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optimized with a cooling coefficient. Besides, the increment in T increased the thermal stress on the legs,
h
which accounted for performance reduction. Meanwhile, the optimized geometrical parameter of TEG
minimized the thermal stress, avoiding the device's cracks and heat stagging. In the staggered and integrated
device, mechanical stability was a crucial parameter. The same author reported the optimization focused on
thermomechanical properties in the TEG and solar cell integrated device. The numerical method generated
data was used to feed the DNN to predict the performance quickly and with more accuracy. After the
optimization by focusing on the thermo-mechanical stability, the output power was increased, and the
[46]
thermal stress was reduced .
Designing an ML model based on actual environmental conditions is crucial, which helps design the TEG
for large-scale applications and suitable environmental conditions. However, the temperature of the TEG
surface depends on the environmental conditions. Ameenuddin Irfan et al. designed the TEG based on the
actual environment by analyzing the humidity variation and actual room temperature, predicting T and
h
[47]
T . For this, 35-day real-time data was recorded, which was used to feed the ML model to analyze the
c
variation. ML models, such as linear, tree regression, and Gaussian process regression (GPR), were
employed, and the GPR model had a high accuracy in predicting the environmental conditions.

MACHINE LEARNING (ML) METHODS
In ML methods, problems are solved by developing mathematical models and algorithms by discovering
patterns through statistical analysis of input data. The most common ML algorithms in chemistry are
support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), ensemble, decision tree
(DT), and NN methods, respectively.

Machine learning in materials chemistry
In materials chemistry, AI technologies, including ML methods, have been increasingly used to predict the
crucial factors for the synthesis and select the optimal reaction conditions. Gulevich et al. have reviewed the
application of ML methods to develop synthesis and choose the best synthesis conditions for colloidal
[48]
nanomaterials . As shown in Figure 3, AI technologies have several benefits for synthesizing colloidal
nanomaterials . Thus, optimization of synthesis parameters (time, temperature, concentration of
[48]
precursors, and additives) necessary for the synthesis of nanocrystals of several chalcogenides (CdS, CdSe,
PbS, and ZnSe) have been studied using ML . The use of similar ML methods for synthesizing size and
[33]
shape-controlled chalcogenides-based TE nanomaterials is yet to be known. This is one area that will
expand in the coming future.

The relation between the structural parameters in chemistry and the thermoelectric parameters is crucial to
segregating the material from high performance to low performance. Compared with the conventional
cascaded arrangement of TE devices, low-cost spin-driven thermoelectric (STE) consisting of simple layered
structures gained much attention since it has been fabricated by sputtering, coating, and platting, which is a
direct approach. However, STE consists of rare earth elements that are pretty costly. Applying ML to
analyze suitable composition is more time-efficient and can reduce material waste.

Iwasaki et al. applied the ML approach to discover novel STE material through actual material synthesis .
[49]
The experimental data of series of rare earth substituted yttrium iron garnet Pt/R:YIG (R = La, Ce, Pr, Nd,
Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu, except Pm) fabricated on Gd Ga O (GGG) and
3
5
12
Gd Ca Ga Mg Zr O (SGGG) substrates taken to analyze the Seebeck coefficient (S ) relation
STE
4.025
0.325
2.675
0.325
12
0.65
with other parameters such as atomic weight n , spin and orbital angular momenta S and L , lattice
R
R
R
mismatch Δa between R:YIG and the substrate under spin driven effect. In this study, four types of ML