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
Article, Secondary publication, Publisher's Version
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Item Type: | Article |
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Type of entry: | Secondary publication |
Title: | A machine-learned interatomic potential for silica and its relation to empirical models |
Language: | English |
Date: | 7 June 2022 |
Place of Publication: | Darmstadt |
Year of primary publication: | 2022 |
Publisher: | Springer |
Journal or Publication Title: | npj Computational Materials |
Volume of the journal: | 8 |
Collation: | 12 Seiten |
DOI: | 10.26083/tuprints-00021424 |
Corresponding Links: | |
Origin: | Secondary publication via sponsored Golden Open Access |
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: | Publisher's Version |
URN: | urn:nbn:de:tuda-tuprints-214241 |
Additional Information: | Keywords: Atomistic models, ceramics |
Classification DDC: | 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering |
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: | 18 Nov 2024 19:01 |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/21424 |
PPN: | 49542403X |
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