ABSTRACT A comprehensive first‐principles and machine‐learning‐assisted study of the oxychalcogenide BaTa 4 Te 3 O 17 , highlighting its promise as a multifunctional material for thermoelectric, optoelectronic, and photocatalytic applications. Density functional theory (DFT) calculations show a direct bandgap of 3.3 eV with mixed dispersive and flat valence/conduction states, promoting anisotropic carrier transport. The effective electron and hole masses along the Γ–Γ direction exhibit more balanced carrier masses (m e * = 0.682 m 0 , m h * = 0.714 m 0 ), corresponding to a reduced mass of 0.348 m 0 , a binding energy of 260 meV, and a Bohr radius of 6.48 Å, signifying weaker exciton confinement. Thermoelectric analysis yields a Seebeck coefficient of 1029.23 µV K −1 and a figure of merit ZT ≈ 0.94 at 300 K, improving at higher temperatures. The band‐edge positions align well with the hydrogen evolution potential, suggesting photocatalytic suitability. To complement DFT results, supervised regression models (XGBoost and ensemble‐stacking) predict E g ≈ 3.3 eV (R 2 = 0.55) and ZT ≈ 0.94 with >90% accuracy. This integrated DFT–ML framework demonstrates a cost‐effective route for screening and optimizing heteroanionic oxychalcogenides for next‐generation energy and electronic applications.
Thamizharasan et al. (Thu,) studied this question.