Speaker:

Jordi Arbiol, Professor at the Catalan Institute of Nanoscience and Nanotechnology 

Abstract

The discovery, optimization, and application of new materials is a complex and multifaceted process that involves identifying technological needs, reviewing existing literature, proposing candidate materials, engineering devices, characterizing structures, and evaluating performance. In particular, understanding the underlying physical and chemical processes requires visualizing materials under working conditions. Furthermore, the vast amount of data generated by modern microscopes demands innovative methodologies that enable workflows with atomic-level precision and statistically meaningful results.[1]

To address these challenges, I will present the approaches developed at the Joint Electron Microscopy Center at ALBA (JEMCA), where we integrate (S)TEM-based techniques with scanning probe microscopies and X-ray microscopies and spectroscopies, all under in-situ and operando conditions.[2] Additionally, we introduce an AI-enhanced analytical workflow that leverages machine learning and deep learning to automate the analysis of transmission electron microscopy (TEM) data. This workflow enables comprehensive characterization of materials and device architectures, focusing on energy and environmental applications, as well as quantum materials and their heterostructures.

Our pioneering workflow autonomously identifies material composition, crystallographic phases, and spatial orientations across diverse regions of (S)TEM images and datasets through advanced model comparison. It also incorporates automated strain analysis, offering a detailed understanding of structural properties. The extracted data is used to generate 3D atomic and finite element models in a fully automated way, which facilitate theoretical simulations and provide critical physical and chemical insights into device performance under real-world conditions.

This methodology is highly versatile and demonstrates strong generalization capabilities across different material systems. Beyond addressing the urgent need for automation in materials characterization, it enables the generation of accurate physical models and simulations of complex devices with unprecedented precision. [3-5] 

Speaker Bio

Prof. Jordi Arbiol studied Physics at Universitat de Barcelona (UB) in 1997, where also obtained his PhD (European Doctorate and PhD Extraordinary Award) in 2001. He worked as Assistant Professor at UB (2001-2009). From 2009 to 2015 he was ICREA Prof. at Institut de Ciència de Materials de Barcelona, ICMAB-CSIC. Since 2015 he is ICREA Prof. at Institut Català de Nanociència i Nanotecnologia (ICN2) and Leader of the Advanced Electron Nanoscopy Group. President of the Spanish Microscopy Society (SME) (2017-2021), Vice-President (2013-2017) and Executive Board Member (2009-2021). Executive Board Member of the International Federation of Societies for Microscopy (IFSM) (2019-2026). Founding member of e-DREAM (European Distributed REsearch Infrastructure for Advanced Electron Microscopy). Scientific Coordinator and Advisor for the Materials Science Section at the Joint Electron Microscopy Center at ALBA Synchrotron (JEMCA). Since 2023 Associate Editor of Nano Letters (ACS). Received the FWO Commemorative Medal (Flanders Research Foundation) in 2021, the BIST IGNITE Award in 2018 and was awarded with the EU40 Materials Prize in 2014.

[1] J. Yu et al., Advanced Materials, DOI: 10.1002/adma.202511345 (2025)

[2] E. Pastor et al., Nature Reviews Chemistry, 8, 159 (2024)

[3] M. Botifoll, I. Pinto-Huguet et. al, Nanoscale Horizons, 7, 1427 (2022)

[4] M. Botifoll et. al, Advanced Materials, DOI: 10.1002/adma.202506785 (2025)

[5] I. Pinto-Huguet et. al, Advanced Intelligent Systems, DOI: 10.1002/aisy.202501077 ( 2025)