Litcius/Paper detail

Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images

Shizhao Lu, Brian Montz, Todd Emrick, Arthi Jayaraman

2022Digital Discovery29 citationsDOIOpen Access PDF

Abstract

Semi-supervised transfer learning workflow facilitates rapid, automated nanomaterial morphology classification for small image datasets. Self-supervised training enables label-free pretraining that minimizes drawbacks of manual labeling.

Topics & Concepts

Transmission electron microscopyWorkflowNanowireElectron microscopeComputer scienceMaterials scienceNanotechnologyTransmission (telecommunications)Artificial intelligencePhysicsOpticsDatabaseTelecommunicationsMachine Learning in Materials ScienceAdvanced Electron Microscopy Techniques and ApplicationsElectron and X-Ray Spectroscopy Techniques
Semi-supervised machine learning workflow for analysis of nanowire morphologies from transmission electron microscopy images | Litcius