发表机构
Indian Institute of Science（印度科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出物理信息机器学习框架，结合高斯过程回归与多目标优化，逆向设计高γ'固溶温度且低密度的钴基高温合金，识别关键描述符，生成高性能候选成分。
AI 中文摘要
发现具有更高高温稳定性的下一代钴基高温合金受到巨大的成分设计空间以及控制γ'相稳定性的合金元素间复杂相互作用的阻碍。本研究提出一个物理信息机器学习框架，用于逆向设计具有更高γ'固溶温度并考虑合金密度的钴基高温合金。采用四个描述符，即原子尺寸错配度（δ-MV）、混合焓（Δ-Hm）、电负性错配度（δ-EN）和价电子浓度错配度（δ-VEC），来表征控制相稳定性的化学因素。使用留一法交叉验证评估回归算法，其中高斯过程回归（GPR）表现最佳（R² = 0.932，RMSE = 34.7°C，MAE = 25.9°C）。GPR的概率特性使得能够进行不确定性感知的贝叶斯优化，以探索未知的成分空间。采用非支配排序遗传算法II（NSGA-II）和遗传算法同时提高γ'固溶温度和降低合金密度。优化生成了大量先前未探索的钴基合金成分，预测的γ'固溶温度为1248-1353°C，包括接近或超过实验数据集中最大值的候选合金。描述符分析确定原子尺寸错配度和混合焓是控制γ'稳定性的关键因素，而主成分分析证实所提出的合金占据化学上相关的描述符区域。这种物理信息逆向设计框架为识别高性能钴基高温合金提供了高效途径，并可推广到其他先进结构合金。
英文摘要
The discovery of next-generation Co-based superalloys with improved high-temperature stability is hindered by the vast compositional design space and complex interactions among alloying elements governing gamma-prime phase stability. This study presents a physics-informed machine learning framework for the inverse design of Co-based superalloys with higher gamma-prime solvus temperature while accounting for alloy density. Four descriptors representing atomic size mismatch (delta-MV), mixing enthalpy (Delta-Hm), electronegativity mismatch (delta-EN), and valence electron concentration mismatch (delta-VEC) were used to characterize the chemistry governing phase stability. Regression algorithms were evaluated using leave-one-out cross-validation, with Gaussian Process Regression (GPR) achieving the best performance (R2 = 0.932, RMSE = 34.7 deg C, and MAE = 25.9 deg C). The probabilistic nature of GPR enabled uncertainty-aware Bayesian optimization for exploring the uncharted composition space. Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Genetic Algorithms were used to simultaneously increase gamma-prime solvus temperature and reduce alloy density. The optimization generated numerous previously unexplored Co-based alloy compositions, with predicted gamma-prime solvus temperatures of 1248-1353 deg C, including candidates approaching or exceeding the maximum value in the experimental dataset. Descriptor analysis identified atomic size mismatch and mixing enthalpy as key factors governing gamma-prime stability, while principal component analysis confirmed that the proposed alloys occupy chemically relevant descriptor regions. This physics-informed inverse design framework provides an efficient route for identifying high-performance Co-based superalloys and can be adapted to other advanced structural alloys.
Comments37 pages, 11 figures
