  Water isotope separation, specifically separating heavy from light water, is
a socially significant issue due to the usage of heavy water in applications
such as nuclear magnetic resonance, nuclear power, and spectroscopy. Separation
of heavy water from light water is difficult due to very similar physical and
chemical properties between the isotopes. We show that a catalytically active
ultrathin membrane (e.g., a nanopore in MoS2) can enable chemical exchange
processes and physicochemical mechanisms that lead to efficient separation of
deuterium from hydrogen, quantified as the D2O and deuterium separation ratio
of 4.5 and 1.73, respectively. The separation process is inherently multiscale
in nature with the shorter times representing chemical exchange processes and
the longer timescales representing the transport phenomena. To bridge the
timescales, we employ a deep learning methodology which uses short time scale
ab-initio molecular dynamics data for training and extends the timescales to
classical molecular dynamics regime to demonstrate isotope separation and
reveal the underlying complex physicochemical processes.
