  Colloidal self-assembly -- the spontaneous organization of colloids into
ordered structures -- has been considered key to produce next-generation
materials. However, the present-day staggering variety of colloidal building
blocks and the limitless number of thermodynamic conditions make a systematic
exploration intractable. The true challenge in this field is to turn this logic
around, and to develop a robust, versatile algorithm to inverse design colloids
that self-assemble into a target structure. Here, we introduce a generic
inverse design method to efficiently reverse-engineer crystals, quasicrystals,
and liquid crystals by targeting their diffraction patterns. Our algorithm
relies on the synergetic use of an evolutionary strategy for parameter
optimization, and a convolutional neural network as an order parameter, and
provides a new way forward for the inverse design of experimentally feasible
colloidal interactions, specifically optimized to stabilize the desired
structure.
