Deep learning-based image classification for microstructural analysis in computational materials science - Liu, Haiyan; Zhu, Penghua; Tan, Chenyu (Frontiers in Materials, 2026)
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.