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Machine learning accelerates analysis of fusion materials
- April 15, 2026 at 9:20 PM
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Tungsten's superior performance in extreme environments makes it a leading candidate for plasma-facing components (PFCs) in fusion reactors, but the ultra-high heat can damage its microscopic structure and lead to component failure. Scanning electron microscopy (SEM) can capture and quantify these microstructure changes, but assembling a sufficiently large dataset of SEM imagery is expensive and logistically challenging.
Originally published at Phys.org