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第一原理計算による複合ペロブスカイト酸化物材料の定量設計

Research Project

Project/Area Number 14F04376
Research Category

Grant-in-Aid for JSPS Fellows

Allocation TypeSingle-year Grants
Section外国
Research Field Physical properties of metals/Metal-base materials
Research InstitutionKyoto University

Principal Investigator

田中 功  京都大学, 工学研究科, 教授 (70183861)

Co-Investigator(Kenkyū-buntansha) LEE JOOHWI  京都大学, 工学研究科, 外国人特別研究員
LEE Joohwi  京都大学, 工学(系)研究科(研究院), 外国人特別研究員
Project Period (FY) 2014-04-25 – 2017-03-31
Project Status Completed (Fiscal Year 2016)
Budget Amount *help
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 2016: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2015: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2014: ¥600,000 (Direct Cost: ¥600,000)
Keywordsペロブスカイト / 第一原理計算 / 電子構造 / バンドギャップ / 回帰手法
Outline of Annual Research Achievements

カチオン混合ペロブスカイト酸化物の第一原理計算による定量設計をめざし,主にATiO3(A=Ba, Sr, Ca, Pb)/LaAlO3を対象とした研究を進めている。これまでにSrTiO3/LaAlO3およびCaTiO3/LaAlO3ヘテロ界面について検討した。精確な原子配列,とくにTiO6八面体の傾角の電子構造への影響について的確に知ることは,これら物質の界面で生じることが期待される2次元電子ガス(2DEG)を設計するうえで不可欠である。これらの研究は,次世代の半導体デバイス開発という観点から,実用上極めて重要である。上記のテーマと並行して,このような電子材料の設計に密度汎関数(DFT)計算を利用する際に常に問題となるバンドギャップ過少評価問題についても検討を進めている。一つの解決策として,機械学習の方法を適用して,DFT計算結果を補正する方法を開発した。具体的には,GW法という高精度であるがコストの高い第一原理計算によって得られるバンドギャップ値を目的変数として,通常に広く用いられているGGA法(PBE)ならびにmBJという汎関数を利用したDFT計算で得られたバンドギャップに加え,構成元素固有の予測子,たとえば電気陰性度や価電子数,さらにそれらの組合せを用い,適切な予測子の選択手法と回帰手法を比較検討した。その結果,有効な特性予測モデルを構築し,フィジカルレビューB誌に発表した。この手法は,今後,さまざまな半導体材料を仮想スクリーニングする際に重要な役割を持つと期待される。

Research Progress Status

28年度が最終年度であるため、記入しない。

Strategy for Future Research Activity

28年度が最終年度であるため、記入しない。

Report

(3 results)
  • 2016 Annual Research Report
  • 2015 Annual Research Report
  • 2014 Annual Research Report
  • Research Products

    (10 results)

All 2016 2015

All Journal Article (2 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 2 results,  Acknowledgement Compliant: 1 results) Presentation (8 results) (of which Int'l Joint Research: 6 results)

  • [Journal Article] Estimation of band-gap of inorganic materials by density functional theory and machine2016

    • Author(s)
      J. Lee, A. Seko, K. Shitara, K. Nakayama, I. Tanaka
    • Journal Title

      AMTC Letters

      Volume: 5 Pages: 164-165

    • Related Report
      2016 Annual Research Report
    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] Predicgtion model of band gap for inorganic compounds by combination of density functional theory calculations and machine learning techniques2016

    • Author(s)
      Joohwi Lee, Atsuto Seko, Kazuki Shitara, Keita Nakayama, Isao Tanaka
    • Journal Title

      Phys. Rev. B.

      Volume: 93 Issue: 11

    • DOI

      10.1103/physrevb.93.115104

    • Related Report
      2015 Annual Research Report
    • Peer Reviewed / Acknowledgement Compliant
  • [Presentation] Efficient and accurate prediction of band gap of inorganic materials by density functional theory and machine learning techniques2016

    • Author(s)
      LEE, Joohwi
    • Organizer
      The Future of phsyics chemsitry
    • Place of Presentation
      Oxford, UK
    • Year and Date
      2016-08-30
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Band gap prediction of inorganic compounds by combination of density functional theory and machine learning techniques2016

    • Author(s)
      LEE, Joohwi
    • Organizer
      IUMRS-ICEM 2016
    • Place of Presentation
      Singapore
    • Year and Date
      2016-07-04
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Estimation of band-gap of inorganic materials by density functional theory and machine learning techniques2016

    • Author(s)
      LEE, Joohwi
    • Organizer
      The 5th International Symposium on Advanced Microscopy and Theoretical Calculations
    • Place of Presentation
      Nagoya, Japan
    • Year and Date
      2016-05-11
    • Related Report
      2016 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Band-gap prediction of binary and ternary compounds by combination of density functional theory and machine learning techniques2015

    • Author(s)
      Joohwi Lee
    • Organizer
      2nd International Symposium on Frontiers in Materials Science
    • Place of Presentation
      Tokyo, Japan
    • Year and Date
      2015-11-17
    • Related Report
      2015 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Band-gap prediction of AX binary compounds by combination of density functional theory and machine learning techniques2015

    • Author(s)
      Joohwi Lee
    • Organizer
      18th Asian workshop on first-principles electronic principles electronic structure calculations
    • Place of Presentation
      Chiba, Japan
    • Year and Date
      2015-11-09
    • Related Report
      2015 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Machine learning techniques on the correction of band-gaps of binary compounds obtained by systematic density functional theory calculations2015

    • Author(s)
      Joohwi Lee
    • Organizer
      Psi-k Conference 2015
    • Place of Presentation
      San Sebastian, Spain
    • Year and Date
      2015-09-06
    • Related Report
      2015 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Estimation of band-gaps of binary compounds using density functional theory calculations and machine learning techniques2015

    • Author(s)
      Joowhi Lee
    • Organizer
      CERAM/Psi-k
    • Place of Presentation
      Berlin, Germany
    • Year and Date
      2015-02-01 – 2015-02-05
    • Related Report
      2014 Annual Research Report
  • [Presentation] The bandgap estimation of binary compounds using the first-principles calculations and machine learning techniques2015

    • Author(s)
      Joowhi Lee
    • Organizer
      第53回セラミックス基礎科学検討会
    • Place of Presentation
      京都
    • Year and Date
      2015-01-08 – 2015-01-09
    • Related Report
      2014 Annual Research Report

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Published: 2015-01-22   Modified: 2024-03-26  

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