Seebeck coefficient of ionic conductors from Bayesian regression analysis
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917684103348224 |
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| author | Drigo, Enrico Baroni, Stefano Pegolo, Paolo |
| author_facet | Drigo, Enrico Baroni, Stefano Pegolo, Paolo |
| contents | We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the probability distribution of the off-diagonal elements of a Wishart matrix, we develop a consistent and unbiased estimator for the Seebeck coefficient whose statistical uncertainty can be arbitrarily reduced in the long-time limit. To validate the effectiveness of our method, we benchmark it against extensive equilibrium molecular dynamics simulations conducted on molten $\mathrm{CsF}$ using empirical force fields. We then employ this procedure to calculate the Seebeck coefficient of molten $\mathrm{NaCl}$, $\mathrm{KCl}$ and $\mathrm{LiCl}$ using neural-network force fields trained on ab initio data over a range of pressure-temperature conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04873 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Seebeck coefficient of ionic conductors from Bayesian regression analysis Drigo, Enrico Baroni, Stefano Pegolo, Paolo Materials Science We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the probability distribution of the off-diagonal elements of a Wishart matrix, we develop a consistent and unbiased estimator for the Seebeck coefficient whose statistical uncertainty can be arbitrarily reduced in the long-time limit. To validate the effectiveness of our method, we benchmark it against extensive equilibrium molecular dynamics simulations conducted on molten $\mathrm{CsF}$ using empirical force fields. We then employ this procedure to calculate the Seebeck coefficient of molten $\mathrm{NaCl}$, $\mathrm{KCl}$ and $\mathrm{LiCl}$ using neural-network force fields trained on ab initio data over a range of pressure-temperature conditions. |
| title | Seebeck coefficient of ionic conductors from Bayesian regression analysis |
| topic | Materials Science |
| url | https://arxiv.org/abs/2402.04873 |