Estimating the lattice thermal conductivity of AlCoCrNiFe high-entropy alloy using machine learning

Abstract

The lattice thermal conductivity stands as a pivotal thermos-physical parameter of high-entropy alloys; nonetheless, achieving precise predictions of the lattice thermal conductivity for high-entropy alloys poses a formidable challenge due to their complex composition and structure. In this study, machine learning models were built to predict the lattice thermal conductivity of AlCoCrNiFe high-entropy alloy based on molecular dynamic simulations. Our model shows high accuracy with R2, mean absolute percentage error, and root mean square error of the test set is 0.91, 0.031, and 1.128 W m−1 k−1, respectively. In addition, a high-entropy alloy with low a lattice thermal conductivity of 2.06 W m−1 k−1 (Al8Cr30Co19Ni20Fe23) and with a high lattice thermal conductivity of 5.29 W m−1 k−1 (Al0.5Cr28.5Co25Ni25.5Fe20.5) was successfully predicted, which shows good agreement with the results from molecular dynamics simulations. The mechanisms of the thermal conductivity divergence are further explained through their phonon density of states and elastic modulus. The established model provides a powerful tool for developing high-entropy alloys with the desired properties.