2026-07-21T07:25:22Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:2589 2025-03-11T15:10:51Z community-mcarchive openaire_data

Cao, Chuntian Lu, Deyu Cao, Chuntian Zhang, Chunyi Wu, Xifan Carbone, Matthew R. van Dam, Hubertus Yoo, Shinjae Lu, Deyu 2025-03-11 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). application/zip text/plain text/markdown https://doi.org/10.24435/materialscloud:xb-4f oai:materialscloud.org:2589 mcid:2025.37 eng Materials Cloud https://doi.org/10.1103/PRXEnergy.4.023004 https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:9g-02 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Neural network potential ZnCl2 solution water DeePMD water in salt Resolving the solvation structure and transport properties of aqueous zinc electrolytes from salt-in-water to water-in-salt using neural network potential info:eu-repo/semantics/other