Informatics and data science in materials microscopy

The breadth, complexity, and volume of data generated by materials characterization using various forms of microscopy has expanded significantly. Combined with increases in computing power, this has led to increased application of techniques from informatics and data science to materials microscopy data, both to improve the data quality and improve the materials information extracted from the data. This review covers recent advances in data science applied to materials microscopy, including problems such as denoising, drift and distortion correction, spectral unmixing, and the use of simulated experiments to derive information about materials from microscopy data. Techniques covered include non-local patch-based methods, component analysis, clustering, optimization, and compressed sensing. Examples illustrate the need to combine several informatics approaches to solve problems and showcase recent advances in materials microscopy made possible by informatics.