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This primer helps the reader understand the basic categories of molecular representations and provides computational tools to generate molecular descriptors in each of these categories. After reading this primer, you will be able to use various methods to generate machine and/or human interpretable representations of molecular systems for inputs to machine learning models or for general chemical data science applications.more » « less
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Heiss, Teresa; Tymochko, Sarah; Story, Brittany; Garin, Adelie; Bui, Hoa; Bleile, Bea; Robins, Vanessa (, 2021 IEEE International Conference on Big Data (Big Data))Digital images enable quantitative analysis of material properties at micro and macro length scales, but choosing an appropriate resolution when acquiring the image is challenging. A high resolution means longer image acquisition and larger data requirements for a given sample, but if the resolution is too low, significant information may be lost. This paper studies the impact of changes in resolution on persistent homology, a tool from topological data analysis that provides a signature of structure in an image across all length scales. Given prior information about a function, the geometry of an object, or its density distribution at a given resolution, we provide methods to select the coarsest resolution yielding results within an acceptable tolerance. We present numerical case studies for an illustrative synthetic example and samples from porous materials where the theoretical bounds are unknown.more » « less
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