| Project/Area Number |
21K05003
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 32020:Functional solid state chemistry-related
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2023: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2022: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | Quantum annealing / Self-assembly / Monte Carlo / Adsorption / First-princples / Genetic algorthm / Molecular / Surface / Molecule / First-principles / Machine learning / Quantum 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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| Outline of Research at the Start |
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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| Outline of Final Research Achievements |
The goal of this project was to implement our on-surface molecular self-assembly simulations on a quantum annealer, an emerging type of quantum hardware. We succeeded to develop a simple model for surface-adsorbed molecules which can be mapped to an Ising-type Hamiltonian. Using first-principles calculations, we showed how this model closely approximates a realistic system of gold(100)-adsorbed porphyrin molecules. Quantum annealing was successfully implemented using the quantum Monte Carlo method, and consistently found the ground state for the surface-adsorbed molecules for all regimes tested. However, we found no evidence for the superiority of quantum annealing compared to classical annealing in our simulations.
In addition, this work developed Evolution Under Fire, a highly effective classical algorithm for predicting on-surface molecular assembly. The codes in this work were also used to develop databases and machine learning methods for organic semiconducting materials.
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| Academic Significance and Societal Importance of the Research Achievements |
Quantum computing has undergone impressive developments in recent years. It is believed that simulations of molecular systems will be possible using quantum computers within this decade. This work provides an algorithm for simulating molecular self-assembly processes on emerging quantum hardware.
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