Article Dans Une Revue Molecular Physics Année : 2018

Neural-network-based order parameters for classification of binary hard-sphere crystal structures

Résumé

Identifying crystalline structures is a common challenge in many types of research. Here, we focus on binary mixtures of hard spheres of various size ratios, which stabilise a range of crystal structures with varying complexity. We train feed-forward neural networks to distinguish different crystalline and fluid environments on a single-particle basis, by analysing vectors composed of several averaged local bond order parameters. For all size ratios considered, we achieve a classification accuracy above 98% for all phases, meaning that our method is completely general and able to capture structural differences of a wide range of binary crystals.

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hal-04414925 , version 1 (13-11-2024)

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Emanuele Boattini, Michel Ram, Frank Smallenburg, Laura Filion. Neural-network-based order parameters for classification of binary hard-sphere crystal structures. Molecular Physics, 2018, 116 (21-22), pp.3066-3075. ⟨10.1080/00268976.2018.1483537⟩. ⟨hal-04414925⟩
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