Dynamically Polarizable Water Potential Based on Multipole Moments Trained by Machine Learning.

By C. M. Handley, Paul L. A. Popelier. 2009

65 citations recorded by the source corpus.

It is widely accepted that correctly accounting for polarization within simulations involving water is critical if the structural, dynamic, and thermodynamic properties of such systems are to be accurately reproduced. We propose a novel potential for the water dimer, trimer, tetramer, pentamer, and hexamer that includes polarization explicitly, for use in molecular dynamics simulations. Using thousands of dimer, trimer, tetramer, pentamer, and hexamer clusters sampled from a molecular dynamics simulation lacking polarization, we train (artificial) neural networks (NNs) to predict the atomic multipole moments of a central water molecule. The input of the neural nets consists solely of the coordinates of the water molecules surrounding the central water. The multipole moments are calculated by the atomic partitioning defined by quantum chemical topology (QCT). This method gives a dynamic multipolar representation of the water electron density without explicit polarizabilities. Instead, the required knowledge is stored in the neural net. Furthermore, there is no need to perform iterative calculations to self-consistency during the simulation nor is there a need include damping terms in order to avoid a polarization catastrophe.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry

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Citations in the corpus

Machine-learned potentials for next-generation matter simulations (2021, methodology): “To improve this description 56 , machine learning can be used to predict environment-dependent point charges or molecular dipole/multipole moments 57–59 .”