Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis

Abstract

Achieving targeted microstructures through composition design is a core challenge in developing structural materials for high-performance applications. This study introduces a multiscale Integrated Computational Materials Engineering (ICME) framework that combines CALPHAD-based thermodynamic modeling, machine learning, molecular dynamics, and diffusion kinetics to link alloy chemistry to microstructural evolution. Machine learning models trained on 750,000 CALPHAD-derived datapoints enabled rapid screening of two billion compositions based on thermodynamic criteria. An advanced screening step incorporated nanoscale physical descriptors that capture mechanisms governing precipitate coarsening and dynamic recrystallization. Applied to wrought Ni-based superalloys, the framework identified twelve compositions predicted to form fine intragranular γ′ precipitates within coarse γ grains; one was experimentally validated, with microscopy confirming the predicted microstructure. While demonstrated for Ni-based systems, the methodology is broadly generalizable. This work highlights the power of integrating high-throughput composition screening with atomistic-scale evaluation to accelerate microstructure-driven materials design beyond equilibrium thermodynamics.