Database of scalable training of neural network potentials for complex interfaces through data augmentation

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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>Yeu, In Won</dc:creator>
  <dc:creator>Stuke, Annika</dc:creator>
  <dc:creator>Urban, Alexander</dc:creator>
  <dc:creator>Artrith, Nongnuch</dc:creator>
  <dc:date>2025-04-02</dc:date>
  <dc:description>This database contains the reference data used for direct force training of Artificial Neural Network (ANN) interatomic potentials using the atomic energy network (ænet) and ænet-PyTorch packages (https://github.com/atomisticnet/aenet-PyTorch). It also includes the GPR-augmented data used for indirect force training via Gaussian Process Regression (GPR) surrogate models using the ænet-GPR package (https://github.com/atomisticnet/aenet-gpr).
Each data file contains atomic structures, energies, and atomic forces in XCrySDen Structure Format (XSF). The dataset includes all reference training/test data and corresponding GPR-augmented data used in the four benchmark examples presented in the reference paper, “Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation”.
A hierarchy of the dataset is described in the README.txt file, and an overview of the dataset is also summarized in supplementary Table S1 of the reference paper.</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2025.51</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:w6-9a</dc:identifier>
  <dc:identifier>mcid:2025.51</dc:identifier>
  <dc:identifier>oai:materialscloud.org:2619</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>first principles</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:subject>Li metal battery</dc:subject>
  <dc:subject>potential energy surface</dc:subject>
  <dc:subject>aenet</dc:subject>
  <dc:subject>data augmentation</dc:subject>
  <dc:title>Database of scalable training of neural network potentials for complex interfaces through data augmentation</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>