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

Gabriele Maria Coli; Emanuele Boattini; Laura Filion; Marjolein Dijkstra

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arXiv · 2106.14615

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

Gabriele Maria Coli·Emanuele Boattini·Laura Filion·Marjolein Dijkstra

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Abstract

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.

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Gabriele Maria Coli, Emanuele Boattini, Laura Filion, Marjolein Dijkstra. 2021-06-28. Inverse design of soft materials via a deep-learning-based evolutionary strategy. https://arxiv.org/abs/2106.14615

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