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, 2022, 8
doi: 10.26083/tuprints-00021424
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Silica (SiO₂) 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: | Zweitveröffentlichung |
| Titel: | A machine-learned interatomic potential for silica and its relation to empirical models |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | npj Computational Materials |
| Jahrgang/Volume einer Zeitschrift: | 8 |
| Kollation: | 12 Seiten |
| DOI: | 10.26083/tuprints-00021424 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21424 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung aus gefördertem Golden Open Access |
| Kurzbeschreibung (Abstract): | Silica (SiO₂) 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. |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-214241 |
| Zusätzliche Informationen: | Keywords: Atomistic models, ceramics |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Materialmodellierung |
| Hinterlegungsdatum: | 07 Jun 2022 12:12 |
| Letzte Änderung: | 08 Jun 2022 05:53 |
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| Suche nach Titel in: | TUfind oder in Google |
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- A machine-learned interatomic potential for silica and its relation to empirical models. (deposited 07 Jun 2022 12:12) [Gegenwärtig angezeigt]
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