Deep Learning for Accelerated Long-Horizon Forecasting of Multicomponent Multiphase Microstructure Evolution in High-Entropy Alloys

2026-07-30

AI Summary

This work addresses the challenge of efficiently simulating microstructural evolution in multicomponent high-entropy alloys over long timescales and large spatial domains using conventional phase-field methods. The authors propose an AE-GCN-LSTM surrogate model that, for the first time, integrates a multi-head autoencoder with graph convolutional networks (GCNs) to compress concentration fields and order parameters into latent graph representations, while leveraging LSTM networks to capture their spatiotemporal dynamics. Without requiring retraining, the model generalizes across varying system sizes, alloy compositions, and complex phase transformation scenarios. It successfully predicts microstructural evolution up to 3 million time steps with high accuracy on unseen configurations, achieving computational speedups of 7,200–62,300×, thereby substantially extending the accessible simulation scale and applicability of phase-field modeling.

Abstract

Phase-field modeling provides a powerful approach for predicting microstructure evolution but becomes computationally prohibitive for multicomponent and multiphase systems over large spatial and temporal scales. This work presents an AE-GCN-LSTM surrogate framework for long-horizon forecasting of microstructure evolution in the multicomponent AlCrFeNi high-entropy alloy system containing coexisting BCC and FCC phases. A multi-head autoencoder compresses the four elemental concentration fields and phase-field order parameter into latent representations, which are formulated as graphs for learning their spatial and temporal evolution. The framework accurately forecasts microstructure evolution over horizons extending to 3,000,000 simulation timesteps. Its robustness is systematically evaluated under previously unseen conditions without retraining, fine-tuning, or parameter adaptation. These evaluations include variations in FCC precipitate size and initial position, microstructures containing one, two, and five FCC precipitates, and complex phase interactions involving precipitate merging and splitting. Although trained only on 100 x 100 computational domains containing a single nominal alloy composition, the framework is successfully transferred to larger 256 x 256 and 512 x 512 systems and to previously unseen AlCrFeNi compositions. Across the evaluated configurations, the model preserves the dominant phase morphology and compositional evolution while providing computational speedups ranging from approximately 7200 to 62300 relative to conventional phase-field simulations. These results demonstrate that latent graph-based AE-GCN-LSTM forecasting provides a scalable and computationally efficient surrogate for long-horizon simulation of multicomponent, multiphase microstructures and offers a promising foundation for high-throughput alloy design.

Problem

• microstructure evolution
• high-entropy alloys
• long-horizon forecasting
• multicomponent multiphase systems
• phase-field modeling

Innovation

• AE-GCN-LSTM
• phase-field surrogate
• graph-based forecasting
• high-entropy alloys
• long-horizon microstructure prediction