Determining neighborhood phases in hard-sphere systems using machine learning

J. V. Quentino and P. A. F. P. Moreira ()
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J. V. Quentino: Departamento de Física, UFSCar
P. A. F. P. Moreira: Departamento de Física, UFSCar

The European Physical Journal B: Condensed Matter and Complex Systems, 2021, vol. 94, issue 6, 1-9

Abstract: 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

Date: 2021
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DOI: 10.1140/epjb/s10051-021-00140-9

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