Erhard, Linus C. ; Rohrer, Jochen ; Albe, Karsten ; Deringer, Volker L. (2024)
Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning.
In: Nature Communications, 15 (1)
doi: 10.1038/s41467-024-45840-9
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Silicon–oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si–O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si–O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Erhard, Linus C. ; Rohrer, Jochen ; Albe, Karsten ; Deringer, Volker L. |
| Art des Eintrags: | Bibliographie |
| Titel: | Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning |
| Sprache: | Englisch |
| Publikationsjahr: | 2 März 2024 |
| Verlag: | Springer Nature |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Nature Communications |
| Jahrgang/Volume einer Zeitschrift: | 15 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.1038/s41467-024-45840-9 |
| Kurzbeschreibung (Abstract): | Silicon–oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si–O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si–O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning. |
| Freie Schlagworte: | AL578/29-1 |
| ID-Nummer: | Artikel-ID: 1927 |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Materialmodellierung Zentrale Einrichtungen Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) |
| TU-Projekte: | PTJ|03XP0174A|FestBatt-Daten DFG|RO4542/4-1|Interatomare Potenti DFG|STU611/5-1|Von interatomaren Po |
| Hinterlegungsdatum: | 27 Mai 2024 06:34 |
| Letzte Änderung: | 27 Mai 2024 11:45 |
| PPN: | 518631567 |
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| Suche nach Titel in: | TUfind oder in Google |
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