A Statistical Approach for the Rapid Prediction of Electron Relaxation Time Using Elemental Representatives

Efficiency of a thermoelectric material relies on a combination of electronic and thermal transport properties, which are governed by various scattering mechanisms. Explicit evaluation of temperature dependent scattering time or the electron relaxation time (τel) is thus necessary to assess the efficiency of thermoelectrics. Experimental or computational measurement of τel is very challenging due to the inherent time limitation and high computational cost. Herein, a statistical machine learning (ML) based approach has been developed to predict the experimental electrical conductivity (σ) followed by an estimation of the relaxation time (τel). By utilizing a unique mean ranking method for feature selection, simple elemental properties such as the boiling point, melting point, molar heat capacity, electron affinity, and ionization energy are identified as the potential descriptors for σ. Using a data set of 124 compounds, a Gradient Boost Regression (GBR) model is developed, which has very small root-mean-square error (rmse) of 0.22 S/cm and a high coefficient of determination (R2) of 0.98 for prediction of log-scaled σ. Utilizing the predicted σ values, τel has been calculated for a wide range of temperatures. ML predicted τel values outperform the τdef, obtained from the deformation potential model. The developed GBR model for accurate prediction of σ could accelerate the assessment of the efficiency of the thermoelectric materials with unprecedented accuracies.