Structure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929735624294400 |
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| author | Töpfer, Kai Wang, Jingchun Patel, Shimoni Meuwly, Markus |
| author_facet | Töpfer, Kai Wang, Jingchun Patel, Shimoni Meuwly, Markus |
| contents | Generating energy functions for heterogeneous systems suitable for quantitative and predictive atomistic simulations is a challenging undertaking. The present work combines a cluster-based approach with electronic structure calculations at the density functional theory level and machine learning-based energy functions for a spectroscopic reporter for eutectic mixtures consisting of water, acetamide and KSCN. Two water models are considered: TIP3P which is consistent with the CGenFF energy function and TIP4P which - as a water model - is superior to TIP4P. Both fitted models, {\bf M2$^{\rm TIP3P}$} and {\bf
M2$^{\rm TIP4P}$}, yield favourable thermodynamic, structural, spectroscopic and transport properties from extensive molecular dynamics simulations. In particular, the slow and fast decay times from 2-dimensional infrared spectroscopy and the viscosity for water-rich mixtures are described realistically and consistent with experiments. On the other hand, including the co-solvent (acetamide) in the present case is expected to further improve the computed viscosity for low-water content. It is concluded that such a cluster-based approach is a promising and generalizable route for routine parametrization of heterogeneous, electrostatically dominated systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions Töpfer, Kai Wang, Jingchun Patel, Shimoni Meuwly, Markus Chemical Physics Generating energy functions for heterogeneous systems suitable for quantitative and predictive atomistic simulations is a challenging undertaking. The present work combines a cluster-based approach with electronic structure calculations at the density functional theory level and machine learning-based energy functions for a spectroscopic reporter for eutectic mixtures consisting of water, acetamide and KSCN. Two water models are considered: TIP3P which is consistent with the CGenFF energy function and TIP4P which - as a water model - is superior to TIP4P. Both fitted models, {\bf M2$^{\rm TIP3P}$} and {\bf M2$^{\rm TIP4P}$}, yield favourable thermodynamic, structural, spectroscopic and transport properties from extensive molecular dynamics simulations. In particular, the slow and fast decay times from 2-dimensional infrared spectroscopy and the viscosity for water-rich mixtures are described realistically and consistent with experiments. On the other hand, including the co-solvent (acetamide) in the present case is expected to further improve the computed viscosity for low-water content. It is concluded that such a cluster-based approach is a promising and generalizable route for routine parametrization of heterogeneous, electrostatically dominated systems. |
| title | Structure and Dynamics of Deep Eutectic Systems from Cluster-Optimized Energy Functions |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2502.21233 |