Regular Article

Gaussian mixture model for the unsupervised classification of AgCu nanoalloys based on the common neighbor analysis^★

Dipartimento di Fisica dell’Università di Genova, via Dodecaneso 33, Genova 16146, Italy

^* e-mail: roncaglia@fisica.unige.it

Received: 18 November 2021
Received in final form: 10 January 2022
Accepted: 11 January 2022
Published online: 1 March 2022

Abstract

In this short communication we describe the results obtained from the application of the Gaussian mixture model, a popular unsupervised learning algorithm, to some modified data sets gained after the global optimizations of three different AgCu nanoalloys. In particular we highlight both positive and negative aspects of such an approach to this kind of data. We show indeed that thanks to the Common Neighbor Analysis we are still able to describe nanoalloys well enough to exploit a physically meaningful separation in different structural families, even with a very low-dimensional representation. On the other hand, we show that the imposition of an energy cutoff over the data set is a delicate matter since it forces us to find a tradeoff between having a large set of data and having clean data.