Resolving the solvation structure and transport properties of aqueous zinc electrolytes from salt-in-water to water-in-salt using neural network potential

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Cao, Chuntian</dc:creator>
  <dc:creator>Zhang, Chunyi</dc:creator>
  <dc:creator>Wu, Xifan</dc:creator>
  <dc:creator>Carbone, Matthew R.</dc:creator>
  <dc:creator>van Dam, Hubertus</dc:creator>
  <dc:creator>Yoo, Shinjae</dc:creator>
  <dc:creator>Lu, Deyu</dc:creator>
  <dc:date>2025-03-11</dc:date>
  <dc:description>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).</dc:description>
  <dc:identifier>https://materialscloud-archive-failover.cineca.it/record/2025.37</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:xb-4f</dc:identifier>
  <dc:identifier>mcid:2025.37</dc:identifier>
  <dc:identifier>oai:materialscloud.org:2589</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Neural network potential</dc:subject>
  <dc:subject>ZnCl2 solution</dc:subject>
  <dc:subject>water</dc:subject>
  <dc:subject>DeePMD</dc:subject>
  <dc:subject>water in salt</dc:subject>
  <dc:title>Resolving the solvation structure and transport properties of aqueous zinc electrolytes from salt-in-water to water-in-salt using neural network potential</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>