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Evaluation of Thermochemical Machine Learning for Potential Energy Curves and Geometry Optimization

preprint
submitted on 30.09.2020, 18:46 and posted on 06.11.2020, 19:37 by Dakota Folmsbee, David R. Koes, Geoffrey Hutchison
While many machine learning methods, particularly deep neural networks have been trained for density functional and quantum chemical energies and properties, the vast majority of these methods focus on single-point energies. In principle, such ML methods, once trained, offer thermochemical accuracy on par with density functional and wave function methods but at speeds comparable to traditional force fields or approximate semiempirical methods. So far, most efforts have focused on optimized equilibrium single-point energies and properties. In this work, we evaluate the accuracy of several leading ML methods across a range of bond potential energy curves and torsional potentials. Methods were trained on the existing ANI-1 training set, calculated using the ωB97X / 6-31G(d) single points at non-equilibrium geometries. We find that across a range of small molecules, several methods offer both qualitative accuracy (e.g., correct minima, both repulsive and attractive bond regions, anharmonic shape, and single minima) and quantitative accuracy in terms of the mean absolute percent error near the minima. At the moment, ANI-2x, FCHL, and our new grid-based convolutional neural net show good performance.

Funding

NSF CHE-1800435

History

Email Address of Submitting Author

geoffh@pitt.edu

Institution

University of Pittsburgh

Country

United States

ORCID For Submitting Author

0000-0002-1757-1980

Declaration of Conflict of Interest

No conflict of interest

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