Erhard, Linus C. ; Rohrer, Jochen ; Albe, Karsten ; Deringer, Volker L. (2022)
A machine-learned interatomic potential for silica and its relation to empirical models.
In: npj Computational Materials, 8 (1)
doi: 10.1038/s41524-022-00768-w
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Silica (SiO 2) is an abundant material with a wide range of applications. Despite much progress, the atomistic modelling of the different forms of silica has remained a challenge. Here we show that by combining density-functional theory at the SCAN functional level with machine-learning-based interatomic potential fitting, a range of condensed phases of silica can be accurately described. We present a Gaussian approximation potential model that achieves high accuracy for the thermodynamic properties of the crystalline phases, and we compare its performance (and performance–cost trade-off) with that of multiple empirically fitted interatomic potentials for silica. We also include amorphous phases, assessing the ability of the potentials to describe structures of melt-quenched glassy silica, their energetic stability, and the high-pressure structural transition to a mainly sixfold-coordinated phase. We suggest that rather than standing on their own, machine-learned potentials for silica may be used in conjunction with suitable empirical models, each having a distinct role and complementing the other, by combining the advantages of the long simulation times afforded by empirical potentials and the near-quantum-mechanical accuracy of machine-learned potentials. This way, our work is expected to advance atomistic simulations of this key material and to benefit further computational studies in the field.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Erhard, Linus C. ; Rohrer, Jochen ; Albe, Karsten ; Deringer, Volker L. |
| Art des Eintrags: | Bibliographie |
| Titel: | A machine-learned interatomic potential for silica and its relation to empirical models |
| Sprache: | Englisch |
| Publikationsjahr: | 28 April 2022 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | npj Computational Materials |
| Jahrgang/Volume einer Zeitschrift: | 8 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.1038/s41524-022-00768-w |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Silica (SiO 2) is an abundant material with a wide range of applications. Despite much progress, the atomistic modelling of the different forms of silica has remained a challenge. Here we show that by combining density-functional theory at the SCAN functional level with machine-learning-based interatomic potential fitting, a range of condensed phases of silica can be accurately described. We present a Gaussian approximation potential model that achieves high accuracy for the thermodynamic properties of the crystalline phases, and we compare its performance (and performance–cost trade-off) with that of multiple empirically fitted interatomic potentials for silica. We also include amorphous phases, assessing the ability of the potentials to describe structures of melt-quenched glassy silica, their energetic stability, and the high-pressure structural transition to a mainly sixfold-coordinated phase. We suggest that rather than standing on their own, machine-learned potentials for silica may be used in conjunction with suitable empirical models, each having a distinct role and complementing the other, by combining the advantages of the long simulation times afforded by empirical potentials and the near-quantum-mechanical accuracy of machine-learned potentials. This way, our work is expected to advance atomistic simulations of this key material and to benefit further computational studies in the field. |
| Zusätzliche Informationen: | Artikel-ID: 90 |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Materialmodellierung Zentrale Einrichtungen Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) Zentrale Einrichtungen > Hochschulrechenzentrum (HRZ) > Hochleistungsrechner |
| TU-Projekte: | PTJ|03XP0174A|FestBatt-Daten DFG|RO4542/4-1|Interatomare Potenti DFG|STU611/5-1|Von interatomaren Po |
| Hinterlegungsdatum: | 09 Mai 2022 06:05 |
| Letzte Änderung: | 03 Jul 2024 02:57 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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A machine-learned interatomic potential for silica and its relation to empirical models. (deposited 07 Jun 2022 12:12)
- A machine-learned interatomic potential for silica and its relation to empirical models. (deposited 09 Mai 2022 06:05) [Gegenwärtig angezeigt]
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