Library of thermoelectric materials

Organic and hybrid thermoelectric materials can be designed using a large variety of constituents and processing conditions and further characterized regarding their thermoelectric performances using different experimental methods. Given the ever growing amount of experimental data available on the performances of such new TE materials, there is a stringent need to make a survey of existing literature as well as the results obtained during the ITN HORATES. A free-access library of TE materials has several important objectives. First, it can help the community to collect the important information necessary to select the most promising TE systems to design Thermoelectric generators. Second, the collected physical properties and associated data can be the starting point for machine learning -based investigations and selections of most promising TE systems. Similar approaches were recently followed for organic and perovskite-based materials used for solar cells design. (1-3)

As a starting point, the TE library is constituted of two xls files collecting the important information on both p-type and n-type materials. During the course of the ITN project, these xls files will gradually collect more data including inorganic TE systems (no database for inorganic TE materials exists so far). To access the library, you can click on the links to the two xls files that will be updated during the project. 

Link to p-type materials.

Link to n-type materials.


 (1) Tao, Q.; Xu, P.; Li, M.; Lu, W. Machine Learning for Perovskite Materials Design and Discovery. npj Computational Materials 2021, 7 (1), 23.

 (2) Jacobsson, T. J.; Hultqvist, A.; García-Fernández, A.; Anand, A.; Al-Ashouri, A.; Hagfeldt, A.; Crovetto, A.; Abate, A.; Ricciardulli, A. G.; Vijayan, A.; Kulkarni, A.; Anderson, A. Y.; Darwich, B. P.; Yang, B.; Coles, B. L.; Perini, C. A. R.; Rehermann, C.; Ramirez, D.; Fairen-Jimenez, D.; Di Girolamo, D.; Jia, D.; Avila, E.; Juarez-Perez, E. J.; Baumann, F.; Mathies, F.; González, G. S. A.; Boschloo, G.; Nasti, G.; Paramasivam, G.; Martínez-Denegri, G.; Näsström, H.; Michaels, H.; Köbler, H.; Wu, H.; Benesperi, I.; Dar, M. I.; Bayrak Pehlivan, I.; Gould, I. E.; Vagott, J. N.; Dagar, J.; Kettle, J.; Yang, J.; Li, J.; Smith, J. A.; Pascual, J.; Jerónimo-Rendón, J. J.; Montoya, J. F.; Correa-Baena, J.-P.; Qiu, J.; Wang, J.; Sveinbjörnsson, K.; Hirselandt, K.; Dey, K.; Frohna, K.; Mathies, L.; Castriotta, L. A.; Aldamasy, Mahmoud. H.; Vasquez-Montoya, M.; Ruiz-Preciado, M. A.; Flatken, M. A.; Khenkin, M. V.; Grischek, M.; Kedia, M.; Saliba, M.; Anaya, M.; Veldhoen, M.; Arora, N.; Shargaieva, O.; Maus, O.; Game, O. S.; Yudilevich, O.; Fassl, P.; Zhou, Q.; Betancur, R.; Munir, R.; Patidar, R.; Stranks, S. D.; Alam, S.; Kar, S.; Unold, T.; Abzieher, T.; Edvinsson, T.; David, T. W.; Paetzold, U. W.; Zia, W.; Fu, W.; Zuo, W.; Schröder, V. R. F.; Tress, W.; Zhang, X.; Chiang, Y.-H.; Iqbal, Z.; Xie, Z.; Unger, E. An Open-Access Database and Analysis Tool for Perovskite Solar Cells Based on the FAIR Data Principles. Nature Energy 2022, 7 (1), 107–115.

 (3) Sahu, H.; Rao, W.; Troisi, A.; Ma, H. Toward Predicting Efficiency of Organic Solar Cells via Machine Learning and Improved Descriptors. Advanced Energy Materials 2018, 8 (24), 1801032.