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

Peter Maxwell, Paul Popelier

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Number of pages12
    JournalStructural Chemistry
    Early online date28 Feb 2017
    Publication statusPublished - Oct 2017


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