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

Physical Chemistry Chemical Physics 2026

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Authors: N Serir, M Khenata, H. Meradji, R. Khenata, Hamad Rahman Jappor, Devraj Singh, Shanay Rab, S. Bin-Omran, Souraya Goumri-Said
DOI: 10.1039/d5cp04318b
Cited by: 3
References: 63

Abstract

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.

Fields of Study & Subjects

Advanced Thermoelectric Materials and Devices Heusler alloys: electronic and magnetic properties Machine Learning in Materials Science

Publication details

Journal / source
Physical Chemistry Chemical Physics
Publisher
Royal Society of Chemistry (RSC)
Volume
28
Issue
15
Pages
9441–9463
Publication date
1 January 2026
Language
EN