Accurate Prediction of the Energetics of Weakly Bound Complexes Using the Machine Learning Method Kriging

Here, we extend the system energy prediction approach used in the force field FFLUX (Maxwell et al. Theor Chem Acc 135:195, 2016) to complexes bound by weak intermolecular interactions. The investigation features the first application of the approach to bound complex systems, additionally challenged by investigating complexes held together only weakly, through either a predominant dispersion contribution, or through mixed dispersion and hydrogen-bonding. Our approach uses the interacting quantum atoms (IQA) energy partitioning scheme to obtain the intra-atomic, EAintraEintraA , and interatomic, VAA'interVinterAA' , energies, which when summed, compose the molecular energy, EsystemIQAEIQAsystem . The EAintraEintraA and VAA'interVinterAA' energies are mapped to the positions of the nuclear coordinates through the machine learning method kriging to build atomic energy models. A model’s quality is established through its ability to accurately predict the atomic and molecular energies of atoms in an external test set. Mean absolute error percentages (MAE%) of 1.5, 1.5, 1.6, 1.0, 2.6 and 1.7% are obtained in recovering the molecular energy for ammonia…benzene, water…benzene, HCN…benzene, methane…benzene, stacked-benzene (C2h) dimer and T-benzene (C2v) dimer complexes, respectively.