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.
@article{arxiv.2210.13453,
title = {A multi-category inverse design neural network and its application to diblock copolymers},
author = {Dan Wei and Tiejun Zhou and Yunqing Huang and Kai Jiang},
journal= {arXiv preprint arXiv:2210.13453},
year = {2022}
}