A combined first-principles and machine learning study of phase stability and thermoelectric properties in ZrSiPt

Abstract

ZrSiPt is a versatile ternary compound that stabilizes in two distinct crystal structures: a cubic half-Heusler phase and an orthorhombic TiNiSi-type phase. This study presents an integrated first-principles and machine learning (ML) investigation of its phase-dependent structural, electronic, mechanical, thermodynamic, and thermoelectric properties. Density functional theory (DFT) calculations with the GGA-PBE functional and TB-mBJ potential reveal that the cubic phase is an indirect-gap semiconductor (∼1.40 eV), while the orthorhombic phase is a semimetallic, mechanically stiff and anisotropic system. Spin–orbit coupling is shown to have a negligible effect on the electronic structure. Thermodynamic analysis within the quasi-harmonic approximation suggests a possible phase-stability crossover near 300 K, driven primarily by vibrational entropy. Refined transport calculations, incorporating deformation-potential-derived relaxation times and lattice thermal conductivity from the Debye–Callaway model, drastically revise the thermoelectric assessment: the cubic phase exhibits a high lattice thermal conductivity that suppresses its figure of merit (ZT), whereas the orthorhombic phase, with intrinsically low thermal conductivity, emerges as the more promising candidate for further investigation. A proof-of-concept machine-learning framework; trained on a synthetic dataset derived from DFT results and validated via stratified cross-validation; successfully classifies the crystal phase (∼90% accuracy) and predicts key thermoelectric properties. Feature-importance analysis identifies lattice constant and band gap as the dominant descriptors, directly linking geometric packing and electronic stability to phase selection. This combined DFT+ML approach not only elucidates the dual-phase behavior of ZrSiPt but also demonstrates a synergistic workflow for accelerating the design of phase-specific materials for thermoelectric, optoelectronic, and mechanically robust applications.