  Neural network potentials are a powerful tool for atomistic simulations,
allowing to accurately reproduce \textit{ab initio} potential energy surfaces
with computational performance approaching classical force fields. A central
component of such potentials is the transformation of atomic positions into a
set of atomic features in a most efficient and informative way.In this work, a
feature selection method is introduced for high dimensional neural network
potentials, based on the Adaptive Group Lasso (AGL) approach. It is shown that
the use of an embedded method, taking into account the interplay between
features and their action in the estimator, is necessary to optimize the number
of features. The method's efficiency is tested on three different monoatomic
systems, including Lennard-Jones as a simple test case, Aluminium as a system
characterized by predominantly radial interactions, and Boron as representative
of a system with strongly directional interactions. The AGL is compared with
unsupervised filter methods and found to perform consistently better in
reducing the number of features needed to reproduce the reference simulation
data. {In particular, our results show the importance of taking into account
model predictions in feature selection for interatomic potentials.
