Assessing Conformer Energies using Electronic Structure and Machine Learning Methods
Our paper on assessing conformer energies utilizing different computational techniques is now published in the International Journal of Quantum Chemistry as an interactive article. We find that machine learning techniques are currently on par with semiempirical methods accuracy wise with great promise for future improvements in accuracy as well as speed through GPU acceleration. The article is open access with the raw data available on GitHub.
