Development and demonstration of a material structure exploration method based on data assimilation of first-principles calculations and experiments
| Project/Area Number |
17H02930
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Mathematical physics/Fundamental condensed matter physics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Tsuneyuki Shinji 東京大学, 大学院理学系研究科(理学部), 教授 (90197749)
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| Co-Investigator(Kenkyū-buntansha) |
藤堂 眞治 東京大学, 大学院理学系研究科(理学部), 教授 (10291337)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥19,240,000 (Direct Cost: ¥14,800,000、Indirect Cost: ¥4,440,000)
Fiscal Year 2019: ¥5,590,000 (Direct Cost: ¥4,300,000、Indirect Cost: ¥1,290,000)
Fiscal Year 2018: ¥6,500,000 (Direct Cost: ¥5,000,000、Indirect Cost: ¥1,500,000)
Fiscal Year 2017: ¥7,150,000 (Direct Cost: ¥5,500,000、Indirect Cost: ¥1,650,000)
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| Keywords | 物質構造探索 / データ同化 / 第一原理計算 / 結晶構造解析 / 物質構造探査 |
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| Outline of Final Research Achievements |
Combining first-principles calculations with data science methods, we have developed a data-assimilative structure exploration method that enables us to analyze the crystal structure of materials using measured data. We defined a penalty function based on the difference between the experimental X-ray powder diffraction data and the powder diffraction data calculated from the atomic positions, and added this penalty function to the potential energy function calculated for the atomic positions to obtain a cost function. By searching for the global minimum of this cost function, we have realized a structure search that takes advantage of both first-principles calculations, which are good at predicting local structures and properties, and experiments, which are good at obtaining data in wavenumber space reflecting order.
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| Academic Significance and Societal Importance of the Research Achievements |
物質の化学組成から結晶構造を予測することは、物質科学における重要かつ極めて困難な課題である。本研究では、実験データだけでは結晶構造決定ができない場合、その不十分な実験データを利用して結晶構造シミュレーションを大幅に加速し結晶構造探索を可能にする、データ同化手法を開発した。これにより、従来捨てられていたような実験データを活かして、新物質の結晶構造を解析することが可能となり、新結晶・新物質の探索を加速することができる。
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Report
(4 results)
Research Products
(27 results)
