论文标题
多类逆设计神经网络及其在二嵌段共聚物中的应用
A multi-category inverse design neural network and its application to diblock copolymers
论文作者
论文摘要
在这项工作中，我们设计了一个多类逆设计神经网络，以将订购的周期性结构映射到物理参数。神经网络模型由两个部分组成：分类器和结构参数映射（SPM）子网。分类器用于识别结构，SPM子网用于预测所需结构的物理参数。我们还提出了一种可扩展的相互空间数据增强方法，以确保周期性结构的旋转和翻译不变。我们根据Landau-Brazovskii模型将提出的网络模型和数据增强方法应用于二维二嵌段共聚物。结果表明，多类逆设计神经网络在预测所需结构的物理参数方面非常精确。此外，多类别化的想法也可以扩展到其他逆设计问题。
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.
