ArticleThe journal of physical chemistry letters2025
Mechanism of Fe(II) Chemisorption on Hematite(001) Revealed by Reactive Neural Network Potential Molecular Dynamics.
Article in The journal of physical chemistry letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Transition from Vehicular to Structural Ionic Transport in Electrified Alkali Aqueous Solutions.The journal of physical chemistry. B · 2026Article
- Kinetics of hydrogen migration and string of conformation transformation for pyruvic acid dimer under external electric field.Journal of molecular modeling · 2025Article
- Kinetics of Proton Transfer and String of Conformational Transformation for 4-Pyridone-3-carboxylic Acid Under External Electric Field.Molecules (Basel, Switzerland) · 2025Article
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Abstract
Atomic-scale understanding of important geochemical processes including sorption, dissolution, nucleation, and crystal growth is difficult to obtain from experimental measurements alone and would benefit from strong continuous progress in molecular simulation. To this end, we present a reactive neural network potential-based molecular dynamics approach to simulate the interaction of aqueous ions on mineral surfaces in contact with liquid water, taking Fe(II) on hematite(001) as a model system. We show that a single neural network potential predicts rate constants for water exchange for aqueous Fe(II) and for the exergonic chemisorption of aqueous Fe(II) on hematite(001) in good agreement with experimental observations. The neural network potential developed herein allows one to converge free energy profiles and transmission coefficients at density functional theory-level accuracy outperforming state-of-the-art classical force field potentials. This suggests that machine learning potential molecular dynamics should become the method of choice for atomistic studies of geochemical processes.
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