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

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.