A multi-category inverse design neural network and its application to diblock copolymers. (arXiv:2210.13453v1 [cond-mat.soft])

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.