Inverse process design for microstructure engineering in FePt-X nanocomposite thin films: phase-field simulations and machine learning

Abstract

FePt-X nanocomposite thin film (X is a non-magnetic segregant) is a critical enabling material for ultrahigh-capacity magnetic recording, but a time- and cost-efficient strategy to identify process parameters that yield desirable microstructure features has remained scarce. Here, we present a computational framework, which employs high-throughput phase-field simulations for the forward prediction of process-microstructure relationship and integrates automated microstructure quantification and active learning for the inverse identification of process parameters. As a proof of concept, the framework is used to inversely identify the gradient energy coefficients that lead to microstructure features matching experimental observation in FePt-Al2O3 films. The identified parameters result in a closer agreement with experiments than expert best guesses. Notably, the framework yields a tenfold reduction in computational cost compared with brute-force high-throughput phase-field simulations. Our results demonstrate the potential of integrating phase-field simulations with active learning to significantly accelerate inverse process design for microstructure engineering, compared with using phase-field simulations alone.