Modeling Intermolecular Interactions With XDM Dispersion Corrections to Neural Network Potentials

07 May 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Neural network potentials (NNPs) are an innovative approach for calculating the potential energy and forces of a chemical system. In principle, these methods are capable of modeling large systems with an accuracy approaching that of a high-level ab initio calculation but with a much smaller computational cost. Due to their training to density-functional theory (DFT) data and neglect of long-range interactions, some classes of NNPs require an additional term to include London dispersion physics. In this perspective, we discuss the requirements for a dispersion model for use with an NNP, focusing on the MLXDM (Machine Learned eXchange Hole Dipole Moment) model developed by our groups. This model is based on the DFT-based XDM dispersion correction, which calculates interatomic dispersion coefficients in terms of atomic moments and polarizabilities, both of which can be effectively approximated using neural networks.


London dispersion

Supplementary materials

Supporting information
Technical details of calculations and additional figures


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