Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

(Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of devices due to its time-intensive nature. To address this, an analytical workflow is introduced for the holistic characterization, modelling, and simulation of device heterostructures. This workflow automates the experimental (S)TEM data analysis, providing an in-depth characterization of crystallographic information, 3D orientation, elemental composition, and strain distribution. It reduces a process that typically takes days for a trained human into an automatic routine solved in minutes. Utilizing a physics-guided artificial intelligence model, it generates representative descriptions of materials and samples. The workflow culminates in creating digital twins of systems limited with at least one axis of translational invariance -3D finite element and atomic models of millions of atoms-enabling simulations that provide crucial insights into device behavior in practical applications. Demonstrated with SiGe planar heterostructures for scalable spin qubits, the workflow links digital twins to theoretical properties, revealing how atomic structure impacts materials and functional properties such as spatially-resolved phononic or electronic characteristics, or (inverse) spin orbit lengths. The versatility of the workflow is demonstrated through its application to a wide array of materials systems, device configurations, and sample morphologies.

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Institutions

Publication Details

Journal
Advanced Materials
Published
2025-10-22
DOI
https://doi.org/10.1002/adma.202506785
Citations
3
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.55

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article

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Georgios Katsaros, Payam Habibzadeh Kavkani, Jordi Arbiol, Catalina Coll et al.
3 citations
Advanced Materials
Machine Learning in Materials Science
1.55
article

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Georgios Katsaros, Payam Habibzadeh Kavkani, Jordi Arbiol, Catalina Coll, Enzo Rotunno, Pablo Ordejón, Peter Krogstrup, Martin Eriksen, Stephan Roche, Yann‐Michel Niquet, Giordano Scappucci, Marc Botifoll, Sara Martí‐Sánchez, Vincenzo Grillo, Andreu Cabot, Ivan Pinto‐Huguet, Thomas Galvani, María Chiara Spadaro, Giovanni Isella, G. Merino
article en
3 citations

Abstract

(Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of devices due to its time-intensive nature. To address this, an analytical workflow is introduced for the holistic characterization, modelling, and simulation of device heterostructures. This workflow automates the experimental (S)TEM data analysis, providing an in-depth characterization of crystallographic information, 3D orientation, elemental composition, and strain distribution. It reduces a process that typically takes days for a trained human into an automatic routine solved in minutes. Utilizing a physics-guided artificial intelligence model, it generates representative descriptions of materials and samples. The workflow culminates in creating digital twins of systems limited with at least one axis of translational invariance -3D finite element and atomic models of millions of atoms-enabling simulations that provide crucial insights into device behavior in practical applications. Demonstrated with SiGe planar heterostructures for scalable spin qubits, the workflow links digital twins to theoretical properties, revealing how atomic structure impacts materials and functional properties such as spatially-resolved phononic or electronic characteristics, or (inverse) spin orbit lengths. The versatility of the workflow is demonstrated through its application to a wide array of materials systems, device configurations, and sample morphologies.

Advanced Materials
Institució Catalana de Recerca i Estudis Avançats (ES), University of Modena and Reggio Emilia (IT), Institute of Science and Technology Austria (AT), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), CEA Grenoble (FR), University of Catania (IT), Istituto Nanoscienze (IT), Institut de Recherche Interdisciplinaire de Grenoble (FR), Institut Català de Nanociència i Nanotecnologia (ES), Centre de Recerca Matemàtica (ES), Institut de Recerca en Energia de Catalunya (ES), QuantumWise (Denmark) (DK), QuTech (NL), Port d'Informació Científica (ES), Institute for Microelectronics and Microsystems (IT), Université Grenoble Alpes (FR), Politecnico di Milano (IT), Delft University of Technology (NL)
Centres de Recerca de Catalunya, Institut Català de Nanociència i Nanotecnologia, European Commission, Generalitat de Catalunya, Universitat Autònoma de Barcelona, Directorate-General for Employment, Social Affairs and Inclusion, Agència de Gestió d'Ajuts Universitaris i de Recerca, European Regional Development Fund, Agencia Estatal de Investigación
Openalex Percentile: Top 16%
Machine Learning in Materials Science
1.55
DOIOpen Access PDFOpenAlex
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