1.2k citations · 3k across the 25 of their papers we have counts for
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When Band Convergence is Not Beneficial for Thermoelectrics
Junsoo Park, Max Dylla, Yi Xia +3
Band convergence is considered a clear benefit to thermoelectric performance because it increases the charge carrier concentration for a given Fermi level, which typically enhances…
Optimal Band Structure for Thermoelectrics with Realistic Scattering and Bands
Junsoo Park, Yi Xia, Vidvuds Ozoliņš +1
Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified ban…
Efficient calculation of carrier scattering rates from first principles
Alex M. Ganose, Junsoo Park, Alireza Faghaninia +3
The electronic transport behaviour of materials determines their suitability for technological applications. We develop an efficient method for calculating carrier scattering rates…
Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm
Alexander Dunn, Qi Wang, Alex Ganose +2
We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk mat…
A Model for Thermodynamic Properties of Monoatomic Liquids
Drew Lilly, Anubhav Jain, Ravi Prasher
We present an analytical model for calculating the thermodynamic properties of monoatomic liquids using a rough potential energy surface (PES). The PES is transformed into an equiv…
High Thermoelectric Performance and Defect Energetics of Multi-pocketed Full-Heusler Compounds
Junsoo Park, Yi Xia, Alex Ganose +2
We report first-principles density-functional study of electron-phonon interactions and thermoelectric transport properties of full-Heusler compounds SrBiAu and SrSbAu.…
A critical examination of compound stability predictions from machine-learned formation energies
Christopher J. Bartel, Amalie Trewartha, Qi Wang +3
Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of…