Using Nearest-Neighbor Distributions to Quantify Machine Learning of Materials’ Microstructures

Using Nearest-Neighbor Distributions to Quantify Machine Learning of Materials’ Microstructures

Machine learning strategies for the semantic segmentation of materials’ micrographs, such as U-Net, have been employed in recent years to enable the automated identification of grain-boundary networks in polycrystals. For example, most recently, this architecture has allowed researchers to address t...

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Bibliographic Details
Main Authors: Jeffrey M. Rickman, Katayun Barmak, Matthew J. Patrick, Godfred Adomako Mensah
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Entropy
Subjects:
microstructural segmentation
convolutional network
neighbor distribution
Online Access:https://www.mdpi.com/1099-4300/27/5/536
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