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Assessing Conformer Energies using Electronic Structure and Machine Learning Methods.pdf (1.78 MB)

Assessing Conformer Energies using Electronic Structure and Machine Learning Methods

revised on 15.05.2020, 22:01 and posted on 18.05.2020, 08:07 by Dakota Folmsbee, Geoffrey Hutchison
We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.


National Science Foundation 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