MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction

• URL: http://arxiv.org/abs/2511.08955v2
• Date: Tue, 18 Nov 2025 06:31:29 GMT
• Title: MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction
• Authors: Qinyi Zhang, Duanyu Feng, Ronghui Han, Yangshuai Wang, Hao Wang,
• Abstract summary: We introduce MicroEvoEval, the first comprehensive benchmark for image-based evolution prediction.<n>We evaluate 14 models, encompassing both domain-specific and general-purpose architectures, across four representative MicroEvo tasks.
• Score: 5.114987417721577
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Simulating microstructure evolution (MicroEvo) is vital for materials design but demands high numerical accuracy, efficiency, and physical fidelity. Although recent studies on deep learning (DL) offer a promising alternative to traditional solvers, the field lacks standardized benchmarks. Existing studies are flawed due to a lack of comparing specialized MicroEvo DL models with state-of-the-art spatio-temporal architectures, an overemphasis on numerical accuracy over physical fidelity, and a failure to analyze error propagation over time. To address these gaps, we introduce MicroEvoEval, the first comprehensive benchmark for image-based microstructure evolution prediction. We evaluate 14 models, encompassing both domain-specific and general-purpose architectures, across four representative MicroEvo tasks with datasets specifically structured for both short- and long-term assessment. Our multi-faceted evaluation framework goes beyond numerical accuracy and computational cost, incorporating a curated set of structure-preserving metrics to assess physical fidelity. Our extensive evaluations yield several key insights. Notably, we find that modern architectures (e.g., VMamba), not only achieve superior long-term stability and physical fidelity but also operate with an order-of-magnitude greater computational efficiency. The results highlight the necessity of holistic evaluation and identify these modern architectures as a highly promising direction for developing efficient and reliable surrogate models in data-driven materials science.

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