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Advances in instrumentation for experimental characterization of materials such as microscopy and spectroscopy have led to an explosion in information available on materials chemistry, structures, and transformations. But the interpretation of microscopy and spectroscopy data is increasingly challenging due to the increasing volume and complexity of these data. In this article, we discuss the use of theoretical modeling, artificial intelligence/machine learning (AI/ML), and AI/ML in conjunction with theory, for the interpretation of microscopy and spectroscopy data.