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A machine-learned interatomic potential for silica and its relation to empirical models

Erhard, Linus C. ; Rohrer, Jochen ; Albe, Karsten ; Deringer, Volker L. (2022):
A machine-learned interatomic potential for silica and its relation to empirical models. (Publisher's Version)
In: npj Computational Materials, 8, Springer, e-ISSN 2057-3960,
DOI: 10.26083/tuprints-00021424,
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Item Type: Article
Origin: Secondary publication via sponsored Golden Open Access
Status: Publisher's Version
Title: A machine-learned interatomic potential for silica and its relation to empirical models
Language: English
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.

Journal or Publication Title: npj Computational Materials
Volume of the journal: 8
Place of Publication: Darmstadt
Publisher: Springer
Collation: 12 Seiten
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 11 Department of Materials and Earth Sciences > Material Science
11 Department of Materials and Earth Sciences > Material Science > Materials Modelling
Date Deposited: 07 Jun 2022 12:12
Last Modified: 22 Aug 2022 08:09
DOI: 10.26083/tuprints-00021424
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-214241
Additional Information:

Keywords: Atomistic models, ceramics

URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21424
PPN: 49542403X
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