From Grotthuss Transfer to Conductivity: Machine Learning Molecular Dynamics of Aqueous KOH
V Jelle Lagerweij1, Sana Bougueroua2, Parsa Habibi1
1Engineering Thermodynamics, Process and Energy Department, Faculty of Mechanical Engineering, Delft University of Technology, Leeghwaterstraat 39, Delft 2628CB, The Netherlands.
The Journal of Physical Chemistry. B
|June 9, 2025
Summary
Machine learning molecular dynamics accurately predicts KOH(aq) conductivity by simulating hydroxide ion (OH-) Grotthuss transfer. This method overcomes limitations of classical simulations, providing quantitative insights into ion mobility and transport properties for electrolysis.
Area of Science:
- Physical Chemistry
- Computational Chemistry
- Materials Science
Background:
- Accurate prediction of potassium hydroxide (KOH) aqueous solutions' conductivity is vital for electrolysis.
- The Grotthuss transfer mechanism enhances hydroxide ion (OH-) mobility in water.
- Classical and ab initio molecular dynamics methods face computational and chemical reaction limitations in modeling this phenomenon.
Purpose of the Study:
- To quantitatively investigate the Grotthuss transfer mechanism of hydroxide ions in water using machine learning molecular dynamics.
- To determine the rate-limiting factors and transport properties associated with Grotthuss transfer.
- To provide accurate conductivity predictions for KOH(aq) relevant to electrolysis applications.
Main Methods:
- Employed machine learning molecular dynamics to simulate over 50,000 hydroxide ion transfer events.
- Analyzed hydrogen bond rearrangements during Grotthuss transfer to identify rate-limiting steps.
- Computed self-diffusion coefficients and electrical conductivities for quantitative comparison.
Main Results:
- Confirmed that Grotthuss transfer involves reduced accepted and increased donated hydrogen bonds to the hydroxide, indicating hydrogen bond rearrangement is rate-limiting.
- Achieved quantitative agreement between computed and experimental self-diffusion coefficients and electrical conductivities across a wide temperature range.
- Demonstrated superior performance compared to classical interatomic force fields and ab initio molecular dynamics simulations.
Conclusions:
- Machine learning molecular dynamics offers a computationally efficient and accurate approach to study ion transport mechanisms like Grotthuss transfer.
- The findings provide crucial quantitative data for optimizing electrolysis processes involving KOH(aq).
- This study advances the understanding of hydroxide ion mobility and its impact on electrolyte conductivity.
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