Accelerated Materials Discovery Based on Data Analysis and Machine Learning
- The stability of materials is a very important property for materials discovery. In recent years, we have explored several approaches for advancing the prediction of the stability of crystals (e.g., classical chemical heuristics and machine-learned interatomic potentials). For example, we have assessed a famous chemical heuristic – the Pauling rules – regarding their usefulness for structure prediction. They are only of limited predictive power, i.e. the rules 2 to 5 only work for 13 % of all tested oxides. Furthermore, we have shown that machine-learned interatomic potentials can be used to predict phonon properties of a range of silicon allotropes accurately and, therefore, they can also be used to assess the dynamic stability of materials. To do so, we have developed new schemes to build reference databases for machine learned interatomic potentials.
Autor*innen: | Janine GeorgeORCiD |
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Dokumenttyp: | Vortrag |
Veröffentlichungsform: | Präsentation |
Sprache: | Deutsch |
Jahr der Erstveröffentlichung: | 2021 |
Organisationseinheit der BAM: | 6 Materialchemie |
6 Materialchemie / 6.0 Abteilungsleitung und andere | |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
Freie Schlagwörter: | Data analysis; High-throughput computations; Machine learning |
Themenfelder/Aktivitätsfelder der BAM: | Material |
Material / Materialdesign | |
Veranstaltung: | PACRIM & GOMD 2021 |
Veranstaltungsort: | Online meeting |
Beginndatum der Veranstaltung: | 13.12.2021 |
Enddatum der Veranstaltung: | 16.12.2021 |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 20.12.2021 |
Referierte Publikation: | Nein |
Eingeladener Vortrag: | Nein |