Intramolecular polarisable multipolar electrostatics from the machine learning method Kriging

Matthew J L Mills, Paul L A Popelier

    Research output: Contribution to journalArticlepeer-review


    We describe an intramolecularly polarisable multipolar electrostatic potential model for ethanol, which acts as a pilot molecule for this proof-of-concept study. We define atoms via the partitioning prescribed by quantum chemical topology (QCT). A machine learning method called Kriging is employed to capture the way atomic multipole moments vary upon conformational change. The multipole moments predicted by the Kriging models are used in the calculation of atom-atom electrostatic interaction energies. Charge transfer is treated in the same way as dipolar polarisation and the polarisation of higher rank multipole moments. This method enables the development of a new and more accurate force field. © 2011 Elsevier B.V.
    Original languageEnglish
    Pages (from-to)42-51
    Number of pages9
    JournalComputational and Theoretical Chemistry
    Issue number1-3
    Publication statusPublished - 15 Nov 2011


    • Atoms in molecules
    • Force field
    • Machine learning
    • Multipole moment
    • Polarisation
    • Quantum chemical topology


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