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

Mukherjee, M and Satsangi, S and Singh, AK (2020) A Statistical Approach for the Rapid Prediction of Electron Relaxation Time Using Elemental Representatives. In: Chemistry of Materials, 32 (15). pp. 6507-6514.

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Official URL: https://dx.doi.org/10.1021/acs.chemmater.0c01778


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 (�el) is thus necessary to assess the efficiency of thermoelectrics. Experimental or computational measurement of �el 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 (�) followed by an estimation of the relaxation time (�el). 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 �. 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 �. Utilizing the predicted � values, �el has been calculated for a wide range of temperatures. ML predicted �el values outperform the �def, obtained from the deformation potential model. The developed GBR model for accurate prediction of � could accelerate the assessment of the efficiency of the thermoelectric materials with unprecedented accuracies. © 2020 American Chemical Society.

Item Type: Journal Article
Publication: Chemistry of Materials
Publisher: American Chemical Society
Additional Information: The copyright of this article belongs to American Chemical Society
Keywords: Forecasting; Ionization potential; Mean square error; Relaxation time; Specific heat; Thermoelectric equipment; Thermoelectricity, Coefficient of determination; Deformation potential; Electrical conductivity; Electron relaxation time; Electronic and thermal transports; Root mean square errors; Statistical machine learning; Thermo-Electric materials, Electron affinity
Department/Centre: Division of Chemical Sciences > Materials Research Centre
Date Deposited: 23 Sep 2020 06:53
Last Modified: 23 Sep 2020 06:53
URI: http://eprints.iisc.ac.in/id/eprint/66601

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