TY - JOUR
T1 - Dynamics of Aqueous Electrolyte Solutions
T2 - Challenges for Simulations
AU - Panagiotopoulos, Athanassios Z.
AU - Yue, Shuwen
N1 - Funding Information:
Financial support for work on electrolyte solutions in the Panagiotopoulos group has been provided by the Office of Basic Energy Sciences, U.S. Department of Energy, under Award No. DE-SC0002128. The “Chemistry in Solution and at Interfaces” (CSI) Center that supports the machine-learning force field development is funded by the U.S. Department of Energy under Award DE-SC001934.
Publisher Copyright:
© 2023 American Chemical Society.
PY - 2023/1/19
Y1 - 2023/1/19
N2 - 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.
AB - 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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U2 - 10.1021/acs.jpcb.2c07477
DO - 10.1021/acs.jpcb.2c07477
M3 - Article
C2 - 36607836
AN - SCOPUS:85146003881
SN - 1520-6106
VL - 127
SP - 430
EP - 437
JO - Journal of Physical Chemistry B
JF - Journal of Physical Chemistry B
IS - 2
ER -