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

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Abstract

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 (Formula presented.) 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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Boattini, E., Ram, M., Smallenburg, F., & Filion, L. (2018). Neural-network-based order parameters for classification of binary hard-sphere crystal structures. Molecular Physics, 116(21–22), 3066–3075. https://doi.org/10.1080/00268976.2018.1483537

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