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High-dimensional order parameters and neural network classifiers applied to amorphous ices.

Authors :
Faure Beaulieu, Zoé
Deringer, Volker L.
Martelli, Fausto
Source :
Journal of Chemical Physics. 2/28/2024, Vol. 160 Issue 8, p1-6. 6p.
Publication Year :
2024

Abstract

Amorphous ice phases are key constituents of water's complex structural landscape. This study investigates the polyamorphic nature of water, focusing on the complexities within low-density amorphous ice (LDA), high-density amorphous ice, and the recently discovered medium-density amorphous ice (MDA). We use rotationally invariant, high-dimensional order parameters to capture a wide spectrum of local symmetries for the characterization of local oxygen environments. We train a neural network to classify these local environments and investigate the distinctiveness of MDA within the structural landscape of amorphous ice. Our results highlight the difficulty in accurately differentiating MDA from LDA due to structural similarities. Beyond water, our methodology can be applied to investigate the structural properties and phases of disordered materials. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
*ICE
*LANDSCAPES
*SYMMETRY
*OXYGEN

Details

Language :
English
ISSN :
00219606
Volume :
160
Issue :
8
Database :
Academic Search Index
Journal :
Journal of Chemical Physics
Publication Type :
Academic Journal
Accession number :
175757107
Full Text :
https://doi.org/10.1063/5.0193340