Unsupervised landmark analysis for jump detection in molecular dynamics simulations


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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>Kahle, Leonid</dc:creator>
  <dc:creator>Musaelian, Albert</dc:creator>
  <dc:creator>Marzari, Nicola</dc:creator>
  <dc:creator>Kozinsky, Boris</dc:creator>
  <dc:date>2019-02-12</dc:date>
  <dc:description>Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states of the system's free energy surface. However, the analysis of trajectories for relevant but rare events, such as a jump of the diffusing mobile ion, is still rather cumbersome, requiring prior knowledge of the diffusive process in order to get meaningful results. In this work we present a novel approach to detect the relevant events in a diffusive system without assuming prior information regarding the underlying process. We start from a projection of the atomic coordinates into a landmark basis to identify the dominant features in a mobile ion's environment. Subsequent clustering in landmark space enables a discretization of any trajectory into a sequence of distinct states. As a final step, the use of the Smooth Overlap of Atomic Positions descriptor allows distinguishing between different environments in a straightforward way. We apply this algorithm to ten Li-ionic systems and conduct in-depth analyses of cubic Li7La3Zr2O12, tetragonal Li10GeP2S12, and the β-eucryptite LiAlSiO4. We compare our results to existing methods, underscoring strong points, weaknesses, and insights into the diffusive behavior of the ionic conduction in the materials investigated. </dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2019.0008/v1</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:2019.0008/v1</dc:identifier>
  <dc:identifier>mcid:2019.0008/v1</dc:identifier>
  <dc:identifier>oai:materialscloud.org:102</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>molecular dynamics</dc:subject>
  <dc:subject>site analysis</dc:subject>
  <dc:subject>tracer diffusion</dc:subject>
  <dc:title>Unsupervised landmark analysis for jump detection in molecular dynamics simulations</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>