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Scientific Reports|December 5, 2024
Graph convolutional network for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolutionRyan Cohn, Elizabeth A HolmData in Brief|September 20, 2018
A dataset of synthetic face centered cubic 3D polycrystalline microstructures, grain-wise microstructural descriptors and grain averaged stress fields under uniaxial tensile deformationAnkita Mangal, Elizabeth A HolmData in Brief|December 7, 2018
A dataset of synthetic hexagonal close packed 3D polycrystalline microstructures, grain-wise microstructural descriptors and grain averaged stress fields under uniaxial tensile deformation for two sets of constitutive parametersAnkita Mangal, Elizabeth A HolmScience (New York, N.Y.)|May 29, 2010
How grain growth stops: a mechanism for grain-growth stagnation in pure materialsElizabeth A Holm, Stephen M FoilesData in Brief|June 2, 2018
Corrigendum to "A large dataset of synthetic SEM images of powder materials and their ground truth 3D structures" [Data Brief 9 (2016) 727-731]Brian L DeCost, Elizabeth A HolmData in Brief|November 11, 2016
A large dataset of synthetic SEM images of powder materials and their ground truth 3D structuresBrian L DeCost, Elizabeth A HolmNanoscale Advances|September 22, 2022
A transfer learning approach for improved classification of carbon nanomaterials from TEM imagesQixiang Luo, Elizabeth A Holm, Chen WangMicroscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada|March 15, 2019
High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon SteelBrian L DeCost, Bo Lei, Toby Francis, et al.Nature Materials|January 10, 2006
Computing the mobility of grain boundariesKoenraad G F Janssens, David Olmsted, Elizabeth A Holm, et al.Nature Communications|November 2, 2021
A deep learning approach for complex microstructure inferenceAli Riza Durmaz, Martin Müller, Bo Lei, et al.Pageof 2