1.2k citations · 1.3k across the 5 of their papers we have counts for
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Recent Advances and Applications of Deep Learning Methods in Materials Science
Kamal Choudhary, Brian DeCost, Chi Chen +10
Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data mo…
Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution
Ryan Cohn, Elizabeth Holm
Recent developments in graph neural networks show promise for predicting the occurrence of abnormal grain growth, which has been a particularly challenging area of research due to…
A taxonomy of grain boundary migration mechanisms via displacement texture characterization
Ian Chesser, Brandon Runnels, Elizabeth Holm
Atomistic simulations provide the most detailed picture of grain boundary (GB) migration currently available. Nevertheless, extracting unit mechanisms from atomistic simulation dat…
Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data
Ryan Cohn, Iver Anderson, Tim Prost +3
We propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individ…
Unsupervised machine learning via transfer learning and k-means clustering to classify materials image data
Ryan Cohn, Elizabeth Holm
Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled data sets and for achieving maximum machine learning performance. This paper…