  One emerging approach for the fabrication of complex architectures on the
nanoscale is to utilize particles customized to intrinsically self-assemble
into a desired structure. Inverse methods of statistical mechanics have proven
particularly effective for the discovery of interparticle interactions suitable
for this aim. Here we evaluate the generality and robustness of a recently
introduced inverse design strategy [Lindquist et al., J. Chem. Phys. 145,
111101 (2016)] by applying this simulated-based, machine learning method to
optimize for interparticle interactions that self-assemble particles into a
variety of complex microstructures: cluster fluids, porous mesophases, and
crystalline lattices. Using the method, we discover isotropic pair interactions
that lead to self-assembly of each of the desired morphologies, including
several types of potentials that were not previously understood to be capable
of stabilizing such systems. One such pair potential led to assembly of the
highly asymmetric truncated trihexagonal lattice and another produced a fluid
containing spherical voids, or pores, of designed size via purely repulsive
interactions. Through these examples, we demonstrate several advantages
inherent to this particular design approach including the use of a parametrized
functional form for the optimized interparticle interactions, the ability to
constrain the range of said parameters, and compatibility of the inverse design
strategy with a variety of simulation protocols (e.g., positional restraints).
