13 citations · 37 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Illustrating an Effective Workflow for Accelerated Materials Discovery
Mrinalini Mulukutla, A. Nicole Person, Sven Voigt +26
Algorithmic materials discovery is a multi-disciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, c…
Computer Vision Approach to Study Deformation of Materials
Chaoyi Zhu, Haoren Wang, Kevin Kaufmann +1
Characterization of the deformation of materials across different length scales has continuously attracted enormous attention from the mechanics and materials communities. In this…