Topological contribution to the split first peak of the liquid water structure factor using the MACE machine-learning potential
The splitting of the principal peak in the structure factor of liquid water is commonly interpreted as evidence of a competition between two distinct local environments. Here, we show that this peak splitting is captured by medium-range topological features of the hydrogen-bond network. Using atomistic simulations, we systematically decompose the structure factor into contributions from hydrogen-bonded rings of different sizes. We find that 5-8-membered rings, which dominate the network topology of liquid water at low temperatures, can directly explain the experimentally observed bimodal scattering signal. Among these, 5-membered rings are particularly persistent, maintaining distinct structural signatures even above room temperature. Our findings establish a direct link between the network topology of liquid water and experimentally accessible diffraction features and provide a topological perspective on the nature of the split first diffraction peak in tetrahedral materials.
Authors
- Fausto Martelli (ORCID: https://orcid.org/0000-0002-5350-8225)
- Zoé Faure Beaulieu (ORCID: https://orcid.org/0009-0009-7799-8669)
- Volker L. Deringer (ORCID: https://orcid.org/0000-0001-6873-0278)
Institutions
- University of Oxford (GB)
- Institute for Complex Systems (IT)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- The Journal of Chemical Physics
- Published
- 2026-08-20
- DOI
- https://doi.org/10.1063/5.0337533
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- UK Research and Innovation
- IBM Research