Inverse design of soft materials via a deep-learning-based evolutionary strategy

Colloidal self-assembly -- the spontaneous organization of colloids into
ordered structures -- has been considered key to producing 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.