# Modeling thermoelectric performance of p-type Cu 3 SbSe 4 -based chalcogenide materials using decision trees and structural risk error minimization intelligent computational methods

## TL;DR

This paper uses machine learning to predict and improve the thermoelectric performance of Cu3SbSe4-based materials for energy conversion.

## Contribution

A novel GESVR model outperforms RFR in predicting thermoelectric performance of Cu3SbSe4-based materials.

## Key findings

- GESVR model shows 188.04% improvement in correlation coefficient over RFR for figure of merit prediction.
- The model investigates the impact of inclusions on Cu3Sb1-xSnxSe4 and Cu3Sb1-xFexSe2.8S1.2 compounds.
- Simpler descriptors enable efficient exploration of Cu3SbSe4-based materials for green energy applications.

## Abstract

Cu3SbSe4-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 Cu3SbSe4-based materials consists of Cu-Se three dimensional frameworks with inserted CuSe4 tetrahedra layer. This energy band structure and crystal arrangement in Cu3SbSe4-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 Cu3SbSe4-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 Cu3SbSe4-based compounds. Influence of inclusions on energy conversion efficiency of Cu3Sb1-xSnxSe4 and Cu3Sb1-xFexSe2.8S1.2 compounds was investigated using GESVR- based model. The simplicity of descriptors coupled with the demonstrated precision would facilitate the exploration of Cu3SbSe4-based materials for green applications and ultimately address the current global energy crisis.