Affiliation:
1. Institute of Low Temperature and Structure Research, Polish Academy of Sciences, Okólna 2, 50-370 Wrocław, Poland
Abstract
Machine learning models (Support Vector Regression) were applied for predictions of several targets for 18-electron half-Heusler phases: a lattice parameter, a bulk modulus, a band gap, and a lattice thermal conductivity. The training subset, which consisted of 47 stable phases, was studied with the use of Density Functional Theory calculations with two Exchange-Correlation Functionals employed (GGA, MBJGGA). The predictors for machine learning models were defined among the basic properties of the elements. The most optimal combinations of predictors for each target were proposed and discussed. Root Mean Squared Errors obtained for the best combinations of predictors for the particular targets are as follows: 0.1 Å (lattice parameters), 11–12 GPa (bulk modulus), 0.22 eV (band gaps, GGA and MBJGGA), and 9–9.5 W/mK (lattice thermal conductivity). The final results of the predictions for a large set of 74 semiconducting half-Heusler compounds were disclosed and compared to the available literature and experimental data. The findings presented in this work encourage further studies with the use of combined machine learning and ab initio calculations.
Funder
Wroclaw Center for Networking and Supercomputing
Reference65 articles.
1. DFT study of RhTiP half Heusler semiconductors: Revealing its mechanical, optoelectronic, and thermoelectric properties;Dubey;Phys. B Condens. Matter,2024
2. Investigation of optoelectronic properties of half-Heusler KZnN and KZnP compounds;Azzi;Rev. Mex. Fis.,2023
3. Sartipi, E., Elahi, S.M., Hantehzadeh, M.R., Boochani, A., and Ghoranneviss, M. (2023). Giant magneto-optical Kerr effect and thermoelectric properties in CeBiPt half-Heusler by DFT. Mod. Phys. Lett. B, 2350253.
4. Bilińska, K., and Winiarski, M.J. (2023). High-Throughput Exploration of Half-Heusler Phases for Thermoelectric Applications. Crystals, 13.
5. Ferrimagnetic half Heusler alloys for waste heat recovery application-First principle study using different exchange–correlation functionals;Sudharsan;J. Magn. Magn. Mater.,2023