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Discovering a Transferable Charge Assignment Model Using Machine Learning

preprint
submitted on 21.06.2018 and posted on 22.06.2018 by Andrew E. Sifain, Nicholas Lubbers, Benjamin T. Nebgen, Justin S. Smith, Andrey Y. Lokhov, Olexandr Isayev, Adrian E. Roitberg, Kipton Barros, Sergei Tretiak

Partial atomic charge assignment is of immense practical value to force field parametrization, molecular docking, and cheminformatics. Machine learning has emerged as a powerful tool for modeling chemistry at unprecedented computational speeds given ground-truth values, but for the task of charge assignment, the choice of ground-truth may not be obvious. In this letter, we use machine learning to discover a charge model by training a neural network to molecular dipole moments using a large, diverse set of CHNO molecular conformations. The new model, called Affordable Charge Assignment (ACA), is computationally inexpensive and predicts dipoles of out-of-sample molecules accurately. Furthermore, dipole-inferred ACA charges are transferable to dipole and even quadrupole moments of much larger molecules than those used for training. We apply ACA to long dynamical trajectories of biomolecules and successfully produce their infrared spectra. Additionally, we compare ACA with existing charge models and find that ACA assigns similar charges to Charge Model 5, but with a greatly reduced computational cost.

Funding

US Department of Energy, LANL,

History

Email Address of Submitting Author

olexandr@olexandrisayev.com

Email Address(es) for Other Author(s)

kbarros@lanl.gov, serg@lanl.gov

Institution

University of North Carolina

Country

USA

ORCID For Submitting Author

0000-0001-7581-8497

Declaration of Conflict of Interest

None

Version Notes

Initial Version

Exports