Data and trained machine learning interatomic potentials supporting publication "Modeling the inner electric double layer using short-range machine learning potentials: A critical assessment"

Zenodo (CERN European Organization for Nuclear Research)

Abstract

Scripts, data and figure code for the benchmark of machine-learned interatomic potentials (DP, DP-MP, GRACE, MACE, eSEN-OC25) for Au/electrolyte interfaces. datasets/ training and test sets, DFT-MD reference, MD initial structures01_ ... 06_ one folder per experiment, in order of the manuscriptS1_ ... S3_ experiments in the SItools/ scripts shared by several experiments (DFT, LAMMPS, trajectory analysis) Experiment 03 is split into five tarballs: one folder for each model architecture, and one folder with scripts to regenerate the figures. Every folder has a README describing its contents.

Authors 5

1. Lucas B. T. de Kam Aachen

   Leiden University

   Affiliation as printed

   Leiden University

2. Jiaxin Zhu
3. Ankit Mathanker

   University of Michigan

   Affiliation as printed

   University of Michigan

4. Katharina Doblhoff-Dier Aachen

   Leiden University

   Affiliation as printed

   Leiden University

5. Nitish Govindarajan

   Nanyang Technological University

   Affiliation as printed

   Nanyang Technological University