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arXiv · 2210.13453

A multi-category inverse design neural network and its application to diblock copolymers

Dan Wei·Tiejun Zhou·Yunqing Huang·Kai Jiang
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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.

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Dan Wei, Tiejun Zhou, Yunqing Huang, Kai Jiang. 2022-10-12. A multi-category inverse design neural network and its application to diblock copolymers. https://arxiv.org/abs/2210.13453

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