Modeling thermoelectric performance of p-type Cu3SbSe4-based chalcogenide materials using decision trees and structural risk error minimization intelligent computational methods - Fawaz Saad Alharbi (PLoS ONE, 2026)
Cu 3 SbSe 4 -based materials are ternary chalcogenides thermoelectric compounds with unique sphalerite super-lattice structures and adjustable characteristics which stand them out as promising material for attaining efficient thermal and electrical energy conversion. The crystal structure of Cu 3 SbSe 4 -based materials consists of Cu-Se three dimensional frameworks with inserted CuSe 4 tetrahedra layer. This energy band structure and crystal arrangement in Cu 3 SbSe 4 -based materials lead to large seebeck coefficient, low thermal conductivity and large carrier mobility with restricted number of available carriers which hinders the potential of these materials as thermoelectric compound due to low value of thermoelectric performance. Experimental methods of thermoelectric performance (using figure of merit as a measure of energy conversion efficiency) enhancement are laborious, costly and consume appreciable resources which necessitate the need of computational methods for figure of merit prediction. In this contribution, figure of merit of Cu 3 SbSe 4 -based materials has been modeled through random forest regression (decision trees) and genetic algorithm incorporated support vector regression (structural risk error minimization-based) model using temperature, dopants ionic radii and their respective concentrations as predictors. Genetically optimized support vector regression (GESVR) model outperforms random forest regression (RFR)-based model with improvement of 188.04%, 30.18% and 42.36% using correlation coefficient, mean absolute error and root mean square error, respectively for testing samples of Cu 3 SbSe 4 -based compounds. Influence of inclusions on energy conversion efficiency of Cu 3 Sb 1-x Sn x Se 4 and Cu 3 Sb 1-x Fe x Se 2.8 S 1.2 compounds was investigated using GESVR- based model. The simplicity of descriptors coupled with the demonstrated precision would facilitate the exploration of Cu 3 SbSe 4 -based materials for green applications and ultimately address the current global energy crisis.