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.

doi:10.1038/s41598-024-66902-4

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

Journal Article · Fri Aug 02 04:00:00 EDT 2024 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-024-66902-4· OSTI ID:2439101

Hollenbach, Jonathan D. ^[1]; Pate, Cassandra M. ^[1]; Jia, Haili ^[1]; Hart, James L. ^[1]; Clancy, Paulette ^[1]; Taheri, Mitra L. ^[2]

Johns Hopkins Univ., Baltimore, MD (United States)
Johns Hopkins Univ., Baltimore, MD (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES); US Department of the Navy, Office of Naval Research (ONR); US Air Force Office of Scientific Research (AFOSR); National Science Foundation (NSF)

Grant/Contract Number:: AC05-76RL01830; SC0020314

OSTI ID:: 2439101

Report Number(s):: PNNL-SA--200076

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 14; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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