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Secondary-data-driven and interpretable machine learning for experimental band-gap prediction in inorganic semiconductors
A secondary experimental dataset containing 6,354 band-gap records was curated by removing invalid observations, consolidating repeated measurements, normalising chemically equivalent formulas, and applying an experimental-consistency criterion. This resulted in 2,264 unique compositions. Composi
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