  Within simulations of molecules deposited on a surface we show that
neuroevolutionary learning can design particles and time-dependent protocols to
promote self-assembly, without input from physical concepts such as thermal
equilibrium or mechanical stability and without prior knowledge of candidate or
competing structures. The learning algorithm is capable of both directed and
exploratory design: it can assemble a material with a user-defined property, or
search for novelty in the space of specified order parameters. In the latter
mode it explores the space of what can be made rather than the space of
structures that are low in energy but not necessarily kinetically accessible.
