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Chen et al. Energy Mater. 2025, 5, 500120 https://dx.doi.org/10.20517/energymater.2024.311 Page 11 of 21

Figure 5. Process of screening compounds under the optimization, band gap, and phonon frequency to get nonmetallic and stable
[53]
compounds, to calculate electronic properties . (Creative Commons Attribution 4.0 license).
to predict the band gap, κ, and elastic properties of zeolites . Rather than directly influencing model
[55]
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precision, the size of the dataset exerts its effect indirectly through the model DoF. It was challenging to
predict the results in unknown domains without affecting the precision. Integration of crude estimation
features in ML model improved the predictive accuracy without using the high DoF. Experimental and
density functional theory (DFT)-based datasets were used to train the model before and after integrating the
crude estimation, respectively. Kerne ridge regression ML model was used to predict the κ, the error of 6.2%
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that occurred was reduced to 4.1% after the integration of the crude estimation feature. This study implies
the improvement of accuracy with the integration of crude estimation, which needs less data to create an
ML model.

Doping is crucial for TE material performance enhancement. However, more elements can be used for
doping in a particular site due to the availability of more elements in a similar oxidation state. Determining