Which molecules can challenge density-functional tight-binding methods in evaluating the energies of conformers? investigation with machine-learning toolset

Large organic molecules and biomolecules can adopt multiple conformations, with the occurrences determined by their relative energies. Identifying the energetically most favorable conformations is crucial, especially when interpreting spectroscopic experiments conducted under cryogenic conditions. When the effects of irregular surrounding medium, such as noble gas matrices, on the vibrational properties of molecules become important, semi-empirical (SE) quantum-chemical methods are often employed for computational simulations. Although SE methods are computationally more efficient than first-principle quantum-chemical methods, they can be inaccurate in determining the energies of conformers in some molecules while displaying good accuracy in others. In this study, we employ a combination of advanced machine learning techniques, such as graph neural networks, to identify molecules with the highest errors in the relative energies of conformers computed by the semi-empirical tight-binding method GFN1-xTB. The performance of three different machine learning models is assessed by comparing their predicted errors with the actual errors in conformer energies obtained via the GFN1-xTB method. We further applied the ensemble machine-learning model to a larger collection of molecules from the ChEMBL database and identified a set of molecules as being challenging for the GFN1-xTB method. These molecules hold potential for further improvement of the GFN1-xTB method, showcasing the capability of machine learning models in identifying molecules that can challenge its physical model.