Comparing the expense and accuracy of methods to simulate atomic vibrations in rubrene.

Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder in metal-organic frameworks. Currently, there are several methods for predicting these vibrations using simulations, but the accuracy-efficiency tradeoffs have not been examined in depth. In this study, rubrene was used as a model system to predict atomic vibrational properties using six different simulation methods: Density Functional Theory, Density Functional Tight Binding, a Machine Learning model based on a trained Neural Net, the pre-trained ANI-1 Machine Learning method, a Molecular Dynamics-based method, and Density Functional Tight Binding with a Chebyshev polynomial-based correction. The accuracy of each method is evaluated by comparison to the experimental inelastic neutron scattering spectrum. All methods discussed here show some accuracy across a wide energy region, though the Chebyshev-corrected tight binding method showed the optimal combination of high accuracy with low expense. We then offer broad simulation guidelines to yield efficient, accurate results for inelastic neutron scattering spectrum prediction.