| Budget Amount *help |
¥45,110,000 (Direct Cost: ¥34,700,000、Indirect Cost: ¥10,410,000)
Fiscal Year 2022: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2021: ¥17,550,000 (Direct Cost: ¥13,500,000、Indirect Cost: ¥4,050,000)
Fiscal Year 2020: ¥10,010,000 (Direct Cost: ¥7,700,000、Indirect Cost: ¥2,310,000)
Fiscal Year 2019: ¥12,870,000 (Direct Cost: ¥9,900,000、Indirect Cost: ¥2,970,000)
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| Outline of Final Research Achievements |
In this study, we developed a method that combines electron energy loss spectroscopy (EELS) with advanced spectral calculations and machine learning to obtain high spatial resolution information on local atomic vibrations (related to heat and migration) in materials. We successfully performed experimental analysis of thermal properties and phase separation behavior in local regions under high temperatures. Additionally, the development of a spectral analysis method using machine learning enabled faster and more accurate analysis than conventional methods, and it was shown to be applicable for new property predictions. These achievements are expected to greatly contribute to the development of new heat-resistant and optical materials.
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