Scalable Autoregressive Deep Surrogates for Dendritic Microstructure Dynamics Article Swipe

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Kaihua Ji , Luning Sun , Shusen Liu , Fei Zhou , Tae Wook Heo ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2511.03884

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.

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Type: preprint
Landing Page: http://arxiv.org/abs/2511.03884
PDF: https://arxiv.org/pdf/2511.03884
OA Status: green
OpenAlex ID: https://openalex.org/W4416022637

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DOI: https://doi.org/10.48550/arxiv.2511.03884

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Title: Scalable Autoregressive Deep Surrogates for Dendritic Microstructure Dynamics

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Type: preprint

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Publication year: 2025

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Publication date: 2025-11-05

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Authors: Kaihua Ji, Luning Sun, Shusen Liu, Fei Zhou, Tae Wook Heo

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Landing page: https://arxiv.org/abs/2511.03884

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PDF URL: https://arxiv.org/pdf/2511.03884

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