| Autor: |
Drigo E; SISSA─Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy., Baroni S; SISSA─Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy.; CNR-IOM─Istituto Officina Materiali, DEMOCRITOS SISSA Unit, 34136 Trieste, Italy., Pegolo P; SISSA─Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of chemical theory and computation [J Chem Theory Comput] 2024 Jul 23; Vol. 20 (14), pp. 6152-6159. Date of Electronic Publication: 2024 Jun 10. |
| DOI: |
10.1021/acs.jctc.4c00124 |
| Abstrakt: |
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. We assess the efficacy of our method by benchmarking it against extensive equilibrium molecular dynamics simulations conducted on molten CsF using empirical force fields. We then employ this procedure to calculate the Seebeck coefficient of molten NaCl, KCl, and LiCl using neural network force fields trained on ab initio data over a range of pressure-temperature conditions. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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