The recent explosion of ML utilizations in quantum chemistry has shown that ML potentials can mimic potential energy surfaces within the chemical accuracy of QC methods. There are several ML techniques for regression tasks, such as artificial neural networks (NN), Gaussian process regression (GPR), and kernel ridge regression (KRR), each with their own strengths and weaknesses. The first task in creating an ML model is the choice of molecular representation for the studied system. Such representation should be invariant to transformations that do not change the particular property (translation, rotation, mirroring, or nuclear permutation). It should uniquely describe the system and be continuous and, ideally, differentiable. Commonly used representations for molecular systems are the Coulomb Matrix (CM), Bag of Bonds (BoB), Many-Body Tensor Representation (MBTR), Smooth Overlap of Atomic Positions (SOAP), 86 FCHL18/19, and those integrated in NN architectures such as SchNet and PaiNN. Several ML studies have already been conducted for atmospherically relevant molecular systems. M. Jääskeläinen showed that ML approaches are useful to improve cluster structure selection and sampling in general. NNs have been used to model large sulfuric acid-dimethylamine clusters and the NN potential ANI-2x has been benchmarked for small dimer clusters. KRR/GPR has been used to predict cluster binding energies, saturation vapor pressures of organic molecules, and chemical potentials of organic molecules in atmospherically relevant solutions. Our ML-oriented subpackage, JKML, offers an interface between the JKQC-constructed database files (e.g., those stored in ACDB 2.0) and two ML programs, quantum machine learning (QML ) and SchNetPack. In the procedure, XYZ coordinates are extracted and, together with the property of interest (e.g., electronic energy, forces, or mobility), stored in a database. Subsequently, JKML uses QML or SchNetPack to perform the training/validation/testing of the predicted property or its difference from a reference state. In the case of energies, these are atomization energies, or in the case of molecular clusters, binding energies