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Gesch et al. J. Mater. Inf. 2025, 5, 42 Journal of
DOI: 10.20517/jmi.2025.23
Materials Informatics

Research Article Open Access

Accelerating phase-field simulation of coupled
microstructural evolution using autoencoder-based

recurrent neural networks

Aidan Gesch , Chongze Hu *
Department of Aerospace Engineering and Mechanics, The University of Alabama, Tuscaloosa, AL 35487, USA.

* Correspondence to: Prof. Chongze Hu, Department of Aerospace Engineering and Mechanics, The University of Alabama, 251
Shelby Ln, Tuscaloosa, AL 35487, USA. E-mail: hucz@ua.edu

How to cite this article: Gesch, A.; Hu, C. Accelerating phase-field simulation of coupled microstructural evolution using
autoencoder-based recurrent neural networks. J. Mater. Inf. 2025, 5, 42. https://dx.doi.org/10.20517/jmi.2025.23

Received: 7 Apr 2025 First Decision: 7 May 2025 Revised: 20 May 2025 Accepted: 26 May 2025 Published: 25 Jun 2025

Academic Editors: Ming Hu, Xiang-Dong Ding Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
Accelerated phase-field frameworks leveraging time-dependent neural networks have recently been developed to
accelerate microstructure-based phase-field simulations in both temporal and spatial domains. However, most of
these frameworks have been designed for phase-field problems involving a single variable field, such as spinodal
decomposition. In this study, we developed an accelerated framework for predicting the microstructural evolution
of Ostwald ripening, a classical phase-field problem involving multiple interdependent parameter fields. This
framework integrates various components: high-throughput phase-field simulations for generating high-quality
microstructure database, autoencoder-based dimensionality reduction to transform 2D microstructure images into
latent representations, and long short-term memory (LSTM) networks serving as the microstructure learning
engine. Our results demonstrate that autoencoder techniques can effectively reduce the large dimension of
microstructure images into 16 key values, while maintaining high accuracy in reconstructing these reduced
representations back to their original space. Using these latent representations, LSTM models are employed to
capture the key microstructural features of Ostwald ripening and predict their evolution over future time
5
sequences, with a speedup of approximately 3.35 × 10 times compared to the high-fidelity phase-field simulations.
The accelerated framework presented in this work is the first data-driven emulation specifically designed for
coupled phase-field problems, and it can be easily extended to predict other evolutionary phenomena with more
complex microstructural features.

Keywords: Accelerated phase-field framework, microstructural evolution, recurrent neural networks,
dimensionality reduction, Ostwald ripening

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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