Machine Learning-Accelerated Spectral Imaging Analysis for Nanomaterials

Scanning transmission electron microscopy (STEM)-based electron energy loss spectroscopy (EELS) has been developed recently to probe the atomic-scale structure of nanostructures. Despite its potential, one critical challenge using STEM-EELS is the difficulty of extracting information from a large spectral image dataset with convoluted spatial, energy and spectroscopic data. The complexity arises largely from limited energy resolution and overlapping spatial and spectroscopic information from different materials and physical/chemical states, especially in a reactive environment. To address this challenge, we developed an "on the fly" machine learning-enabled real-time spectral imaging analysis system based on non-negative robust principal analysis and compressed sensing. This offers a new way to study structure-property relationships of complex nanomaterials to facilitate materials design that are otherwise challenging to obtain through conventional experimental or simulation approaches. Test cases will show it provides a viable path to conduct spectroscopic studies of structure and chemical and electronic properties in a reactive environment and is general enough to be deployed for other spectral imaging tools in material research.