Machine learning supported annealing for prediction of grand canonical crystal structures
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916349762076672 |
|---|---|
| author | Couzinie, Yannick Seki, Yuya Nishiya, Yusuke Nishi, Hirofumi Kosugi, Taichi Tanaka, Shu Matsushita, Yu-ichiro |
| author_facet | Couzinie, Yannick Seki, Yuya Nishiya, Yusuke Nishi, Hirofumi Kosugi, Taichi Tanaka, Shu Matsushita, Yu-ichiro |
| contents | This study investigates the application of Factorization Machines with Quantum Annealing (FMQA) to address the crystal structure problem (CSP) in materials science. FMQA is a black-box optimization algorithm that combines machine learning with annealing machines to find samples to a black-box function that minimize a given loss. The CSP involves determining the optimal arrangement of atoms in a material based on its chemical composition, a critical challenge in materials science. We explore FMQA's ability to efficiently sample optimal crystal configurations by setting the loss function to the energy of the crystal configuration as given by a predefined interatomic potential. Further we investigate how well the energies of the various metastable configurations, or local minima of the potential, are learned by the algorithm. Our investigation reveals FMQA's potential in quick ground state sampling and in recovering relational order between local minima. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03556 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning supported annealing for prediction of grand canonical crystal structures Couzinie, Yannick Seki, Yuya Nishiya, Yusuke Nishi, Hirofumi Kosugi, Taichi Tanaka, Shu Matsushita, Yu-ichiro Materials Science Computational Physics This study investigates the application of Factorization Machines with Quantum Annealing (FMQA) to address the crystal structure problem (CSP) in materials science. FMQA is a black-box optimization algorithm that combines machine learning with annealing machines to find samples to a black-box function that minimize a given loss. The CSP involves determining the optimal arrangement of atoms in a material based on its chemical composition, a critical challenge in materials science. We explore FMQA's ability to efficiently sample optimal crystal configurations by setting the loss function to the energy of the crystal configuration as given by a predefined interatomic potential. Further we investigate how well the energies of the various metastable configurations, or local minima of the potential, are learned by the algorithm. Our investigation reveals FMQA's potential in quick ground state sampling and in recovering relational order between local minima. |
| title | Machine learning supported annealing for prediction of grand canonical crystal structures |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2408.03556 |