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

Year: 2026

Type: dataset

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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18865584

Authors: de Kam, Lucas B.T., Zhu, Jia-Xin, Mathanker, Ankit, Doblhoff-Dier, Katharina, Govindarajan, Nitish

Institutions: Leiden University, University of Michigan, Nanyang Technological University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7225096277

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