| Abstract Scope |
I will highlight a number of scanning transmission electron microscopy (STEM) developments that have provided new insights into material properties and have the potential to dramatically accelerate materials characterization. I will present a core component to enable the autonomous electron microscope, the Universal Scripting Engine for Transmission Electron Microscopy (USETEM). We will show that this scripting engine is widely applicable and simplifies scripting to enable high-throughput atomic-level imaging of materials. As a first step towards this vision, a deep convolutional neural network is demonstrated that can be used to automate convergent beam electron diffraction pattern analysis. The process enables, for example, autonomous determination of sample thickness to within 1 nm and tilt to within a fraction of a milliradian, at real-time speeds. Automating the electron microscope using artificial intelligence will address data size, bias, and documentation concerns, providing improved inputs for machine learning algorithms for faster discovery of new materials. |