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Deep learning-based image classification for microstructural analysis in computational materials science
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DOI：10.3389/fmats.2025.1648653.png)
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Abstract
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IntroductionRecently; the integration of deep learning techniques and computational materials science has catalyzed significant advances in the microstructural analysis of materials; particularly through the lens of multiscale; high-dimensional imaging data. However; conventional models often fall short in capturing the intricate topology and spatial variability that define realistic microstructural patterns; limiting their ability to inform material property predictions; inverse design; and structural synthesis.MethodsTo overcome these challenges; we introduce an innovative deep learning framework designed for microstructural image classification and representation learning; incorporating physical; geometric; and topological constraints directly into the training process. Our method; centered on the structured generative model MorphoTensor; introduces hierarchical tensorial embeddings that retain directionality; anisotropy; and spatial locality—features crucial for realistic material modeling. We further incorporate a Topology-Aware Latent Refinement strategy; which couples persistent homology with differentiable approximations of Betti numbers to enforce topological consistency and augment microstructural diversity. Unlike existing data-driven pipelines; our framework seamlessly integrates statistical encoding; topologicalization; and latent manifold alignment within a unified architecture; ensuring robustness across diverse datasets including phase-field simulations and real microscopy data.Results and DiscussionEmpirical evaluations on benchmark and experimental datasets demonstrate that our method significantly outperforms standard convolutional and autoencoding baselines in accuracy; stability; and generalization. Moreover; our approach aligns closely with the ongoing efforts in the broader computational materials and mechanics communities to build interpretable; physically informed; and adaptable deep learning systems. These contributions illustrate the potential of structured deep generative modeling as a foundational tool for advancing intelligent microstructure analysis and design in materials informatics.
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