Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning

Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning

Abstract In situ Electron Energy Loss Spectroscopy (EELS) combined with Transmission Electron Microscopy (TEM) has traditionally been pivotal for understanding how material processing choices affect local structure and composition. However, the ability to monitor and respond to ultrafast transient c...

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Bibliographic Details
Main Authors: Jonathan D. Hollenbach, Cassandra M. Pate, Haili Jia, James L. Hart, Paulette Clancy, Mitra L. Taheri
Format: Article
Language:English
Published: Nature Portfolio 2024-08-01
Series:Scientific Reports
Subjects:
Electron energy-loss spectroscopy
Transmission electron microscopy
Machine learning
Variational autoencoders
Hyperspectral
Theory
Online Access:https://doi.org/10.1038/s41598-024-66902-4

Internet

https://doi.org/10.1038/s41598-024-66902-4

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