From Grotthuss Transfer to Conductivity: Machine Learning Molecular Dynamics of Aqueous KOH
V.J. Lagerweij (TU Delft - Engineering Thermodynamics)
Sana Bougueroua (Université Paris-Saclay)
Parsa Habibi (TU Delft - Engineering Thermodynamics, TU Delft - Team Poulumi Dey)
Poulumi Dey (TU Delft - Team Poulumi Dey)
Marie Pierre Gaigeot (Université Paris-Saclay)
Othon Moultos (TU Delft - Engineering Thermodynamics)
Thijs J. H. Vlugt (TU Delft - Engineering Thermodynamics)
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Abstract
Accurate conductivity predictions of KOH(aq) are crucial for electrolysis applications. OH– is transferred in water by the Grotthuss transfer mechanism, thereby increasing its mobility compared to that of other ions. Classical and ab initio molecular dynamics struggle to capture this enhanced mobility due to limitations in computational costs or in capturing chemical reactions. Most studies to date have provided only qualitative descriptions of the structure during Grotthuss transfer, without quantitative results for the transfer rate and the resulting transport properties. Here, machine learning molecular dynamics is used to investigate 50,000 transfer events. Analysis confirmed earlier works that Grotthuss transfer requires a reduction in accepted and a slight increase in donated hydrogen bonds to the hydroxide, indicating that hydrogen-bond rearrangements are rate-limiting. The computed self-diffusion coefficients and electrical conductivities are consistent with experiments for a wide temperature range, outperforming classical interatomic force fields and earlier AIMD simulations.
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