  We show that neural networks trained by evolutionary reinforcement learning
can enact efficient molecular self-assembly protocols. Presented with molecular
simulation trajectories, networks learn to change temperature and chemical
potential in order to promote the assembly of desired structures or choose
between competing polymorphs. In the first case, networks reproduce in a
qualitative sense the results of previously-known protocols, but faster and
with higher fidelity; in the second case they identify strategies previously
unknown, from which we can extract physical insight. Networks that take as
input the elapsed time of the simulation or microscopic information from the
system are both effective, the latter more so. The evolutionary scheme we have
used is simple to implement and can be applied to a broad range of examples of
experimental self-assembly, whether or not one can monitor the experiment as it
proceeds. Our results have been achieved with no human input beyond the
specification of which order parameter to promote, pointing the way to the
design of synthesis protocols by artificial intelligence.
