Prediction of 3D Cluster Energy Based on CatBoost Regression Model and Genetic Algorithm

Clusters, also known as ultra-fine clusters, belong to the scale concept of nanomaterials and represent the initial state of condensed matter. Cluster science is a very important research direction in the field of condensed matter physics. In this paper, for the gold cluster
$Au_{20}$
with known structural data, the energy prediction of different structures of
$Au_{20}$
is realized by establishing a CatBoost regression prediction model. At the same time, the L-J potential energy function is used to obtain the energy of different structures of
$Au_{32}$
. Finally, the global maximum between
$Au_{20}$
and
$Au_{32}$
is obtained by genetic algorithm.