2023 Fiscal Year Annual Research Report
Quantum Annealing for Functional Molecular Assemblies
| Project/Area Number |
21K05003
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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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| Keywords | Quantum annealing / Self-assembly / Surface / Molecule / First-principles / Machine learning / Quantum Monte Carlo / Monte Carlo |
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| Outline of Annual Research Achievements |
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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