| 研究課題/領域番号 |
21K05003
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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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| キーワード | self-assembly / surface / simulation / quantum annealing / porphryin |
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| 研究実績の概要 |
Computational methods for predicting the assembly of surface-adsorbed molecules will accelerate breakthroughs in nanotechnology. During FY2022, we developed a quantum annealing method to predict the orientations of porphyrin molecules adsorbed to a gold surface. This system involves realistic, density functional theory-calculated intermolecular and surface-molecule potentials, but is also simple enough to be expressed as an Ising model. It is therefore a relevant target for developing quantum annealing algorithm. With this method, we succeeded to correctly predict the orientations of the molecules within a few minutes of computational time on a laptop, thereby confirming the ability of quantum annealing to be applied to molecular self-assembly phenomena.
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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
We have succeeded to implement quantum annealing for a realistic molecular system, which was the FY2022 goal of the original proposal. However, since submitting the original proposal, we have also developed a new genetic algorithm method for predicting molecular self-assembly as well. This method looks like it can be combined with quantum annealing to yield a novel classical-quantum prediction algorithm. For this reason, the research plan has changed slightly from the original proposal, although quantum annealing still features as the major element.
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| 今後の研究の推進方策 |
In addition to quantum annealing, we have also developed a novel genetic algorithm for predicting molecular self-assembly called 'evolution under fire' (Adv. Phys. Res. 1, 2022, 2200019). This method is very effective at predicting the positions of the molecules on the surface. On the other hand, quantum annealing looks very effective at predicting the orientations of the molecules for fixed molecule positions.
For FY2023, we will therefore combine both methods, yielding a new type of classical-quantum algorithm. We will characterise the performance of the algorithm in detail, and compare it carefully with an exclusively genetic algorithm approach. To finish the project, we will also explore the possibility of running our algorithm on a real quantum annealer.
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| 次年度使用額が生じた理由 |
For FY2022, we purchased some crystal structure prediction software (687764 yen), from which we can form reasonable guesses for the orientations of the molecules on the surface and check whether our simulation results are reasonable. With the remaining money (412,236 yen), we plan to purchase access to test our code on a real quantum annealer in FY2023. Note that we could not perform such testing in FY2022, as our code is not yet finished.
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| 備考 |
Seminar given at Victoria University of Wellington / MacDiarmid Institute on February 16 2023.
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