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Determining neighborhood phases in hard-sphere systems using machine learning

  • Regular Article - Computational Methods
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Abstract

A challenging problem in particle-based modeling is one of classifying the many structures which involve very large networks of bonds. Based on capacity to judge if a system is amorphous or solid from radial distribution functions, we set up two machine-learning systems able to identify local structures in mono-component hard-sphere simulations. The machines are constituted of logistic or support-vector regressions applied to linear model, second- and third-degree polynomial hypothesis. We labeled the sphere as solid or amorphous following a bond-order parameter and characterized them with radial structure functions. The features were enough to machine-learning systems predicting the labels with great accuracy.

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Data Availability Statement

All data generated or analysed during this study are included in this published article. [Authors’ comment: The paper provides all information to reproduce the data or figures shown. There are no datasets generated during this current study.]

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Acknowledgements

The authors acknowledge the CENAPAD-SP (Centro Nacional de Processamento de Alto Desempenho em São Paulo) for providing HPC resources - project UNICAMP/FINEP - MCT.

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  1. Departamento de Física, UFSCar, Rodovia Washington Luiz, km 235, CP 676, Sao Carlos, SP, 13565-905, Brazil

    J. V. Quentino & P. A. F. P. Moreira

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  1. J. V. Quentino
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  2. P. A. F. P. Moreira
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Correspondence to P. A. F. P. Moreira.

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Quentino, J.V., Moreira, P.A.F.P. Determining neighborhood phases in hard-sphere systems using machine learning. Eur. Phys. J. B 94, 130 (2021). https://doi.org/10.1140/epjb/s10051-021-00140-9

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