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  <doc>
    <id>62250</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>poster</type>
    <publisherName/>
    <publisherPlace/>
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    <completedDate>--</completedDate>
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    <title language="eng">Unveiling the potential of the Kanamori-Goodenough-Anderson rules for magnetic property prediction</title>
    <abstract language="eng">Recently, machine learning of magnetic properties of transition metal compounds has attracted large interest due to environmental and availability issues of rare-earth-based functional magnetic materials. Surprisingly, bond-angle-derived features were not found to be relevant for magnetic structure prediction in previous studies using DFT-computed labels. This contrasts with a well-known magnetism heuristic, the Kanamori-Goodenough-Anderson (KGA) rules of superexchange. We review magnetic interaction trends within the MAGNDATA database of experimentally determined magnetic structures. Observed trends follow the KGA rules „of thumb“ and exceptions can be rationalized. We introduce a new, informative label for predicting magnetic structures that can be extended to magnetic sites and structures of arbitrary complexity. Bond-angle-derived features are found to be highly relevant for magnetic structure prediction.</abstract>
    <enrichment key="eventName">Faraday Discussion: Data-driven discovery in the chemical sciences</enrichment>
    <enrichment key="eventPlace">Oxford, UK</enrichment>
    <enrichment key="eventStart">10.09.2024</enrichment>
    <enrichment key="eventEnd">12.09.2024</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Katharina Ueltzen</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetism</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>High-throughput analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transition metal compounds</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">6 Materialchemie</collection>
    <collection role="themenfelder" number="">Material</collection>
    <collection role="fulltextaccess" number="">Datei im Netzwerk der BAM verfügbar ("Closed Access")</collection>
    <collection role="literaturgattung" number="">Präsentation</collection>
    <collection role="institutes" number="">6.0 Abteilungsleitung und andere</collection>
    <collection role="themenfelder" number="">Materialdesign</collection>
    <collection role="institutes" number="">VP Vizepräsident</collection>
    <collection role="institutes" number="">VP.1 eScience</collection>
  </doc>
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