| 研究課題/領域番号 |
23K04890
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| 研究機関 | 広島大学 |
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研究代表者 |
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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| キーワード | ESDA / Density of states / DFT method / Machine learning / Support effect / Heterogeneous catalysis |
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| 研究実績の概要 |
Over the last year, my research has focused on introducing and promoting the efficacy of the Electronic Structure Decomposition Approach (ESDA).
-Conference Presentations: ESDA has been promoted via presentations at local and international conferences, where it was praised as ingenious by peers.
-Publications: I published a paper: “Predicting CO Interaction and Activation on Inhomogeneous Ru Nanoparticles Using Density Functional Theory Calculations and Machine Learning Models” in the Journal of Physical Chemistry C, a highly reputable journal.
Additionally, I have finished writing a new paper, soon to be submitted to ACS Nano, exploring ESDA's capabilities and applications.
-Research Impact: ESDA is being tested for various systems involving different catalyst materials and adsorbates.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
A lot of effort and time are being dedicated to making this research great.
Several peers have accepted the ESDA and have called the approach "genius."
I am currently writing a second paper to be submitted to a high-impact journal.
Lastly, the ESDA is being expanded to confirm its universality for different catalytic systems by changing the catalyst and the adsorbate.
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| 今後の研究の推進方策 |
The methodology of this research is being expanded and applied to various systems consisting of different catalysts and adsorbates.
We are focusing on the universal application of ESDA.
Challenges encountered include optimizing the methodology for diverse systems and integrating more complex machine learning algorithms.
To address these, we will collaborate with experts in related fields and acquire updated computational resources to enhance our analysis capabilities.
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| 次年度使用額が生じた理由 |
The funds remaining from FY2023 are strategically reserved to enhance the ESDA research and expand its impact as follows:
Funds will promote ESDA at major international conferences, which occur every 3 to 5 years, and support international collaborations by covering travel and project costs.
I am working on optimizing ESDA with advanced machine learning algorithms, requiring specialized software and collaboration with experts.
Additionally, funds will be used to purchase updated and powerful equipment for DFT calculations, essential for predicting catalytic properties.
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