Journal
PHYSICAL REVIEW E
Volume 96, Issue 5, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevE.96.052111
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Funding
- Laboratory Directed Research and Development (LDRD) DR [20140013DR]
- Center for Nonlinear Studies (CNLS) at the Los Alamos National Laboratory (LANL)
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We apply recent advances in machine learning and computer vision to a central problem in materials informatics: the statistical representation of microstructural images. We use activations in a pretrained convolutional neural network to provide a high-dimensional characterization of a set of synthetic microstructural images. Next, we usemanifold learning to obtain a low-dimensional embedding of this statistical characterization. We show that the low-dimensional embedding extracts the parameters used to generate the images. According to a variety of metrics, the convolutional neural network method yields dramatically better embeddings than the analogous method derived from two-point correlations alone.
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