A series of GPR models are presented herein, which differ in the type of representation and in how the covariance matrix is constructed. The most straightforward of these uses a representation of the molecular geometry of the lowest-energy conformer in the neutral charge state. This representation x (i) s is constructed in two steps. First, each atomic environment is encoded into a rotationally invariant local representation using the smooth overlap of atomic positions (SOAP) as implemented in Dscribe 49 (see Fig. for details). These atomic representations are then combined into molecular representations using the auto-bag method , which partitions the local feature vectors into k max clusters using the k-means algorithm. Each molecular structure can then be encoded by a k maxdimensional global feature vector that counts the occurrence of local environments that are assigned to each cluster. The effect of the hyperparameter k max on the predictive performance is shown in Fig. , arriving at a converged value of 500.