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. 2024 Nov 18;14:28545. doi: 10.1038/s41598-024-80212-9

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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Fig. 2 — Machine learning procedure. From the combination of the design parameters () and the material energy gaps, the first solution of the potential profile () is constructed, and its features are reduced by applying the principal component analysis (PCA(PP₀)) to obtain the principal components (). The combined with the V are the inputs of the first multi-layer perceptron (MLP1), which gives the difference between potential profile (PP) and (PP-PP₀) as the output. The PP of the device is obtained by applying the inverse principal component analysis (PCA) ((PP-PP₀) and adding the PP₀. The inputs of the second multi-layer perceptron (MLP2) are the PP obtained from the application of PCA(PP) to the PP. Finally, the MLP2 provides, as output the information about the cooling properties (CP, ) and the device activation energies (, ).

Inline graphic — Machine learning procedure. From the combination of the design parameters () and the material energy gaps, the first solution of the potential profile () is constructed, and its features are reduced by applying the principal component analysis (PCA(PP₀)) to obtain the principal components (). The combined with the V are the inputs of the first multi-layer perceptron (MLP1), which gives the difference between potential profile (PP) and (PP-PP₀) as the output. The PP of the device is obtained by applying the inverse principal component analysis (PCA) ((PP-PP₀) and adding the PP₀. The inputs of the second multi-layer perceptron (MLP2) are the PP obtained from the application of PCA(PP) to the PP. Finally, the MLP2 provides, as output the information about the cooling properties (CP, ) and the device activation energies (, ).