Researchers at the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) have developed a machine learning-inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.

Known as AtomAI, the package applies deep learning to microscopy data at atomic resolutions, thereby providing quantifiable physical information such as the precise position and type of each atom in a sample.

Using these methods, researchers can quickly derive statistically meaningful information from immensely complex datasets. These datasets routinely include hundreds of images that each contain thousands of atoms and abnormalities in molecular structure. This improvement to data analysis allows researchers to engineer quantum atomically precise abnormalities in materials, and can be used to gain deeper insights into the materials’ physical and chemical qualities.

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