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

revised on 09.02.2021, 17:14 and posted on 10.02.2021, 08:10 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.


NSF CHE-1800435


Email Address of Submitting Author


University of Pittsburgh


United States

ORCID For Submitting Author


Declaration of Conflict of Interest

No conflict of interest