  The synthesis of complex materials through the self-assembly of particles at
the nanoscale provides opportunities for the realization of novel material
properties. However, the inverse design process to create experimentally
feasible interparticle interaction strategies is uniquely challenging. Standard
methods for the optimization of isotropic pair potentials tend toward
overfitting, resulting in solutions with too many features and length scales
that are challenging to map to mechanistic models. Here we introduce a method
for the optimization of simple pair potentials that minimizes the relative
entropy of the complex target structure while directly considering only those
length scales most relevant for self-assembly. Our approach maximizes the
relative information of a target pair distribution function with respect to an
ansatz distribution function via an iterative update process. During this
process, we filter high frequencies from the Fourier spectrum of the pair
potential, resulting in interaction potentials that are smoother and simpler in
real space, and therefore likely easier to make. We show that pair potentials
obtained by this method assemble their target structure more robustly with
respect to optimization method parameters than potentials optimized without
filtering.
