期刊
JOURNAL OF PHYSICAL CHEMISTRY B
卷 127, 期 2, 页码 430-437出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcb.2c07477
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This article summarizes recent simulation work on the dynamics of aqueous electrolytes. It shows that full-charge, nonpolarizable models for water and ions predict solution dynamics that are too slow, while models with reduced charges have issues describing certain dynamic phenomena. Polarizable models, when appropriately parametrized, show promise but may miss important physical effects. First-principles calculations are emerging to capture polarization, charge transfer, and chemical transformations in solution. Machine-learning models trained on first-principles data offer promise for accurate and transferable modeling of electrolyte solution dynamics.
This Perspective article focuses on recent simulation work on the dynamics of aqueous electrolytes. It is well-established that full-charge, nonpolarizable models for water and ions generally predict solution dynamics that are too slow in comparison to experiments. Models with reduced (scaled) charges do better for solution diffusivities and viscosities but encounter issues describing other dynamic phenomena such as nucleation rates of crystals from solution. Polarizable models show promise, especially when appropriately parametrized, but may still miss important physical effects such as charge transfer. First-principles calculations are starting to emerge for these properties that are in principle able to capture polarization, charge transfer, and chemical transformations in solution. While direct ab initio simulations are still too slow for simulations of large systems over long time scales, machine-learning models trained on appropriate first-principles data show significant promise for accurate and transferable modeling of electrolyte solution dynamics.
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