We present a comparative
study that evaluates the performance of
a machine learning potential (ANI-2x), a conventional force field
(GAFF), and an optimally tuned GAFF-like force field in the modeling
of a set of 10 γ-fluorohydrins that exhibit a complex interplay
between intra- and intermolecular interactions in determining conformer
stability. To benchmark the performance of each molecular model, we
evaluated their energetic, geometric, and sampling accuracies relative
to quantum-mechanical data. This benchmark involved conformational
analysis both in the gas phase and chloroform solution. We also assessed
the performance of the aforementioned molecular models in estimating
nuclear spin–spin coupling constants by comparing their predictions
to experimental data available in chloroform. The results and discussion
presented in this study demonstrate that ANI-2x tends to predict stronger-than-expected
hydrogen bonding and overstabilize global minima and shows problems
related to inadequate description of dispersion interactions. Furthermore,
while ANI-2x is a viable model for modeling in the gas phase, conventional
force fields still play an important role, especially for condensed-phase
simulations. Overall, this study highlights the strengths and weaknesses
of each model, providing guidelines for the use and future development
of force fields and machine learning potentials.