A composition-only model that applies ReLU to a weighted average of per-element learned parameters predicts band gaps with 0.575 eV MAE and yields chemically interpretable element weights.
Venkatraman, The utility of composition-based machine learning models for band gap prediction, Computational Materi- als Science 197, 110637 (2021)
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Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics
A composition-only model that applies ReLU to a weighted average of per-element learned parameters predicts band gaps with 0.575 eV MAE and yields chemically interpretable element weights.