Electron-Passing Neural Networks for Atomic Charge Prediction in Systems with Arbitrary Molecular Charge

Atomic charges are critical quantities in molecular mechanics and molecular dynamics, but obtaining these quantities requires heuristic choices based on atom typing or relatively expensive quantum mechanical computations to generate a density to be partitioned. Most machine learning efforts in this domain ignore total molecular charges, relying on overfitting and arbitrary rescaling in order to match the total system charge. Here, we introduce the electron-passing neural network (EPNN), a fast, accurate neural network atomic charge partitioning model that conserves total molecular charge by construction. EPNNs predict atomic charges very similar to those obtained by partitioning quantum mechanical densities but at such a small fraction of the cost that they can be easily computed for large biomolecules. Charges from this method may be used directly for molecular mechanics, as features for cheminformatics, or as input to any neural network potential.

Automated summary

EPNN is a fast and accurate neural network atomic charge partitioning model that conserves total molecular charge at a fraction of the cost of traditional quantum mechanical computations. It can be easily applied to large biomolecules, making it highly practical for various applications.