Deep Machine Learning for Atomically-Resolved Imaging Experiments: Physics Extraction and Feedback*

Development of the imaging tools such as electron, optical, and scanning probe microscopy in the last decade of 20th century has opened floodgates of imaging data, in the form of images, movies, and hyperspectral data sets. These contain information on the minute details of atomic structure and electronic, magnetic, and phonon functionalities, chemical transformation mechanisms, and quantum phenomena. However, the bottleneck in analysis and knowledge extraction from large volumes of raw imaging data is often human domain expert. Here I will show how utilization of deep artificial neural networks (aka deep learning) offers a path to overcome limitations of human analysis. I will specifically discuss applications of deep learning for rapid fully automated identification of individual atomic types and positions from scanning transmission electron microscopy images, using theoretical or labeled experimental images as a training set. We used this approach to construct reaction pathways for point defects in 2D materials, trace the structural evolution of atomic species during the electron beam manipulation, and create the library of defect configurations in silicon- and vacancy doped graphene. I will discuss specific examples where we showed that coupling of sulphur vacancy to molybdenum dopants in tungsten disulfide and reactions of silicon impurity on the edge and in the bulk of graphene can be explored quantitatively and mapped on the Markov model, giving rise to the transition probabilities on single atomic defect level. The work on genetic engineering optimization framework for automatic design of deep learning network architecture and hyperparameters optimal specifically for electron microscopy datasets will be addressed. Finally, I will discuss how a synergy of deep learning image analytics and real-time feedback allows harnessing beam-induced atomic and bond dynamics to enable direct atom-by-atom fabrication.