We propose a variational autoencoder (VAE)-based model for building forward and inverse structure-property linkages, a problem of paramount importance in computational materials science. Our model systematically combines VAE with regression, linking the two models through a two-level prior conditioned on the regression variables. The regression loss is optimized jointly with the reconstruction loss of the variational autoencoder, learning microstructure features relevant for property prediction and reconstruction. The resultant model can be used for both forward and inverse prediction i.e., for predicting the properties of a given microstructure as well as for predicting the microstructure required to obtain given properties. Since the inverse problem is ill-posed (one-to-many), we derive the objective function using a multi-modal Gaussian mixture prior enabling the model to infer multiple microstructures for a target set of properties. We show that for forward prediction, our model is as accurate as state-of-the-art forward-only models. Additionally, our method enables direct inverse inference. We show that the microstructures inferred using our model achieve desired properties reasonably accurately, avoiding the need for expensive optimization loops.

翻译：我们提出了一种基于变分自编码器（VAE）的模型，用于建立正向与逆向结构-性能关联——这是计算材料科学中至关重要的课题。该模型将VAE与回归系统性地结合，通过以回归变量为条件的双层先验将两者关联。回归损失与变分自编码器的重建损失联合优化，从而学习同时适用于性能预测和微结构重建的微结构特征。所得模型可同时用于正向和逆向预测：即预测给定微结构的性能，以及预测获得指定性能所需的微结构。由于逆问题具有不适定性（一对多映射），我们采用多模态高斯混合先验推导目标函数，使模型能针对目标性能集推断出多种微结构。实验表明，在正向预测中，本模型与当前最优的正向模型精度相当；同时，我们的方法支持直接逆向推断。结果显示，利用本模型推断出的微结构能以合理精度实现目标性能，避免了昂贵的优化循环需求。