Physics-guided inverse design of Co-based superalloys using machine learning and multi-objective optimization for enhanced $γ'$ solvus temperature

Abstract

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.