article epub_ahead yes Marc Botifoll author Ivan Pinto-Huguet author Enzo Rotunno author Thomas Galvani author Catalina Coll author Payam Habibzadeh Kavkani author Maria Chiara Spadaro author Yann Michel Niquet author Martin Børstad Eriksen author Sara Martí-Sánchez author Georgios Katsaros author 38DB5788-F248-11E8-B48F-1D18A9856A870000-0001-8342-202X Giordano Scappucci author Peter Krogstrup author Giovanni Isella author Andreu Cabot author Gonzalo Merino author Pablo Ordejón author Stephan Roche author Vincenzo Grillo author Jordi Arbiol author GeKa department (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. https://creativecommons.org/licenses/by-nc-nd/4.0/ Wiley2025 eng 0935-9648 1521-4095 2411.01024 00159742840000110.1002/adma.202506785 Botifoll, M., Pinto-Huguet, I., Rotunno, E., Galvani, T., Coll, C., Kavkani, P. H., … Arbiol, J. (2025). Artificial intelligence-assisted workflow for transmission electron microscopy: From data analysis automation to materials knowledge unveiling. <i>Advanced Materials</i>. Wiley. <a href="https://doi.org/10.1002/adma.202506785">https://doi.org/10.1002/adma.202506785</a> Botifoll M, Pinto-Huguet I, Rotunno E, Galvani T, Coll C, Kavkani PH, Spadaro MC, Niquet YM, Eriksen MB, Martí-Sánchez S, Katsaros G, Scappucci G, Krogstrup P, Isella G, Cabot A, Merino G, Ordejón P, Roche S, Grillo V, Arbiol J. 2025. Artificial intelligence-assisted workflow for transmission electron microscopy: From data analysis automation to materials knowledge unveiling. Advanced Materials., e06785. Botifoll, Marc, Ivan Pinto-Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, et al. “Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling.” <i>Advanced Materials</i>. Wiley, 2025. <a href="https://doi.org/10.1002/adma.202506785">https://doi.org/10.1002/adma.202506785</a>. M. Botifoll, I. Pinto-Huguet, E. Rotunno, T. Galvani, C. Coll, P.H. Kavkani, M.C. Spadaro, Y.M. Niquet, M.B. Eriksen, S. Martí-Sánchez, G. Katsaros, G. Scappucci, P. Krogstrup, G. Isella, A. Cabot, G. Merino, P. Ordejón, S. Roche, V. Grillo, J. Arbiol, Advanced Materials (2025). M. Botifoll <i>et al.</i>, “Artificial intelligence-assisted workflow for transmission electron microscopy: From data analysis automation to materials knowledge unveiling,” <i>Advanced Materials</i>. Wiley, 2025. Botifoll, Marc, et al. “Artificial Intelligence-Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling.” <i>Advanced Materials</i>, e06785, Wiley, 2025, doi:<a href="https://doi.org/10.1002/adma.202506785">10.1002/adma.202506785</a>. Botifoll M, Pinto-Huguet I, Rotunno E, et al. Artificial intelligence-assisted workflow for transmission electron microscopy: From data analysis automation to materials knowledge unveiling. <i>Advanced Materials</i>. 2025. doi:<a href="https://doi.org/10.1002/adma.202506785">10.1002/adma.202506785</a> 205942025-11-02T23:01:35Z2025-12-01T15:12:53Z