Unknown

Dataset Information

0

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


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.

SUBMITTER: Coli GM 

PROVIDER: S-EPMC8769546 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC7055566 | biostudies-literature
| S-EPMC8795878 | biostudies-literature
| S-EPMC8397714 | biostudies-literature
| S-EPMC7656263 | biostudies-literature
| S-EPMC9814741 | biostudies-literature
| S-EPMC7262997 | biostudies-literature
| S-EPMC7164942 | biostudies-literature
| S-EPMC9137882 | biostudies-literature
| S-EPMC6123479 | biostudies-literature
| S-EPMC7413342 | biostudies-literature