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

Pham, Cong Huy; Berrens, Margaret L.; Calegari Andrade, Marcos F.; Goldman, Nir; Esposito, Daniel V.; Ogitsu, Tadashi; Pham, Tuan Anh

doi:10.1021/acsami.6c00590

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

Journal Article · Tue May 19 00:00:00 UTC 2026 · ACS Applied Materials and Interfaces

DOI: https://doi.org/10.1021/acsami.6c00590 · OSTI ID:3375114

^[1]; Berrens, Margaret L. ^[1];

^[2];

^[3];

^[4]; Ogitsu, Tadashi ^[1];

^[1]

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
University of California, Santa Cruz, CA (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); University of California, Davis, CA (United States)
Columbia University, New York, NY (United States)

Controlling water structure and dynamics at silica interfaces are central to a wide range of technologies, including protective oxide layers for solar water splitting and nanoporous membranes. In this work, we develop a machine learning interatomic potential, trained via active learning, to achieve ab initio accuracy for water confined between hydroxylated silica surfaces over a range of silanol coverages and slit widths. We find that partially hydroxylated surfaces (50 and 75% OH) support stronger water−surface hydrogen bonding and more extended interfacial density profiles than fully hydroxylated (100% OH) surfaces, indicating that increasing OH coverage does not necessarily strengthen interfacial hydrogenbond networks. Translational diffusion decreases approximately linearly with slit width and OH coverage, whereas rotational dynamics respond nonlinearly. In particular, at the smallest slit width of 5 Å, 75% OH coverage produces an enhanced local tetrahedral ordered interfacial network that strongly suppresses reorientation, while 100% coverage yields a crowded, disordered interfacial layer that also hinders rotation. In contrast, the 50% OH coverage is sufficiently sparse that it does not markedly alter water structure or dynamics under confinement. These results show that coupled control of pore size and surface chemistry enables nonlinear tuning of interfacial water structure and transport, providing a design strategy for optimizing porous silica for either enhanced interfacial stability and controlled reactivity or rapid and selective transport.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC52-07NA27344; SC0023431

OSTI ID:: 3375114

Report Number(s):: LLNL-JRNL-2020406

Journal Information:: ACS Applied Materials and Interfaces, Journal Name: ACS Applied Materials and Interfaces Journal Issue: 21 Vol. 18; ISSN 1944-8244; ISSN 1944-8252

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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Title: How Silica Surface Chemistry Modulates Interfacial Water: Insights from Machine Learning Molecular Dynamics

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