  Atomic-scale materials synthesis via layer deposition techniques present a
unique opportunity to control material structures and yield systems that
display unique functional properties that cannot be stabilized using
traditional bulk synthetic routes. However, the deposition process itself
presents a large, multidimensional space that is traditionally optimized via
intuition and trial and error, slowing down progress. Here, we present an
application of deep reinforcement learning to a simulated materials synthesis
problem, utilizing the Stein variational policy gradient (SVPG) approach to
train multiple agents to optimize a stochastic policy to yield desired
functional properties. Our contributions are (1) A fully open source simulation
environment for layered materials synthesis problems, utilizing a kinetic
Monte-Carlo engine and implemented in the OpenAI Gym framework, (2) Extension
of the Stein variational policy gradient approach to deal with both image and
tabular input, and (3) Developing a parallel (synchronous) implementation of
SVPG using Horovod, distributing multiple agents across GPUs and individual
simulation environments on CPUs. We demonstrate the utility of this approach in
optimizing for a material surface characteristic, surface roughness, and
explore the strategies used by the agents as compared with a traditional
actor-critic (A2C) baseline. Further, we find that SVPG stabilizes the training
process over traditional A2C. Such trained agents can be useful to a variety of
atomic-scale deposition techniques, including pulsed laser deposition and
molecular beam epitaxy, if the implementation challenges are addressed.
