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Elizabeth A. Holm

Sandia National Laboratories
Carnegie Mellon University

232

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4663

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Corrigendum to ‘Learning the grain boundary manifold: tools for visualizing and fitting grain boundary properties’ [Acta Materialia 195 (2020) 1-10]

2021 Acta Materialia
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Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

2021 JOM
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Optimal transportation of grain boundaries: A forward model for predicting migration mechanisms

2021 Acta Materialia
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Optimal transportation of grain boundaries: A forward model for predicting migration mechanisms

2021 Acta Materialia
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Hierarchically Structured Classification of Carbon Nanostructures from TEM Images by Machine Learning and Computer Vision

2021 Microscopy and Microanalysis
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Machine-Learning Microstructure for Inverse Material Design

2021 Advanced Science
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Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data

2021
- Ryan Cohn
- Elizabeth A. Holm
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Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data.

2021 arXiv: Materials Science
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Prediction of Inclusion Types From BSE Images: RF vs. CNN

2021 Frontiers in Materials
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Optimal transportation of grain boundaries: A forward model for predicting migration mechanisms.

2021 arXiv: Materials Science
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