Development of a seebeck coefficient prediction simulator using tight-binding quantum chemical molecular dynamics

Hideyuki Tsuboi, Kei Ogiya, Arunabhiram Chutia, Zhigang Zhu, Chen Lv, Ai Suzuki, Riadh Sahnoun, Michihisa Koyama, Nozomu Hatakeyama, Akira Endou, Hiromitsu Takaba, Momoji Kubo, Carlos A. Del Carpio, Akira Miyamoto

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)

    Abstract

    Technologies oriented to the development of thermoelectric materials are of great interest because they can assist in directly tapping the vast reserves of currently underused thermal energy. To improve the rational design of high-performance thermoelectric materials, a new numerical procedure for prediction of Seebeck coefficient based on the electronic information from tight-binding quantum chemical molecular dynamics method, has been developed. The newly developed simulator can evaluate Seebeck coefficient theoretically. The simulator is used for the prediction of the Seebeck coefficients for Pt and Si that are the representative materials for metals and semiconductors, respectively. The results show that both coefficients are in quantitative agreement with experimental data, i.e. in the case of Pt metal the predicted value is 5.62 μV/K while the experimental is -4.45 μV/K. Similarly, in the case of Si semiconductor the predicted value is -324.48 μV/K while the experimental is 300-400 μV/K. This new developed simulator can be used to guide the rational design of high performance thermoelectric materials.

    Original languageEnglish
    Pages (from-to)3134-3137
    Number of pages4
    JournalJapanese Journal of Applied Physics
    Volume47
    Issue number4 PART 2
    DOIs
    Publication statusPublished - Apr 25 2008

    All Science Journal Classification (ASJC) codes

    • Engineering(all)
    • Physics and Astronomy(all)

    Fingerprint Dive into the research topics of 'Development of a seebeck coefficient prediction simulator using tight-binding quantum chemical molecular dynamics'. Together they form a unique fingerprint.

    Cite this