We used several databases of quantum chemistry (QC) data for typical atmospheric molecular clusters and showed that machine learning could easily substitute the computationally demanding QC calculations. Specifically, we used the polarizable atom interaction neural network (PaiNN) to model the cluster's binding energies, both with or without interatomic forces. We show that hyperparameter variation (e.g., reducing batch size) leads to faster converging NN training without compromising accuracy. We demonstrate that NNs do not outperform the accuracy of the KRR modeling by Kubečka et al., but the computational times for NNs are significantly lower. Similar to the case of KRR modeling, we find that ∆-learning improves the accuracy ∼4-fold. We use the energies and interatomic forces to train NN models for sulfuric acid-water clusters and sulfuric acid-ammonia clusters with quite reliable performance compared to the trained QC methods even when tested on larger structures excluded from the training: mean absolute errors of <0.26 kcal/mol for energies and <0.18 kcal/mol/ Å for force components, and root mean squared displacements of <0.34 kcal/mol and ∼0.30 kcal/mol/ Å, respectively.