  Predictive materials synthesis is the primary bottleneck in realizing new
functional and quantum materials. Strategies for synthesis of promising
materials are currently identified by time-consuming trial and error approaches
and there are no known predictive schemes to design synthesis parameters for
new materials. We use reinforcement learning to predict optimal synthesis
schedules, i.e. a time-sequence of reaction conditions like temperatures and
reactant concentrations, for the synthesis of a prototypical quantum material,
semiconducting monolayer MoS$_{2}$, using chemical vapor deposition. The
predictive reinforcement leaning agent is coupled to a deep generative model to
capture the crystallinity and phase-composition of synthesized MoS$_{2}$ during
CVD synthesis as a function of time-dependent synthesis conditions. This model,
trained on 10000 computational synthesis simulations, successfully learned
threshold temperatures and chemical potentials for the onset of chemical
reactions and predicted new synthesis schedules for producing well-sulfidized
crystalline and phase-pure MoS$_{2}$, which were validated by computational
synthesis simulations. The model can be extended to predict profiles for
synthesis of complex structures including multi-phase heterostructures and can
also predict long-time behavior of reacting systems, far beyond the domain of
the MD simulations used to train the model, making these predictions directly
relevant to experimental synthesis.
