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BED-TEM

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Bayesian Experimental Design for in situ Scanning Transmission Electron Micros...

Bayesian Experimental Design for in situ Scanning Transmission Electron Microscopy BED TEM The proposed project, Bayesian Experimental Design for in situ (Scanning) Transmission Electron Microscopy (BED-TEM), addresses challenges in conducting in situ (S)TEM experiments, particularly in selecting optimal stimuli paramet... see more

30/06/2026

TECHNISCHE UNIVERS...

150K€

Project Budget: 150K€

Project leader

TECHNISCHE UNIVERSITAT DARMSTADT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

Financing granted El organismo HORIZON EUROPE notifico la concesión del proyecto The day 2024-09-17

ERC-2024-POC: ERC PROOF OF CONCEPT GRANTS Objective:ObjectivesThe ERC Proof of Concept Grants aim at facilitating exploration of the commercia...

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1 Participantes

TECHNISCHE UNIVERSITAT DARMS...

150.00K€ | Leader

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Project duration: 21 months Date Start: 2024-09-17
End date: 2026-06-30

Project leader

TECHNISCHE UNIVERSITAT DARMSTADT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 150K€

participation deadline Sin fecha límite de participación.

Project description The proposed project, Bayesian Experimental Design for in situ (Scanning) Transmission Electron Microscopy (BED-TEM), addresses challenges in conducting in situ (S)TEM experiments, particularly in selecting optimal stimuli parameters. While recent advancements in microelectromechanical systems (MEMS) have expanded stimulus options, determining appropriate values remains elusive, often relying on time-consuming trial and error. The project aims to introduce a software platform integrating Bayesian experimental design with (S)TEM image analysis, facilitating efficient parameter selection. This platform comprises user interface, image processing, and experimental design modules, offering streamlined workflow and enhanced usability. Leveraging expertise in machine learning and (S)TEM, the project seeks to bridge the gap between complex experimental setups and practical application of Bayesian methods. The envisioned breakthrough lies in transforming offline, iterative parameter determination into an online, iterative process, revolutionizing how in situ experiments are conducted and accelerating materials research and development. The project also poses high risks, particularly in adapting machine learning to (S)TEM data and ensuring market demand for the proposed software. However, mitigation strategies include iterative development, collaboration with experts, and continuous user feedback to align the solution with customer needs and commercial viability. Ultimately, BED-TEM promises to reshape materials science by enabling precise characterization of material behaviors at the nanoscale under real-world conditions, with potential applications spanning electronics, aerospace, and beyond.

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