Scalable Autoregressive Deep Surrogates for Dendritic Microstructure Dynamics

TL;DR

This paper presents a machine-learning framework with autoregressive deep surrogates that significantly accelerates dendritic microstructure simulations, enabling scalable and accurate predictions for materials design.

Contribution

It introduces a novel deep surrogate model trained on phase-field simulations, achieving over 100x speed-up in predicting dendritic microstructure evolution.

Findings

Surrogates predict dendritic growth with high accuracy.

Achieved over 100x computational speed-up.

Validated on alloy solidification scenarios.

Abstract

Microstructural pattern formation, such as dendrite growth, occurs widely in materials and energy systems, significantly influencing material properties and functional performance. While the phase-field method has emerged as a powerful computational tool for modeling microstructure dynamics, its high computational cost limits its integration into practical materials design workflows. Here, we introduce a machine-learning framework using autoregressive deep surrogates trained on short trajectories from quantitative phase-field simulations of alloy solidification in limited spatial domains. Once trained, these surrogates accurately predict dendritic evolution at scalable length and time scales, achieving a speed-up of more than two orders of magnitude. Demonstrations in isothermal growth and in directional solidification of a dilute Al-Cu alloy validate their ability to predict microstructure evolution. Quantitative comparisons with phase-field benchmarks further show excellent agreement in the tip-selection constant, morphological symmetry, and primary spacing evolution.