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• W3175981569 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 way forward for the inverse design of experimentally feasible colloidal interactions, specifically optimized to stabilize the desired structure." @default.
• W3175981569 created "2021-07-05" @default.
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• W3175981569 date "2022-01-21" @default.
• W3175981569 modified "2023-09-30" @default.
• W3175981569 title "Inverse design of soft materials via a deep learning–based evolutionary strategy" @default.
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• W3175981569 doi "https://doi.org/10.1126/sciadv.abj6731" @default.
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