Accurate prediction of the energetics of weakly bound complexes using the machine learning method kriging

作者：Maxwell Peter I; Popelier Paul L A

来源：Structural Chemistry, 2017, 28(5): 1513-1523.

DOI：10.1007/s11224-017-0928-9

• QTAIM
• Quantum chemical topology (QCT)
• IQA
• Kriging
• Machine learning
• Force field development
• S22
• Complexes
• Dispersion

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, , and interatomic, , energies, which when summed, compose the molecular energy, . The and 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 ammoniaaEuro broken vertical bar benzene, wateraEuro broken vertical bar benzene, HCNaEuro broken vertical bar benzene, methaneaEuro broken vertical bar benzene, stacked-benzene (C (2h)) dimer and T-benzene (C (2v)) dimer complexes, respectively.

• 出版日期2017-10