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DTSTAMP:20260424T233055Z
UID:1759827600@ist.ac.at
DTSTART:20251007T110000
DTEND:20251007T120000
DESCRIPTION:Speaker: Jordi Arbiol\nhosted by Maria IBÁÑEZ \nAbstract: Abs
 tract: The discovery\, optimization\, and application of new materials is 
 a complex and multifaceted process that encompasses identifying technologi
 cal needs\, reviewing existing literature\, proposing candidate materials\
 , engineering devices\, characterizing structures\, and testing performanc
 e. This workflow becomes particularly time-consuming and costly when atomi
 c-level precision is required to understand the functionality of materials
  and heterostructured devices.To address these challenges\, we introduce a
 n AI-enhanced analytical workflow based on machine learning and deep learn
 ing techniques that automate the analysis of transmission electron microsc
 opy (TEM) data. This workflow enables comprehensive characterization of ma
 terials and device architectures\, with a focus on energy and environmenta
 l applications\, as well as quantum materials and their associated heteros
 tructures.Our pioneering workflow autonomously identifies material composi
 tion\, crystallographic phases\, and spatial orientations across diverse r
 egions of (S)TEM images and datasets through advanced model comparison. It
  also incorporates automated strain analysis\, offering a detailed underst
 anding of structural properties. The extracted data is used to generate 3D
  atomic and finite element models\, which facilitate theoretical simulatio
 ns and provide critical physical and chemical insights into device perform
 ance 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 cha
 racterization\, it enables the generation of accurate physical models and 
 simulations of complex devices with unprecedented precision. [1-3] [1] M.
  Botifoll\, I. Pinto-Huguet et. al\, Nanoscale Horizons\, 7\, 1427-1477\, 
 2022.[2] M. Botifoll\, I. Pinto-Huguet et. al\, arXiv arXiv:2411.01024\, I
 nici del formulari2024.[3] I. Pinto-Huguet et. al\, arXiv arXiv:2505.01789
 \,  2025.     
LOCATION:Office Bldg West / Ground floor / Heinzel Seminar Room (I21.EG.101
 )\, ISTA
ORGANIZER:Rita.Pacarada@ist.ac.at
SUMMARY:Jordi Arbiol: “ Automatic Atomic Scale Data Analysis and Modellin
 g for (Scanning) Transmission Electron Microscopy”
URL:https://talks-calendar.ista.ac.at/events/5983
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