Artificially Intelligent Transmission Electron Microscopy

H. Xin
University of California, Irvine,
United States

Keywords: deep learning, AI, electron microscopy


Deep learning schemes have already impacted areas such as cognitive game theory (e.g., computer chess and the game of Go), pattern (e.g., facial or fingerprint) recognition, event forecasting, and bioinformatics. They are beginning to make major inroads within physics, chemistry and materials sciences and hold considerable promise for accelerating the discovery of new theories and materials. In this talk, I will introduce deep convolutional neural networks and how they can be applied to the computer vision problems in transmission electron microscopy and tomographic imaging.