Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916716540329984 |
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| author | Forslund, Axel Jung, Jong Hyun Ikeda, Yuji Grabowski, Blazej |
| author_facet | Forslund, Axel Jung, Jong Hyun Ikeda, Yuji Grabowski, Blazej |
| contents | We propose a free-energy-perturbation approach accelerated by machine-learning potentials to efficiently compute transition temperatures and entropies for all rungs of Jacob's ladder. We apply the approach to the dynamically stabilized phases of SiO$_2$, which are characterized by challengingly small transition entropies. All investigated functionals from rungs 1-4 fail to predict an accurate transition temperature by 25-200%. Only by ascending to the fifth rung, within the random phase approximation, an accurate prediction is possible, giving a relative error of 5%. We provide a clear-cut procedure and relevant data to the community for, e.g., developing and evaluating new functionals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs Forslund, Axel Jung, Jong Hyun Ikeda, Yuji Grabowski, Blazej Materials Science We propose a free-energy-perturbation approach accelerated by machine-learning potentials to efficiently compute transition temperatures and entropies for all rungs of Jacob's ladder. We apply the approach to the dynamically stabilized phases of SiO$_2$, which are characterized by challengingly small transition entropies. All investigated functionals from rungs 1-4 fail to predict an accurate transition temperature by 25-200%. Only by ascending to the fifth rung, within the random phase approximation, an accurate prediction is possible, giving a relative error of 5%. We provide a clear-cut procedure and relevant data to the community for, e.g., developing and evaluating new functionals. |
| title | Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs |
| topic | Materials Science |
| url | https://arxiv.org/abs/2505.00789 |