1 Computing and Data Sciences, Brookhaven National Laboratory, Upton, NY 11973, USA

2 Department of Chemistry, Princeton University, Princeton, NJ 08544, USA

3 Department of Physics, Temple University, Philadelphia, PA 19122, USA

4 Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, NY 11973, USA

5 Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, NY 11973, USA

Publication date: Mar 11, 2025

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This database contains the neural network potential (NNP) model and training data for aqueous ZnCl₂ solutions from 1 m to 30 m. The NNP model can be used to compute total energies and atomic forces, with one of its major applications being large-scale molecular dynamics (MD) simulations. The model was trained using DeePMD-kit v2.2.1, with training data generated through an active learning approach implemented in DP-GEN. The energies and forces in the training set were obtained from density functional theory (DFT) calculations using the SCAN exchange-correlation functional performed using Quantum ESPRESSO. Further details on the ab initio calculation procedures and model training methodology are available in the associated manuscript (see reference below).

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