| 研究課題/領域番号 |
21K05003
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分32020:機能物性化学関連
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| 研究機関 | 京都大学 |
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研究代表者 |
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| 研究期間 (年度) |
2021-04-01 – 2024-03-31
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| 研究課題ステータス |
完了 (2023年度)
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| 配分額 *注記 |
4,290千円 (直接経費: 3,300千円、間接経費: 990千円)
2023年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2022年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2021年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
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| キーワード | Quantum annealing / Self-assembly / Surface / Molecule / First-principles / Machine learning / Quantum Monte Carlo / Monte Carlo / self-assembly / surface / simulation / quantum annealing / porphryin / phthalocyanine / 量子アニーリング / 分子自己組織化 / 材料設計 / 表面 / 計算材料化学 |
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| 研究開始時の研究の概要 |
This project will develop a computational method based on quantum annealing for predicting how molecules self-assemble on surfaces. This computational method will be designed for future quantum technologies, providing a “基盤” for a future nanomaterials discovery.
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| 研究実績の概要 |
During FY2022, we created a quantum annealing algorithm for simulating the assembly of surface-adsorbed molecules. During FY2023, we carried on this work as follows: (i) creation of a realistic intermolecular potential for the case of porphyrin molecules adsorbed to a (100) surface, using density functional theory and machine learning; (ii) programming of a quantum Monte Carlo (QMC) algorithm to predict the molecular assembly; (iii) extensive numerical simulations to evaluate QMC performance. It was confirmed that the QMC algorithm performs poorly compared to classical parallel tempering Monte Carlo over a variety of parameter regimes.
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