High-throughput prediction of the carrier relaxation time via data-driven descriptor
Source abstract
Abstract
Abstract It has been demonstrated that many promising thermoelectric materials, such as tetradymite compounds are also three-dimensional topological insulators. In both cases, a fundamental question is the evaluation of carrier relaxation time, which is usually a rough task due to the complicated scattering mechanisms. Previous works using the simple deformation potential theory or considering complete electron-phonon coupling are, however, restricted to small systems. By adopting a data-driven method named SISSO (Sure Independence Screening and Sparsifying Operator) with the training data obtained via deformation potential theory, we propose an efficient and physically interpretable descriptor to evaluate the relaxation time, using tetradymites as prototypical examples. Without any input from first-principles calculations, the descriptor contains only several elemental properties of the constituent atoms, and could be utilized to quickly and reliably predict the carrier relaxation time of a substantial number of tetradymites with arbitrary stoichiometry.
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Research context
OpenAlex topics: Machine Learning in Materials Science · Advanced Thermoelectric Materials and Devices · Thermal properties of materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (51)
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