Machine-Learning-Assisted
Development and Theoretical Consideration for the Al2Fe3Si3 Thermoelectric Material
Version 4 2019-03-18, 15:21
Version 3 2019-03-15, 10:13
Version 2 2019-03-14, 10:13
Version 1 2019-03-05, 12:35
Posted on 2019-03-18 - 15:21
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.
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Hou, Zhufeng; Takagiwa, Yoshiki; Shinohara, Yoshikazu; Xu, Yibin; Tsuda, Koji (2019). Machine-Learning-Assisted
Development and Theoretical Consideration for the Al2Fe3Si3 Thermoelectric Material. ACS Publications. Collection. https://doi.org/10.1021/acsami.9b02381
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AUTHORS (5)
ZH
Zhufeng Hou
YT
Yoshiki Takagiwa
YS
Yoshikazu Shinohara
YX
Yibin Xu
KT
Koji Tsuda