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May 22, 2026ACS Applied Materials & InterfacesOpen Access

How Silica Surface Chemistry Modulates Interfacial Water: Insights from Machine Learning Molecular Dynamics

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Authors

CPC. Huy PhamMBMargaret L. BerrensMAMarcos F. Calegari Andrade

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Overview

Randomized trial demonstrates changes in water dynamics at silica interfaces, suggesting new design strategies.

Key Points

  • This work aims to control water structure and dynamics at silica interfaces using machine learning techniques.
  • Developed a machine learning interatomic potential for water between silica surfaces.
  • Trained via active learning to achieve ab initio accuracy for varying silanol coverages and slit widths.
  • Analyzed translational and rotational dynamics of water under different surface chemistries.
  • 75% OH coverage leads to stronger hydrogen bonding and extended density profiles compared to 100% OH coverage.
  • Translational diffusion decreases linearly with slit width and OH coverage, while rotational dynamics exhibit nonlinearity.
  • 50% OH coverage does not significantly alter water structure or dynamics.

Cite This Study

Pham et al. (2026) studied this question.

synapsesocial.com/papers/6a0ff2f5d674f7c03778b765https://doi.org/10.1021/acsami.6c00590
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Analysis of the Effects of Silica Surface Structure on the Local Diffusion and Hydrogen Bonding of Water via Molecular Dynamics2026
  2. 2Molecular dynamics study on the effect of surface ionization on the interfacial heat transfer between silica and water2024 · 9 citations
  3. 3In Situ Characterization of the Hydration Structure at the Silica-Water Interface.2026 · 1 citations
  4. 4Tailoring Heat Transfer at Silica–Water Interfaces via Hydroxyl and Methyl Surface Groups2025
  5. 5Tailoring Heat Transfer at Silica–Water Interfaces via Hydroxyl and Methyl Surface Groups2025