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Real-time tracking of structural evolution in 2D MXenes using theory-enhanced machine learning.
Hollenbach, Jonathan D; Pate, Cassandra M; Jia, Haili; Hart, James L; Clancy, Paulette; Taheri, Mitra L.
Afiliación
  • Hollenbach JD; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Pate CM; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Jia H; Department of Chemical and Biomolecular and Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Hart JL; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Clancy P; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Taheri ML; Department of Chemical and Biomolecular and Engineering, Johns Hopkins University, Baltimore, MD, USA.
Sci Rep ; 14(1): 17881, 2024 Aug 02.
Article en En | MEDLINE | ID: mdl-39095485
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 changes, now achievable with EELS and TEM, necessitates innovative analytical frameworks. Here, we introduce a machine learning (ML) framework tailored for the real-time assessment and characterization of in operando EELS Spectrum Images (EELS-SI). We focus on 2D MXenes as the sample material system, specifically targeting the understanding and control of their atomic-scale structural transformations that critically influence their electronic and optical properties. This approach requires fewer labeled training data points than typical deep learning classification methods. By integrating computationally generated structures of MXenes and experimental datasets into a unified latent space using Variational Autoencoders (VAE) in a unique training method, our framework accurately predicts structural evolutions at latencies pertinent to closed-loop processing within the TEM. This study presents a critical advancement in enabling automated, on-the-fly synthesis and characterization, significantly enhancing capabilities for materials discovery and the precision engineering of functional materials at the atomic scale.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido