From synthesis to machine learning validation for the thermoelectric Zintl phase Ba1−xEuxZn2−yAlySb2 system

Abstract

Five new Zintl phase compounds in the Ba_1−xEu_xZn_2−yAl_ySb₂ system were synthesized using the molten Pb-flux method. All compounds crystallized in the orthorhombic BaCu₂S₂-type phase with a phase selectivity determined by the r⁺/r⁻ radius ratio criterion. Detailed structural analysis revealed a complex three-dimensional anionic framework of [(Zn/Al)Sb₄] tetrahedra combined with the Ba/Eu mixed-cations occupying the central voids within the frameworks. Density functional theory calculations confirmed structural stability and revealed changes in the electronic environment, resulting in reduced carrier mobility and ultra-low thermal conductivity. Thermoelectric property measurements showed that the enhanced Seebeck coefficients and the minimized thermal conductivities yielded a maximum ZT of 0.50 at 653 K. However, a central finding of this work is the elucidation of the complex chemical interplay governing the system's electronic properties. Despite intentional n-type substitution with Al, all compounds remained robustly p-type. We demonstrate this is a direct consequence of a chemical balance between intrinsic incomplete electron transfer and the competing electronic effects of Eu and Al substituents. Three machine learning models developed by XGBRegressor algorithms and trained using a customized dataset of 5798 thermoelectric real-world experimental outcomes demonstrated high predictive accuracy. These models not only matched the experimental trends but, more importantly, correctly predicted the persistent p-type behavior. This success demonstrates that the model learned the complex, real-world chemical physics, allowing it to make predictions that go beyond simple electron-counting rules. Herein, we established a powerful workflow for accelerating thermoelectric materials discovery by integrating experimental synthesis with density functional theory analysis and machine learning validation.