A multi-category inverse design neural network and its application to diblock copolymers
This work addresses inverse design problems in materials science, specifically for diblock copolymers, and is incremental as it builds on existing methods with a multi-categorization approach.
The authors tackled the problem of inverse design for ordered periodic structures by developing a multi-category neural network with a classifier and Structure-Parameter-Mapping subnets, achieving high accuracy in predicting physical parameters for diblock copolymers.
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.