https://doi.org/10.1140/epjb/s10051-021-00140-9
Regular Article - Computational Methods
Determining neighborhood phases in hard-sphere systems using machine learning
Departamento de Física, UFSCar, Rodovia Washington Luiz, km 235, CP 676, 13565-905, Sao Carlos, SP, Brazil
Received:
15
February
2021
Accepted:
9
June
2021
Published online:
27
June
2021
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.
© The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2021
