Machine-Learning-Assisted Development and Theoretical Consideration for the Al2Fe3Si3 Thermoelectric Material.

Abstract

Chemical composition alteration is a general strategy to optimize the thermoelectric properties of a thermoelectric material to achieve high-efficiency conversion of waste heat into electricity. Recent studies show that the Al2Fe3Si3 intermetallic compound with a relatively high power factor of ∼700 μW m-1 K-2 at 400 K is promising for applications in low-cost and nontoxic thermoelectric devices. To accelerate the exploration of the thermoelectric properties of this material in a mid-temperature range and to enhance its power factor, a machine-learning method was employed herein to assist the synthesis of off-stoichiometric samples (namely, Al23.5+ xFe36.5Si40- x) of the Al2Fe3Si3 compound by tuning the Al/Si ratio. The optimal Al/Si ratio for a high power factor in the mid-temperature range was found rapidly and efficiently, and the optimal ratio of the sample at x = 0.9 was found to increase the power factor at ∼510 K by about 40% with respect to that of the initial sample at x = 0.0. The possible mechanism for the enhanced power factor is discussed in terms of the precipitations of the metallic secondary phases in the Al23.5+ xFe36.5Si40- x samples. Furthermore, the maximum achievable thermal conductivity of Al2Fe3Si3 estimated by the Slack model is ∼10 W m-1 K-1 at the Debye temperature. An avoided-crossing behavior of the acoustic and the low-lying optical modes along several crystallographic directions is found in the phonon dispersion of Al2Fe3Si3 calculated by ab initio density functional theory method. These preliminary results suggest that Al2Fe3Si3 can have a low thermal conductivity. The calculated formation energies of point defects suggest that the antisite defects between Al and Si are likely to cause the Al and Si off-stoichiometries in Al2Fe3Si3. The theoretically obtained insight provides additional information for the further understanding of Al2Fe3Si3.