Fig 1.
Computational flow chart of genetic algorithm for SVR parameters optimization.
Table 1.
Statistical report of data samples employed for figure of merit prediction in Cu3SbSe4 system of material.
Fig 2.
Computational flow chart of fitness computation using SVR algorithm.
Fig 3.
Convergence optimization of GESVR parameters (a) regularization factor (b) Epsilon threshold (c) Gaussian kernel factor (d) RMSE fitness parameter.
Table 2.
Optimum parameters for thermoelectric figure of merit prediction in Cu3SbSe4 system of materials.
Fig 4.
Cross-plots using correlation metric for (a) GESVR (b) RFR models using training and testing samples of Cu3SbSe4 system of materials.
Fig 5.
Performance comparison between GESVR and RFR models using (a) CC training (b) MAE training (c) RMSE training (d) CC testing (e) MAE testing and (f) RMSE testing of Cu3SbSe4 system of materials.
Table 3.
Performance evaluation parameters for GESVR and RFR model with their percentage superiority using different samples of Cu3SbSe4 system of materials.
Table 4.
Estimated thermoelectric figure of merit for Cu3SbSe4 system of materials.
Fig 6.
Effect of dopant substitution on thermoelectric figure of merit of (a) Cu3Sb1-xFexSe2.8S1.2 at a temperature of 600K and (b) Cu3Sb1-xSnxSe4 at a temperature of 673 K.