SOURCE-LINKED INTELLIGENCE

Bayesian Experimental Design for in situ (Scanning) Transmission Electron Microscopy – BED-TEM

CORDIS · observation · Publication date unknown

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 applica

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recordType

award

status

SIGNED

region

EU

value

150000

unit

EUR