A data-driven surrogate model to rapidly predict microstructure morphology during physical vapor deposition

Here, we present a surrogate model that rapidly predicts the microstructures of a binary-alloy thin film during physical vapor deposition. This surrogate model is constructed and trained from a data set produced by phase-field simulations of physical vapor deposition. It relies on a statistical representation of the microstructure, principal component analysis, polynomial chaos expansion, and a microstructure-reconstruction algorithm to estimate the microstructure as a function of the deposition parameters and properties of the materials being deposited. This protocol, exercised on a simplified physical vapor deposition model, demonstrates the efficacy of the surrogate model to rapidly predict a broad class of microstructures as a function of deposition conditions with good accuracy relative to high-fidelity models. The considerable computational gain from the surrogate model compared to the detailed phase-field approach highlights the importance of pursuing such approaches, especially when used for producing parameter-microstructure maps for rapid and accurate predictions of the microstructure. As such, this surrogate model can be used to guide the choice of deposition conditions and materials being deposited to fabricate functional thin films with targeted microstructures.