Zitieren Sie bitte immer diesen URN: urn:nbn:de:kobv:b43-582892
A Quantum-Chemical Bonding Database for Solid-State Materials
- An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model forAn in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.…
Autor*innen: | Aakash NaikORCiD, Christina Ertural, Nidal Dhamrait, Philipp Benner, Janine GeorgeORCiD |
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Dokumenttyp: | Zeitschriftenartikel |
Veröffentlichungsform: | Verlagsliteratur |
Sprache: | Englisch |
Titel des übergeordneten Werkes (Englisch): | Scientific Data |
Jahr der Erstveröffentlichung: | 2023 |
Organisationseinheit der BAM: | 6 Materialchemie |
6 Materialchemie / 6.0 Abteilungsleitung und andere | |
Veröffentlichende Institution: | Bundesanstalt für Materialforschung und -prüfung (BAM) |
Jahrgang/Band: | 10 |
Ausgabe/Heft: | 1 |
Aufsatznummer: | 610 |
Erste Seite: | 1 |
Letzte Seite: | 18 |
DDC-Klassifikation: | Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten |
Freie Schlagwörter: | Bonding Analysis; DFT; Database; High-throughput; Machine Learning; Phonons |
Themenfelder/Aktivitätsfelder der BAM: | Material |
Material / Materialdesign | |
DOI: | 10.1038/s41597-023-02477-5 |
URN: | urn:nbn:de:kobv:b43-582892 |
Verfügbarkeit des Dokuments: | Datei für die Öffentlichkeit verfügbar ("Open Access") |
Lizenz (Deutsch): | Creative Commons - CC BY - Namensnennung 4.0 International |
Datum der Freischaltung: | 18.09.2023 |
Referierte Publikation: | Ja |
Datum der Eintragung als referierte Publikation: | 18.09.2023 |
Schriftenreihen ohne Nummerierung: | Wissenschaftliche Artikel der BAM |