Computational modeling techniques, such as molecular dynamics, have provided valuable insight into chemistry over the years and continue to advance the field concurrently as these methods are developed to be more efficient and accurate. One of the greatest limitations molecular dynamics faces is computational efficiency; many interesting dynamics occur on a timescale that is currently unfeasible to simulate within a reasonable amount of time. A variety of approaches to combat this issue exist, including enhanced sampling techniques. Enhanced sampling requires the definition of a reaction coordinate that describes the process of interest, and subsequently the ability to bias simulations to sample along the reaction coordinate while reducing the natural energy barrier, thus increasing the speed of that process. While this method has been successful, the reaction coordinate is not always clear, and choosing a poor reaction coordinate will result in researcher-frustration and wasted time. The Tiwary group at the University of Maryland has developed a robust machine learning model formalized on the Predictive Information Bottleneck theory that optimizes a reaction coordinate and subsequently performs biased simulations to sample the process of interest. The application of machine learning in the field of molecular dynamics is still new, however I propose this combination will continue to develop and impact the future of enhanced sampling by minimizing the amount of human input required.