Modern theoretical chemistry relies on computer simulations typically involving numerical solution of Schrodinger equation. Such quantum mechanical (QM) methods tend to be more accurate than their classical counterparts, however, their computational scaling is frequently prohibitively expensive to treat realistic systems. Machine learning-based (ML) QM property predictors are capable of fitting directly to reference QM data with low error while remaining computationally as fast as classical techniques. We apply various ML models for QM property prediction. These techniques are trained to large QM datasets and then shown to generalize well outside of the training set. The targeted properties include various atomic charge partitioning schemes, dipoles and quadruples, infrared spectra, reduced Hamiltonians and a single model for singlet and triplet state energies and forces. Our results show the applicability of these accurate ML property predictors to systems many times larger than those in the training set with a several magnitude speedup over reference QM methods, an exciting prospect for computational sciences.

Machine learning for molecular properties

2019 Spring National Meeting

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