| Project/Area Number |
23K13078
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 13040:Biophysics, chemical physics and soft matter physics-related
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| Research Institution | Tohoku University |
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Principal Investigator |
Lieu Uyen 東北大学, 材料科学高等研究所, 助教(研究特任) (00807042)
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| Project Period (FY) |
2023-04-01 – 2026-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2025: ¥390,000 (Direct Cost: ¥300,000、Indirect Cost: ¥90,000)
Fiscal Year 2024: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2023: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | Reinforcement learning / Quasicrystal / Self-assembly / Patchy particles / sefl-assembly / patchy particles / inverse design / dynamic optimization |
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| Outline of Research at the Start |
Our research focuses on patchy particles, which are tiny particles with patches on their surface that can combine to form complex structures with unique properties. This research aims to create a method to optimize the design of these particles and control parameters so that they can dynamically assemble into a specific complex structure. By doing this, we hope to improve our under standing of how these structures form and to identify potential applications.
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| Outline of Annual Research Achievements |
Patchy particles are the particles of nanometer scale, have anisotropic interactions with other particles, and they can form complex structures such as dodecagonal quasicrystal (DDQC). However, it is difficult to control the DDQC structures at steady states because the growth of the DDQC involves several kinetic pathways.
We propose reinforcement learning to learn how to control the dynamical self-assembly of DDQC from patchy particles. In detail, we estimate the best policy of temperature control trained by the Q-learning method and demonstrate that we can generate dodecagonal quasicrystal structures using the estimated policy. The temperature schedule obtained by reinforcement learning can reproduce the desired structure more efficiently than the conventional pre-fixed temperature schedule, such as annealing.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
We have submitted one article and advertised our work by presentations in conferences.
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| Strategy for Future Research Activity |
The patchy particles are capable of organizing themselves into complex structures, which are important for the development of innovative materials. One of the complex structures is quasicrystal. The quasicrystal is interesting in both theoretical and application aspects. The quasicrystal can be applied in various application such as advanced coatings, reinforced composites, magnetism. We have applied reinforcement learning to identify the best parameter control for the growth of dodecagonal quasicrystals. However, aside from the structural configuration, the functions or properties of the obtained structure should be controlled as well because of the practical application. This kind of problem also occurs for other assembled structures.
In the future plan, we aim to apply reinforcement learning to control the properties of the assembled structure, thereby investigating the underlying relation between the structure and property. We also expect that by changing the external driving force, we can further control the functions or properties of these structures.
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