现。
AI 中文摘要
难熔高熵合金（CCAs）在高温下展现出优异的力学性能，但其庞大的成分空间（约10^15种可能组合）给实验探索带来了巨大挑战。我们开发了一种物理信息机器学习框架，将分位数梯度提升与Born稳定性约束相结合，用于预测难熔高熵合金的8项力学性能：体模量B、剪切模量G、维氏硬度Hv、弹性常数C11、C12、C44、杨氏模量E及泊松比ν。基于10元素（Cr、Hf、Mo、Nb、Re、Ta、Ti、V、W、Zr）设计空间中经密度泛函理论（DFT）计算得到的393种合金，我们在留出的测试集（n=59）上，对6项性能的决定系数（R²）达到0.89至0.97，平均绝对误差为0.85至14.74 GPa。值得注意的是，所有测试预测均满足Born稳定性准则（100%符合），证明了物理信息约束的有效性。在价电子浓度（VEC）-原子尺寸错配（δ）空间中进行成分筛选，识别出高性能候选合金，其中MoReW展现出最高的预测剪切刚度（C44=139 GPa）。元素分析显示，铼出现在100%的顶级性能合金中，钼占90%，铬占80%，由此确立了定量设计规则：VEC为6.0至6.4，δ<10%，且Re-Mo-Cr三元体系可最大化剪切阻力。该物理信息筛选框架能从万亿级成分空间中加速发现力学稳定、高性能的难熔合金。
英文摘要
Refractory complex concentrated alloys (CCAs) exhibit exceptional mechanical properties at elevated temperatures, but their vast compositional space (approximately 10^15 possible combinations) poses significant challenges for experimental exploration. We develop a physics-informed machine learning framework combining quantile gradient boosting with Born stability constraints to predict eight mechanical properties (bulk modulus B, shear modulus G, Vickers hardness Hv, elastic constants C11, C12, C44, Young's modulus E, and Poisson's ratio nu) of refractory CCAs. Using 393 density functional theory (DFT)-computed alloys from a 10-element design space (Cr, Hf, Mo, Nb, Re, Ta, Ti, V, W, Zr), we achieve coefficient of determination (R^2) values of 0.89 to 0.97 with mean absolute errors of 0.85 to 14.74 GPa across six properties on a held-out test set (n = 59). Remarkably, all test predictions satisfy the Born stability criteria (100% compliance), demonstrating the efficacy of physics-informed constraints. Compositional screening in valence electron concentration (VEC)-atomic size mismatch (delta) space identifies high-performance candidates, with MoReW exhibiting the highest predicted shear rigidity (C44 = 139 GPa). Elemental analysis reveals that rhenium appears in 100% of the top-performing alloys, molybdenum in 90%, and chromium in 80%, establishing quantitative design rules: VEC = 6.0 to 6.4, delta < 10%, and Re-Mo-Cr ternary systems maximize shear resistance. This physics-informed screening framework enables accelerated discovery of mechanically stable, high-performance refractory alloys from the trillion-scale compositional space.
Comments27 Pages, 4 figures
