---
OA_place: publisher
OA_type: hybrid
_id: '20594'
abstract:
- lang: eng
  text: (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.
acknowledgement: 'ICN2 acknowledged funding from Generalitat de Catalunya 2021SGR00457,
  2021SGR00997 and 2021SGR01519. The authors thank support from the project AMaDE
  (PID2023-149158OB-C43), funded by MCIN/ AEI/10.13039/501100011033/. This study was
  part of the Advanced Materials programme and was supported by MCIN with funding
  from European Union NextGenerationEU (PRTR-C17.I1) and by Generalitat de Catalunya
  (In-CAEM Project). The authors acknowledged support from CSIC Interdisciplinary
  Thematic Platform (PTI+) on Quantum Technologies (PTI-QTEP+). This research work
  had been funded by the European Commission – NextGenerationEU (Regulation EU 2020/2094),
  through CSIC''s Quantum Technologies Platform (QTEP). ICN2 was supported by the
  Severo Ochoa program from Spanish MCIN / AEI (Grant No.: CEX2021-001214-S) and was
  funded by the CERCA Programme / Generalitat de Catalunya. Part of the present work
  had been performed in the framework of Universitat Autònoma de Barcelona Materials
  Science PhD program. I.P.H. acknowledged funding from AGAUR-FI scholarship (2023FI-00268)
  Joan Oró of the Secretariat of Universities of the Generalitat of Catalonia and
  the European SocialPlus Fund. M.B. acknowledged support from SUR Generalitat de
  Catalunya and the EU Social Fund; project ref. 2020 FI 00103. This study was supported
  by EU HORIZON INFRA TECH 2022 project IMPRESS (Ref.: 101094299). Authors acknowledged
  the use of instrumentation as well as the technical advice provided by the Joint
  Electron Microscopy Center at ALBA (JEMCA). ICN2 acknowledged funding from Grant
  IU16-014206 (METCAM-FIB) funded by the European Union through the European Regional
  Development Fund (ERDF), with the support of the Ministry of Research and Universities,
  Generalitat de Catalunya. ICN2 was a founding member of e-DREAM.[135] S.R. was also
  supported by MICIN with European funds NextGenerationEU (PRTRC17.I1) funded by Generalitat
  de Catalunya. P.O. acknowledged support from the EU MaX CoE (Grant No. 101093374),
  Grants No. PCI2022-134972-2 and No. PID2022-139776NB-C62 funded by the Spanish MCIN/AEI/10.13039/501100011033
  and by the ERDF, A way of making Europe.The authors thank the Catalan Quantum Academy
  for support. The authors acknowledged Dámaso Torres for his support in designing
  the graphical material.'
article_number: e06785
article_processing_charge: Yes (in subscription journal)
article_type: original
arxiv: 1
author:
- first_name: Marc
  full_name: Botifoll, Marc
  last_name: Botifoll
- first_name: Ivan
  full_name: Pinto-Huguet, Ivan
  last_name: Pinto-Huguet
- first_name: Enzo
  full_name: Rotunno, Enzo
  last_name: Rotunno
- first_name: Thomas
  full_name: Galvani, Thomas
  last_name: Galvani
- first_name: Catalina
  full_name: Coll, Catalina
  last_name: Coll
- first_name: Payam Habibzadeh
  full_name: Kavkani, Payam Habibzadeh
  last_name: Kavkani
- first_name: Maria Chiara
  full_name: Spadaro, Maria Chiara
  last_name: Spadaro
- first_name: Yann Michel
  full_name: Niquet, Yann Michel
  last_name: Niquet
- first_name: Martin Børstad
  full_name: Eriksen, Martin Børstad
  last_name: Eriksen
- first_name: Sara
  full_name: Martí-Sánchez, Sara
  last_name: Martí-Sánchez
- first_name: Georgios
  full_name: Katsaros, Georgios
  id: 38DB5788-F248-11E8-B48F-1D18A9856A87
  last_name: Katsaros
  orcid: 0000-0001-8342-202X
- first_name: Giordano
  full_name: Scappucci, Giordano
  last_name: Scappucci
- first_name: Peter
  full_name: Krogstrup, Peter
  last_name: Krogstrup
- first_name: Giovanni
  full_name: Isella, Giovanni
  last_name: Isella
- first_name: Andreu
  full_name: Cabot, Andreu
  last_name: Cabot
- first_name: Gonzalo
  full_name: Merino, Gonzalo
  last_name: Merino
- first_name: Pablo
  full_name: Ordejón, Pablo
  last_name: Ordejón
- first_name: Stephan
  full_name: Roche, Stephan
  last_name: Roche
- first_name: Vincenzo
  full_name: Grillo, Vincenzo
  last_name: Grillo
- first_name: Jordi
  full_name: Arbiol, Jordi
  last_name: Arbiol
citation:
  ama: '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>'
  apa: '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>'
  chicago: '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>.'
  ieee: '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.'
  ista: '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.'
  mla: '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>.'
  short: 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).
date_created: 2025-11-02T23:01:35Z
date_published: 2025-10-22T00:00:00Z
date_updated: 2025-12-01T15:12:53Z
day: '22'
ddc:
- '530'
department:
- _id: GeKa
doi: 10.1002/adma.202506785
external_id:
  arxiv:
  - '2411.01024'
  isi:
  - '001597428400001'
has_accepted_license: '1'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1002/adma.202506785
month: '10'
oa: 1
oa_version: Published Version
publication: Advanced Materials
publication_identifier:
  eissn:
  - 1521-4095
  issn:
  - 0935-9648
publication_status: epub_ahead
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Artificial intelligence-assisted workflow for transmission electron microscopy:
  From data analysis automation to materials knowledge unveiling'
tmp:
  image: /images/cc_by_nc_nd.png
  legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
  name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
    (CC BY-NC-ND 4.0)
  short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2025'
...