2023 Fiscal Year Annual Research Report
Quantum Annealing for Functional Molecular Assemblies
| Project/Area Number |
21K05003
|
|---|
| Research Institution | Kyoto University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2021-04-01 – 2024-03-31
|
| Keywords | Quantum annealing / Self-assembly / Surface / Molecule / First-principles / Machine learning / Quantum Monte Carlo / Monte Carlo |
|---|
| 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.
|
Research Products
(4 results)
