mlsanityData to 7 Oct 2026

Overview: Computer vision and machine learning for microstructural characterization and analysis

arXiv 2005.14260 ↗First posted 28 May 2020cs.CV cond-mat.mtrl-scicited by 22

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Abstract (arXiv)

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 May 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 2; 2025: 6; 2026: 5.

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  1. 2024Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responsescited by 18
  2. 2023Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Reviewcited by 15
  3. 2025Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterizationcited by 8
  4. 2021An End-to-End Computer Vision Methodology for Quantitative Metallographycited by 3
  5. 2022Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectivescited by 2
  6. 2021Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolutioncited by 1
  7. 2022Digital Fingerprinting of Microstructurescited by 1
  8. 2025Deep Learning-Driven Prediction of Microstructure Evolution via Latent Space Interpolationcited by 1
  9. 2025Reward driven discovery of the optimal microstructure representations with invariant variational autoencoderscited by 1
  10. 2026Context Determines Optimal Architecture in Materials Segmentationcited by 1
  11. 2021Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographscited by 0
  12. 2022Computational Discovery of Energy-Efficient Heat Treatment for Microstructure Design using Deep Reinforcement Learningcited by 0
10 more
  1. 2023Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopycited by 0
  2. 2023Procedural Generation of Grain Orientations using the Wave Function Collapse Algorithmcited by 0
  3. 2024A versatile machine learning workflow for high-throughput analysis of supported metal catalyst particlescited by 0
  4. 2025Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forestscited by 0
  5. 20253D variational autoencoder for fingerprinting microstructure volume elementscited by 0
  6. 2025MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillationcited by 0
  7. 2026Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentationcited by 0
  8. 2026Speeding up the annotation process in semantic segmentation industrial applicationscited by 0
  9. 2026Physics as the label for measuring and correcting materials reasoning in multimodal modelscited by 0
  10. 2026Inferring Dislocation Microstructures from X-ray Diffraction via Cross-Modal Contrastive Learningcited by 0