Heat transport in superionic materials via machine-learned molecular dynamics
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhou, Wenjiang Tang, Benrui Fan, Zheyong Grasselli, Federico Baroni, Stefano Song, Bai |
| author_facet | Zhou, Wenjiang Tang, Benrui Fan, Zheyong Grasselli, Federico Baroni, Stefano Song, Bai |
| contents | Precise modeling and understanding of heat transport in the superionic phase are of great interest. Although simulations combining Green-Kubo (GK) molecular dynamics with machine-learned potentials (MLPs) stand as a promising approach, substantial challenges remain due to the crucial impact of atomic diffusion. Here, we first show that the thermal conductivity ($κ$) of superionic materials calculated via conventional GK integral of the energy flux varies notably with the MLP model. Subsequently, we highlight that reliable, model-independent $κ$ values can be obtained by applying Onsager's reciprocal relations to correctly capture the coupled heat and mass transport. Remarkably, an anomalously invariant $κ$ can be observed over a wide temperature range, distinct from the characteristic trends in traditional crystals and glasses. In addition, we illustrate that conventional $κ$ decompositions into kinetic, potential, and cross terms suffer from ambiguities in the physical interpretation, despite their mathematical rigor. Finally, we propose a criterion for the necessity of the Onsager correction and reveal the underlying mechanism as a competition between thermally and chemically driven ion fluxes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04718 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Heat transport in superionic materials via machine-learned molecular dynamics Zhou, Wenjiang Tang, Benrui Fan, Zheyong Grasselli, Federico Baroni, Stefano Song, Bai Materials Science Precise modeling and understanding of heat transport in the superionic phase are of great interest. Although simulations combining Green-Kubo (GK) molecular dynamics with machine-learned potentials (MLPs) stand as a promising approach, substantial challenges remain due to the crucial impact of atomic diffusion. Here, we first show that the thermal conductivity ($κ$) of superionic materials calculated via conventional GK integral of the energy flux varies notably with the MLP model. Subsequently, we highlight that reliable, model-independent $κ$ values can be obtained by applying Onsager's reciprocal relations to correctly capture the coupled heat and mass transport. Remarkably, an anomalously invariant $κ$ can be observed over a wide temperature range, distinct from the characteristic trends in traditional crystals and glasses. In addition, we illustrate that conventional $κ$ decompositions into kinetic, potential, and cross terms suffer from ambiguities in the physical interpretation, despite their mathematical rigor. Finally, we propose a criterion for the necessity of the Onsager correction and reveal the underlying mechanism as a competition between thermally and chemically driven ion fluxes. |
| title | Heat transport in superionic materials via machine-learned molecular dynamics |
| topic | Materials Science |
| url | https://arxiv.org/abs/2512.04718 |