Induced Dipole Calculation with E(3)-Equivariant Neural Networks and Multipole Field Perturbation

Polarizable force fields based on induced dipoles are widely implemented in molecular dynamics simulations of biological systems to explicitly capture electric induction effects. The iterative computation of induced dipoles suffers from convergence issue in large systems. We describe the implementation of an E(3)-equivariant neural network for predicting induced dipoles in polar solvent systems to avoid the numerical iterations. The neural network is combined with a physics-informed loss function to enable the use of artificially perturbed training data sets. The architecture is validated on water systems, benchmarked across varying densities, system sizes and ice polymorphs, and further integrated into molecular dynamics simulations. We demonstrate that perturbation-based data augmentation substantially enhances model transferability across diverse chemical environments, while physics-informed loss alone offers limited gains in generalization.