We use HIP-NN, a neural network architecture that excels at predicting molecular energies, to predict atomic charges. The charge predictions are accurate over a wide range of molecules (both small and large) and for a diverse set of charge assignment schemes. To demonstrate the power of charge prediction on non-equilibrium geometries, we use HIP-NN to generate IR spectra from dynamical trajectories on a variety of molecules. The results are in good agreement with reference IR spectra produced by traditional theoretical methods. Critically, for this application, HIP-NN charge predictions are about 104 times faster than direct DFT charge calculations. Thus, ML provides a pathway to greatly increase the range of feasible simulations while retaining quantum-level accuracy. In summary, our results provide further evidence that machine learning can replicate high-level quantum calculations at a tiny fraction of the computational cost.
@article{arxiv.1803.04395,
title = {Transferable Molecular Charge Assignment Using Deep Neural Networks},
author = {Ben Nebgen and Nick Lubbers and Justin S. Smith and Andrew Sifain and Andrey Lokhov and Olexandr Isayev and Adrian Roitberg and Kipton Barros and Sergei Tretiak},
journal= {arXiv preprint arXiv:1803.04395},
year = {2018}
}