PIL-Net: a physics-informed graph convolutional network for predicting atomic multipoles

doi:10.1039/D5DD00228A

PIL-Net: a physics-informed graph convolutional network for predicting atomic multipoles

2025-07-26

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DOI：10.1039/D5DD00228A

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We introduce PIL-Net; a physics-informed graph convolutional network capable of predicting molecular properties quickly and with low error; using only basic information about each molecule's atoms and bonds. The PIL-Net model combines the representational power of graph neural networks with domain knowledge to predefine a set of constraints that force the network to make physically consistent predictions; this leads to faster model convergence. We apply PIL-Net to the task of predicting atomic multipoles; which describe the charge distribution within an atom. Atomic multipoles have several applications; including their incorporation into force fields for molecular dynamics simulations. We emphasize our model's ability to predict atomic octupoles; a higher-order atomic multipole property; with a mean absolute error of only 0.0013 eÅ3; more than an order of magnitude less than results reported in the literature. Moreover; our framework can approximate molecular multipole moments post-training with little additional cost. Finally; we elaborate on how our network can be used for greater model interpretability; reconstruction of the molecular electrostatic surface potential; and prediction on out-of-domain datasets.

Keywords:

PIL-Net

graph convolutional network

molecular properties

atomic multipoles

physics-informed modeling

PIL-Net: a physics-informed graph convolutional network for predicting atomic multipoles

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