Part 2. Raw data associated with the publication "Machine-learning enhanced simulations predict graphene is microscopically hydrophobic and not wetting transparent"

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Authors: Dianwei Hou, Yevhen Horbatenko, Stefan Ringe, Minhaeng Cho
Cited by: 0

Abstract

This repository contains the extensive raw data derived from molecular dynamics trajectories, supporting the geometric and structural analyses presented in the research article: "Machine-learning enhanced simulations predict graphene is hydrophobic and microscopically not wetting transparent" Contents: Water Orientation Data: The water orientation angles Phi and Sigma. Atomic Coordinates: Z-coordinates of oxygen and hydrogen atoms relative to the interface, used for atomic probability density analysis Usage: These datasets correspond to the geometric analysis figures (e.g., angular distributions) presented in the main text and Supplementary Information, as well as the atomic probability density analysis provided in the Supplementary Information. Citation: If you use this data in your research, please cite the associated publication: https://doi.org/10.1038/s41467-026-71053-3

Fields of Study & Subjects

Graphene research and applications Surface Modification and Superhydrophobicity Machine Learning in Materials Science

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
3 February 2026
Document type
Dataset