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- W4307414021 abstract "In this work, we design a multi-category inverse design neural network to map ordered periodic structure to physical parameters. The neural network model consists of two parts, a classifier and Structure-Parameter-Mapping (SPM) subnets. The classifier is used to identify structure, and the SPM subnets are used to predict physical parameters for desired structures. We also present an extensible reciprocal-space data augmentation method to guarantee the rotation and translation invariant of periodic structures. We apply the proposed network model and data augmentation method to two-dimensional diblock copolymers based on the Landau-Brazovskii model. Results show that the multi-category inverse design neural network is high accuracy in predicting physical parameters for desired structures. Moreover, the idea of multi-categorization can also be extended to other inverse design problems." @default.
- W4307414021 created "2022-11-01" @default.
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- W4307414021 date "2022-10-12" @default.
- W4307414021 modified "2023-09-30" @default.
- W4307414021 title "A multi-category inverse design neural network and its application to diblock copolymers" @default.
- W4307414021 doi "https://doi.org/10.48550/arxiv.2210.13453" @default.
- W4307414021 hasPublicationYear "2022" @default.
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