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Data-driven chemical understanding
- Chemical heuristics are essential to understanding molecules and materials in chemistry. The periodic table, atomic radii, and electronegativities are only a few examples. Initially, they have been developed by a combination of physical insight and a limited amount of data. It is now possible to test these heuristics and generate new ones using automation based on Materials Informatic tools like pymatgen and greater amounts of data from databases such as a Materials Project. In this session, I'll speak about heuristics and design rules based on coordination environments and the concept of chemical bonding. For example, we have tested the Pauling rules which describe the stability of materials based on coordination environments and their connections on 5000 oxides from the Materials Project. In addition, we have created automated processes for analyzing the chemical bonding situation in crystalline materials with Lobster (www.cohp.de) in order to discover new heuristics and designChemical heuristics are essential to understanding molecules and materials in chemistry. The periodic table, atomic radii, and electronegativities are only a few examples. Initially, they have been developed by a combination of physical insight and a limited amount of data. It is now possible to test these heuristics and generate new ones using automation based on Materials Informatic tools like pymatgen and greater amounts of data from databases such as a Materials Project. In this session, I'll speak about heuristics and design rules based on coordination environments and the concept of chemical bonding. For example, we have tested the Pauling rules which describe the stability of materials based on coordination environments and their connections on 5000 oxides from the Materials Project. In addition, we have created automated processes for analyzing the chemical bonding situation in crystalline materials with Lobster (www.cohp.de) in order to discover new heuristics and design rules.…
Autor*innen: | Janine GeorgeORCiD |
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Dokumenttyp: | Vortrag |
Veröffentlichungsform: | Präsentation |
Sprache: | Englisch |
Jahr der Erstveröffentlichung: | 2022 |
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: | Chemical heuristics; Crystal Orbital Hamilton Populations; DFT; Machine learning; Phonons |
Themenfelder/Aktivitätsfelder der BAM: | Material |
Material / Materialdesign | |
Veranstaltung: | Materials Project Seminar Series |
Veranstaltungsort: | Online meeting |
Beginndatum der Veranstaltung: | 18.05.2022 |
URL: | https://www.youtube.com/watch?v=e7zYrz6fgog |
URL: | https://next-gen.materialsproject.org/community/seminar |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 14.06.2022 |
Referierte Publikation: | Nein |
Eingeladener Vortrag: | Nein |