Interface structures in complex oxides remain an active area of condensed matter physics research, largely enabled by recent advances in scanning transmission electron microscopy (STEM). Yet the nature of the STEM contrast in which the structure is projected along the given direction precludes separation of possible structural models. Here, we utilize deep convolutional neural networks (DCNN) trained on simulated 4D STEM datasets to predict structural descriptors of interfaces. We focus on the widely studied interface between LaAlO₃ and SrTiO₃, using dynamical diffraction theory and leveraging high performance computing to simulate thousands of possible 4D STEM datasets to train the DCNN to learn properties of the underlying structures on which the simulations are based. We test the DCNN on simulated data and show that it is possible (with >95% accuracy) to identify a physically rough from a chemically diffuse interface and create a DCNN regression model to predict step positions. We quantify the applicability of the model to different thicknesses and the transferability of the approach. The method shown here is general and can be applied for any inverse imaging problem where forward models are present.

复杂氧化物中的界面结构仍然是凝聚态物理研究的一个活跃领域，这在很大程度上得益于扫描透射电子显微镜（STEM）的最新进展。然而，STEM对比的本质（其中结构沿给定方向投影）排除了可能的结构模型的分离。在这里，我们利用在模拟4D STEM数据集上训练的深度卷积神经网络（DCNN）来预测界面的结构描述符。我们专注于广泛研究的LaAlO ₃和SrTiO ₃之间的界面，使用动态衍射理论并利用高性能计算来模拟数千种可能的4D STEM数据集，以训练DCNN来学习模拟所基于的基础结构的属性。我们在模拟数据上测试了DCNN，并表明有可能（准确度> 95％）从化学扩散界面识别出物理粗糙的物体，并创建DCNN回归模型来预测台阶位置。我们量化模型对不同厚度的适用性和方法的可传递性。此处显示的方法是通用的，可以应用于存在正向模型的任何逆成像问题。