Study on model inference technique based on statistical physics to STM dataset analysis
Project/Area Number |
15K13529
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Research Category |
Grant-in-Aid for Challenging Exploratory Research
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Allocation Type | Multi-year Fund |
Research Field |
Mathematical physics/Fundamental condensed matter physics
|
Research Institution | Tohoku University (2017) The University of Tokyo (2015-2016) |
Principal Investigator |
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Project Period (FY) |
2015-04-01 – 2018-03-31
|
Project Status |
Completed (Fiscal Year 2017)
|
Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2017: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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Keywords | 統計物理 / スパースモデリング / データ解析 / 情報統計力学 / データ駆動科学 / 走査型トンネル顕微鏡 / 金属酸化物薄膜 / 機械学習 |
Outline of Final Research Achievements |
We developed an algorithm to estimate the peak position that represents atoms appearing in STM topography data by using the sparse modeling approach. Our algorithm is applicable to the real material samples, for example, SrVO3 thin films. On top of that, we developed algorithms for investigating the effective interaction between the peaks from the experimental data, and for determining the species of atoms from STM topography datasets. We also developed a faster algorithm to calculate Leave-One-Out cross validation errors by using reweighting algorithm with Markov-chain Monte Carlo method.
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Report
(4 results)
Research Products
(16 results)