Assessing conformer energies using electronic structure and machine learning methods

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 similar to 700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find that the current ML methods have potential 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.

Automated summary

The study evaluated the performance of various computational methods on relative single-point energies and found that machine learning methods show promise. Recommendations were made for methods at different levels of accuracy and time tradeoff, and it was indicated that continued improvements in performance and accuracy are expected in the future.