Myriad methods have been developed to improve sampling of protein motions out to biologically relevant timescales . However, many perturb the thermodynamics or kinetics of a system, in turn biasing the predictions these simulations make. The advent of GPUs provides access to longer timescales, but they are bounded by an upper limit of parallelism in computing architectures . Recently, novel adaptive sampling techniques have allowed for the mapping of slower motions but may require system-specific knowledge or user-specified order parameter. Specialized hardware has also been developed that allows computational biophysicists to study these processes at unprecedented timescales. Moreover, utilizing and maintaining this kind of specialized hardware can be a costly endeavor.