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High-throughput prediction of the carrier relaxation time via data-driven descriptor

Physics

High-throughput prediction of the carrier relaxation time via data-driven descriptor

Z. Zhou, G. Cao, et al.

Discover an innovative descriptor crafted by Zizhen Zhou, Guohua Cao, Jianghui Liu, and Huijun Liu, which efficiently predicts carrier relaxation time in tetradymite compounds using a unique data-driven approach. This breakthrough requires no complex calculations and leverages elemental properties, revolutionizing the study of materials with diverse stoichiometries.

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Playback language: English
Abstract
This paper proposes an efficient and physically interpretable descriptor to evaluate the carrier relaxation time in tetradymite compounds, using a data-driven method named SISSO. The descriptor, derived from deformation potential theory, uses only elemental properties of constituent atoms and predicts relaxation time reliably for a large number of tetradymites with arbitrary stoichiometry, without requiring first-principles calculations.
Publisher
npj Computational Materials
Published On
Oct 08, 2020
Authors
Zizhen Zhou, Guohua Cao, Jianghui Liu, Huijun Liu
Tags
carrier relaxation time
tetradymite compounds
data-driven method
descriptor
deformation potential theory
elemental properties
stoichiometry

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