Part 1. Raw data associated with the publication "Machine-learning enhanced simulations predict graphene is microscopically hydrophobic and not wetting transparent"

This repository contains the essential computational datasets and input files associated with the research article: "Machine-learning enhanced simulations predict graphene is hydrophobic and microscopically not wetting transparent" Contents: Machine Learning Potentials: Training and test datasets, and the final Atomic Cluster Expansion (ACE) potential files. Simulation Setup: Molecular dynamics (MD) input scripts (LAMMPS format) and initial atomic structure files. Trajectory Logs: Time-series data from NVE simulations, including total energy, temperature, and extrapolation grade values. Usage: These files enable the reproduction of the molecular dynamics simulations and the validation of the machine learning potentials described in the manuscript. Citation: If you use this data or the ACE potential in your work, please cite the associated publication: https://doi.org/10.1038/s41467-026-71053-3