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Determining neighborhood phases in hard-sphere systems using machine learning

Author

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  • J. V. Quentino

    (Departamento de Física, UFSCar)

  • P. A. F. P. Moreira

    (Departamento de Física, UFSCar)

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. Graphic abstract

Suggested Citation

  • J. V. Quentino & P. A. F. P. Moreira, 2021. "Determining neighborhood phases in hard-sphere systems using machine learning," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(6), pages 1-9, June.
  • Handle: RePEc:spr:eurphb:v:94:y:2021:i:6:d:10.1140_epjb_s10051-021-00140-9
    DOI: 10.1140/epjb/s10051-021-00140-9
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