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    <id>64217</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>lecture</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">High-throughput Computational Screening With Chemical Heuristics, Workflows, and Machine Learning</title>
    <abstract language="eng">Within my talk, I have introduced the audience to high-throughput materials discovery with the help of workflow tools, machine learning and chemical heuristics. I started with a general introduction to the Materials Project software infrastructure and databases such as the Materials Project and NOMAD. Then, I dived into machine learning tasks relying on chemical bonding information, machine-learned interatomic potentials, and experimental data. Target properties of the machine-learning tasks were thermal properties, phonons, magnetism and synthesizability.</abstract>
    <enrichment key="eventName">Seminar at the Inorganic Chemistry Laboratory in Oxford</enrichment>
    <enrichment key="eventPlace">Oxford, United Kingdom</enrichment>
    <enrichment key="eventStart">25.09.2025</enrichment>
    <enrichment key="InvitedTalks">0</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Janine George</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>High-throughput</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sustainability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Materials Design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Thermal Conductivity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetism</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Synthesizability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Materials Acceleration Platform</value>
    </subject>
    <collection role="ddc" number="620">Ingenieurwissenschaften und zugeordnete Tätigkeiten</collection>
    <collection role="institutes" number="">6 Materialchemie</collection>
    <collection role="institutes" number="">6.6 Digitale 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="themenfelder" number="">Materialdesign</collection>
  </doc>
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