With the rapid adoption of ML, there has been a growing desire to use ML in molecular dynamics (MD) applications to provide more accurate simulations than FFs at a much lower cost than time-consuming quantum mechanical calculations. For ML to be reliable, it needs to properly predict geometric changes that occur in MD simulations from non-equilibrium bond stretching to torsional barriers. This work seeks to examine how well the current state of ML performs at these tasks, as well as to display the methods' understanding of chemical physics to help decide key needs for ML to improve as a surrogate for computationally expensive quantum calculations.