Effect of patchy particle designs on the bulk properties of the self-assembled structures
| Project/Area Number |
20K14437
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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 (2021-2023)
National Institute of Advanced Industrial Science and Technology (2020) |
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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) |
2020-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2021: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | Reinforcement Learning / Quasicrystal / Self-assembly / self-assembly / patchy particles / dodecagonal quasicrystal / self-assemble / inverse design / optimisation / patchy particle / kagome lattice / curvature / topological defects |
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| Outline of Research at the Start |
Patchy particles are of micrometre size and have patches on the surface. Such particles can assemble into complex structures with novel properties. In this research, we perform numerical simulation to investigate the relation of patchy particle design, assembled structures and properties.
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| Outline of Annual Research Achievements |
We propose reinforcement learning to control the dynamical self-assembly of the dodecagonal quasicrystal from patchy particles. The patchy particles have anisotropic interactions with other particles and form DDQC. Their structures at steady states are significantly influenced by the kinetic pathways of their structural formation.
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.
In the future plan, applying reinforcement learning to control the properties of the assembled structure is a possible direction to investigate the underlying relation between the structure and property.
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Report
(4 results)
Research Products
(15 results)
