Machine learning calculates pKa of hydrofluoric acid in deep Earth conditions, suggesting enhanced element mobility.
Fluorine is a key component in many hydrothermal ore-forming fluids, commonly occurring as fluorite and in minerals like apatite, fluorite, and mica. The availability of fluoride ions (F-) for forming metal-fluoride complexes is governed by the equilibrium thermodynamics of hydrofluoric acid HF(aq). However, obtaining reliable thermodynamic properties of HF(aq) at high-pressure and high-temperature (high P-T) conditions, typical of the Earth’s crust and upper mantle, has proven challenging due to limitations in experimental techniques. This study employs an automated machine learning molecular dynamics (MLMD) approach that is trained on ab initio data to calculate the pKa of HF(aq) across a wide range of temperatures (150°C to 600°C) and pressures (500 bar to 20 kbar). The calculated pKa values were then fitted into the Ryzhenko–Bryzgalin thermodynamics model with density dependence to derive properties that can predict the role of HF(aq) in controlling element mobility and pH in deep Earth fluids. These results suggest that HF(aq) is a stronger acid than previously estimated, particularly under supercritical conditions, leading to enhanced dissociation and increased availability of fluoride ions (F⁻), which may improve the solubility and transport of metals such as rare earth elements (REEs) and uranium.
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Guan et al. (2025) studied this question.
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