  The use of machine learning (ML) in chemical physics has enabled the
construction of interatomic potentials having the accuracy of ab initio methods
and a computational cost comparable to that of classical force fields. Training
an ML model requires an efficient method for the generation of training data.
Here we apply an accurate and efficient protocol to collect training data for
constructing a neural network based ML interatomic potential for nanosilicate
clusters. Initial training data are taken from normal modes and farthest point
sampling. Later on, the set of training data is extended via an active learning
strategy in which new data are identified by the disagreement between an
ensemble of ML models. The whole process is further accelerated by parallel
sampling over structures. We use the ML model to run molecular dynamics (MD)
simulations of nanosilicate clusters with various sizes, from which infrared
spectra with anharmonicity included can be extracted. Such spectroscopic data
are needed for understanding the properties of silicate dust grains in the
interstellar medium (ISM) and in circumstellar environments.
