3 papers
cond-mat.mtrl-sci2026
Interpretable Material Spatial Intelligence for Discovery of Governing Microstructural Features
Mathieu Calvat, Gregory Sparks, Dhruv Anjaria +9
Many material systems exhibit complex spatial and temporal interactions across multiple length scales and modalities that govern macroscopic behavior. Although Machine Learning (ML…
cond-mat.mtrl-sci2026
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features
Mathieu Calvat, Chris Bean, Dhruv Anjaria +4
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…
cond-mat.mtrl-sci2025
Plasticity Encoding and Mapping during Elementary Loading for Accelerated Mechanical Properties Prediction
Mathieu Calvat, Chris Bean, Dhruv Anjaria +3
Encoding metal plasticity captured from high-resolution digital image correlation (DIC) can be leveraged to predict a wide range of monotonic and cyclic macroscopic properties of m…