# Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features

arXiv: https://arxiv.org/abs/2501.18064 · HTML: https://mlsanity.com/paper/2501.18064/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 118 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Unsupervised Semantic Segmentation by Distilling Feature Correspondences](https://mlsanity.com/paper/2203.08414/) (2022)
- [Stabilizing Generative Adversarial Networks: A Survey](https://mlsanity.com/paper/1910.00927/) (2019)
- [Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping](https://mlsanity.com/paper/2002.11434/) (2020)
- [Paradigm shift in electron-based crystallography via machine learning](https://mlsanity.com/paper/1902.03682/) (2019)
- [Beta-VAE Reproducibility: Challenges and Extensions](https://mlsanity.com/paper/2112.14278/) (2021)
- [GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure](https://mlsanity.com/paper/2401.03661/) (2024)
- [Physics and Chemistry from Parsimonious Representations: Image Analysis via Invariant Variational Autoencoders](https://mlsanity.com/paper/2303.18236/) (2023)
- [Q-RBSA: High-Resolution 3D EBSD Map Generation Using An Efficient Quaternion Transformer Network](https://mlsanity.com/paper/2303.10722/) (2023)
- [Unsupervised Multimodal Fusion of In-process Sensor Data for Advanced Manufacturing Process Monitoring](https://mlsanity.com/paper/2410.22558/) (2024)

## Built on by (most cited first, 5 of 5)

- [Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders](https://mlsanity.com/paper/2510.00243/) (2025)
- [Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders](https://mlsanity.com/paper/2606.14999/) (2026)
- [Reliable End-to-End Material Information Extraction from the Literature with Source-Tracked Multi-Stage Large Language Models](https://mlsanity.com/paper/2510.05142/) (2025)
- [AI-Driven Phase Identification from X-ray Hyperspectral Imaging of cycled Na-ion Cathode Materials](https://mlsanity.com/paper/2603.07666/) (2026)
- [PATHFINDER: Multi-objective discovery in structural and spectral spaces](https://mlsanity.com/paper/2604.04194/) (2026)

## Abstract (arXiv)

To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors. This need is particularly relevant for metallic materials processed through additive manufacturing, which exhibit complex hierarchical microstructures that cannot be adequately described using the conventional metrics typically applied to wrought materials. Furthermore, capturing the spatial heterogeneity of microstructures at the different scales is necessary within such framework to accurately predict their properties. To address these challenges, we propose the physical spatial mapping of metal diffraction latent space features. This approach integrates (i) point diffraction data encoding via variational autoencoders or contrastive learning and (ii) the physical mapping of the encoded values. Together these steps offer a method offers a novel means to comprehensively describe metal microstructures. We demonstrate this approach on a wrought and additively manufactured alloy, showing that it effectively encodes microstructural information and enables direct identification of microstructural heterogeneity not directly possible by physics-based models. This data-reduced microstructure representation opens the application of machine learning models in accelerating metallic material design and accurately predicting their properties.