Expanding density-correlation machine learning representations for anisotropic coarse-grained particles

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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>Lin, Arthur</dc:creator>
  <dc:creator>Huguenin-Dumittan, Kevin</dc:creator>
  <dc:creator>Cho, Yong-Cheol</dc:creator>
  <dc:creator>Nigam, Jigyasa</dc:creator>
  <dc:creator>Cersonsky, Rose</dc:creator>
  <dc:date>2024-08-14</dc:date>
  <dc:description>This record contains three datasets and the scripts used to generate figures in "Expanding density-correlation machine learning representations for anisotropic coarse-grained particles." This paper explores the theory and implementation of machine-learning descriptors for ellipsoidal bodies, extending the popular "Smooth Overlap of Atomic Positions" (SOAP) formalism. These case studies serve to demonstrate the different use cases of this technology.  The three datasets are:
- Generated configurations of nematic and smectic liquid crystal systems, with a range of orientational order, characterized by the nematic order parameter
- Dimers of (1, 1.5, 2) ellipsoids at different interaction cutoffs and rotations, with computed Gay-Berne type energies
- Crystalline configurations of planar benzene molecules, with energetics computed  using QuantumEspresso v7.046 using Perdew–Burke–Ernzerhof (PBE) pseudopotentials and cutoff parameters reported by Prandini et al.,  Grimme D3-dispersion correction, and a 3 × 3 Monkhorst–Pack k-point grid.</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2024.123</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:mk-vn</dc:identifier>
  <dc:identifier>mcid:2024.123</dc:identifier>
  <dc:identifier>oai:materialscloud.org:2302</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>machine learning</dc:subject>
  <dc:subject>Quantum ESPRESSO</dc:subject>
  <dc:subject>benzene</dc:subject>
  <dc:subject>liquid crystals</dc:subject>
  <dc:subject>methods</dc:subject>
  <dc:title>Expanding density-correlation machine learning representations for anisotropic coarse-grained particles</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>