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                   publicationCoverPdf [Last accessed on 4 Jun 2025]
               41.      Lange, R. G.; Carroll, W. P. Review of recent advances of radioisotope power systems. Energy. Convers. Manag. 2008, 49, 393-401.
                   DOI
               42.      Basu, R.; Bhattacharya, S.; Bhatt, R.; et al. Improved thermoelectric performance of hot pressed nanostructured n-type SiGe bulk
                   alloys. J. Mater. Chem. A. 2014, 2, 6922.  DOI
               43.      Ahmad, S.; Singh, A.; Basu, R.; et al. Optimization of thermoelectric properties of mechanically alloyed p-type SiGe by mathematical
                   modelling. J. Electron. Mater. 2019, 48, 649-55.  DOI
               44.      Karthikeyan, V.; Surjadi, J. U.; Li, X.; et al. Three dimensional architected thermoelectric devices with high toughness and power
                   conversion efficiency. Nat. Commun. 2023, 14, 2069.  DOI  PubMed  PMC
               45.      Maduabuchi, C. Thermo-mechanical optimization of thermoelectric generators using deep learning artificial intelligence algorithms fed
                   with verified finite element simulation data. Appl. Energy. 2022, 315, 118943.  DOI
               46.      Maduabuchi, C.; Eneh, C.; Alrobaian, A. A.; Alkhedher, M. Deep neural networks for quick and precise geometry optimization of
                   segmented thermoelectric generators. Energy 2023, 263, 125889.  DOI
               47.      Ameenuddin Irfan, S.; Irshad, K.; Algahtani, A.; et al. Machine learning-based modeling of thermoelectric materials and air-cooling
                   system developed for a humid environment. Mater. Express. 2021, 11, 153-65.  DOI
               48.      Gulevich, D.; Nabiev, I.; Samokhvalov, P. Machine learning-assisted colloidal synthesis: a review. Mater. Today. Chem. 2024, 35,
                   101837.  DOI
               49.      Iwasaki, Y.; Takeuchi, I.; Stanev, V.; et al. Machine-learning guided discovery of a new thermoelectric material. Sci. Rep. 2019, 9,
                   2751.  DOI  PubMed  PMC
               50.      Tewari, A.; Dixit, S.; Sahni, N.; Bordas, S. P. A. Machine learning approaches to identify and design low thermal conductivity oxides
                   for thermoelectric applications. Data-Centric. Eng. 2020, 1, e8.  DOI
               51.      Juneja, R.; Yumnam, G.; Satsangi, S.; Singh, A. K. Coupling the high-throughput property map to machine learning for predicting
                   lattice thermal conductivity. Chem. Mater. 2019, 31, 5145-51.  DOI
               52.      Juneja, R.; Singh, A. K. Unraveling the role of bonding chemistry in connecting electronic and thermal transport by machine learning.
                   J. Mater. Chem. A. 2020, 8, 8716-21.  DOI
               53.      Juneja, R.; Singh, A. K. Guided patchwork kriging to develop highly transferable thermal conductivity prediction models. J. Phys.
                   Mater. 2020, 3, 2.  DOI
               54.      Li, W.; Liu, M. Interpretable machine learning workflow for evaluating and analyzing the performance of high-entropy GeTe-based
                   thermoelectric materials. ACS. Appl. Electron. Mater. 2023, 5, 4523-33.  DOI
               55.      Zhang, Y.; Ling, C. A strategy to apply machine learning to small datasets in materials science. NPJ. Comput. Mater. 2018, 4, 81.
                   DOI
               56.      He, Z.; Peng, J.; Lei, C.; Xie, S.; Zou, D.; Liu, Y. Prediction of superior thermoelectric performance in unexplored doped-BiCuSeO via
                   machine learning. Mater. Des. 2023, 229, 111868.  DOI
               57.      Minhas, H.; Jena, M. K.; Sharma, R. K.; Pathak, B. Machine learning-driven inverse design and role of dopant for tuning
                   thermoelectric efficiency. ACS. Appl. Electron. Mater. 2024, 6, 5815-26.  DOI
               58.      Parse, N.; Pongkitivanichkul, C.; Pinitsoontorn, S. Machine learning approach for maximizing thermoelectric properties of BiCuSeO
                   and discovering new doping element. Energies 2022, 15, 779.  DOI
               59.      Tang, B.; Lu, Y.; Zhou, J.; et al. Machine learning-guided synthesis of advanced inorganic materials. Mater. Today. 2020, 41, 72-80.
                   DOI
               60.      Wang, Z.; Adachi, Y.; Chen, Z. C. Processing optimization and property predictions of hot-extruded Bi-Te-Se thermoelectric materials
                   via machine learning. Adv. Theory. Simul. 2020, 3, 1900197.  DOI
               61.      Song, K.; Xu, G.; Tanvir, A. N. M.; et al. Machine learning-assisted 3D printing of thermoelectric materials of ultrahigh performances
                   at room temperature. J. Mater. Chem. A. 2024, 12, 21243-51.  DOI
               62.      Alrebdi, T.; Wudil, Y.; Ahmad, U.; Yakasai, F.; Mohammed, J.; Kallas, F. Predicting the thermal conductivity of Bi Te -based
                                                                                                   2
                                                                                                     3
                   thermoelectric energy materials: a machine learning approach. Int. J. Therm. Sci. 2022, 181, 107784.  DOI
               63.      Headley, C. V.; Herrera del Valle, R. J.; Ma, J.; et al. The development of an augmented machine learning approach for the additive
                   manufacturing of thermoelectric materials. J. Manuf. Processes. 2024, 116, 165-75.  DOI
               64.      Hou, Z.; Takagiwa, Y.; Shinohara, Y.; Xu, Y.; Tsuda, K. Machine-learning-assisted development and theoretical consideration for the
                   Al Fe Si  thermoelectric material. ACS. Appl. Mater. Interfaces. 2019, 11, 11545-54.  DOI  PubMed
                         3
                       3
                     2
               65.      Na, G. S. Artificial intelligence for learning material synthesis processes of thermoelectric materials. Chem. Mater. 2023, 35, 8272-80.
                   DOI
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