Computational modeling and artificial intelligence (AI) have emerged as valuable tools to accelerate this process by simulation and prediction for a wide variety of design factors . Thermal effects on the stability of the different phases, their phase transformations, and equilibrium geometries, among others, are prominent factors hampering the computational design of alloy systems and processes. Nevertheless, progress has been made in recent years, and alloy properties that are nowadays frequently optimized include yield stress, ultimate tensile strength, and oxidation resistance . Due to the complexity of actual alloy processing, the CALPHAD (CALculation of PHAse Diagrams) method , combining experimental data and theoretical results based on thermodynamic Gibbs energy, has been widely utilized to design complicated alloy systems such as solid-solution high entropy alloys. Its Gibbs free energy functions describing an alloy system are indirectly obtained from experiments, with the caveat that probing all possible combinations of alloy elements through experimental measurement is prohibitive. An additional restriction in CALPHAD is that alloy nanoparticle design with a resolution greater than 5 nm is generally not allowed . Recently, mesoscale AI models have been developed to partially alleviate these limitations .