Machine learning (ML) methods offer promising pathways to access longer timescales and more efficient sampling. The use of autoencoders and compressed sensing for feature extraction, show promise at reducing the dimensionality of high-dimensional simulation data while preserving the key dynamics . By identifying the most relevant collective variables, these autoencoders allow simulations to focus computational resources on functionally important motions, ultimately extending the accessible timescales of a system (Fig. ). Moreover, these learned features can enhance traditional methods like Markov state models by more accurately capturing slow, meaningful transitions in the system's dynamics. The use of geometric features in enhanced sampling techniques demonstrates the potential to more efficiently sample conformational space in parallel datasets. Machine learning models can be trained to identify critical geometric variables that govern slow processes, enabling more targeted exploration of conformational space . Coupled with parallel simulation strategies, rapid state discovery can be efficiently achieved to reduce wall time and compute needed to generate well-sampled datasets.