Author:
Wei Dan,Zhou Tiejun,Huang Yunqing,Jiang Kai
Abstract
In this work, we design a multi-category inverse design neural network to map ordered periodic structures 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 structures, 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 has high accuracy in predicting physical parameters for desired structures. Moreover, the idea of multi-categorization can also be extended to other inverse design problems.
Funder
NSFC Project
Natural Science Foundation for Distinguished Young Scholars of Hunan Province
China’s National Key R&D Programs
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Cited by
3 articles.
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