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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.03884 |
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Table of Contents:
- 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.