The development of advanced molecular dynamics (MD) simulation methods has provided researchers the ability to rapidly screen for new chemistry, biological interactions, and materials. For example, large-scale molecular simulations have been used in the screening of molecular organic frameworks (MOFs) for hydrogen storage. These techniques are also employed in the study of soft materials such as proteins and polymers, and in the design of self-assembled colloidal systems. However, the analysis of MD datasets is challenging due to both their size and complexity; specifically, MD simulations can produce terabytes of data that require computationally efficient and scalable analysis methods, while the complexity of the data requires methods that are generalizable to a broad range of systems and that are robust to data heterogeneity and noise. Quantification and reduction of molecular simulation data has been traditionally conducted via order parameters and summarizing statistics such as radial distribution functions and correlation fields, particularly for condensed-phase systems. These descriptors are usually computationally efficient, physically interpretable, and are derived from principles of physics and statistical mechanics. Moreover, such descriptors usually correlate to emergent properties of interest and thus can be used to construct predictive models. However, order parameters are typically designed for particular applications that meet specific assumptions (e.g., spatial isotropy or crystallinity) and are thus limited in scope. Another approach for quantifying molecular simulation data consists of using machine learning (ML) tools such as convolutional neural networks (CNNs) and autoencoders to extract informative descriptors from data. ML tools are versatile in that they require few assumptions on the application and can be used for processing diverse data formats (e.g., images, tensors, graphs). However, descriptors extracted using ML applied and are easy to justify from a physical standpoint. An example arises in the analysis of non-covalent bonding networks (e.g., hydrogen bonding networks in water).