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We suggest and implement an approach for the bottom-up description of systemsundergoing large-scale structural changes and chemical transformations fromdynamic atomically resolved imaging data, where only partial or uncertain dataon atomic positions are available. This approach is predicated on the synergyof two concepts, the parsimony of physical descriptors and general rotationalinvariance of non-crystalline solids, and is implemented using arotationally-invariant extension of the variational autoencoder applied tosemantically segmented atom-resolved data seeking the most effective reducedrepresentation for the system that still contains the maximum amount oforiginal information. This approach allowed us to explore the dynamic evolutionof electron beam-induced processes in a silicon-doped graphene system, but itcan be also applied for a much broader range of atomic-scale and mesoscopicphenomena to introduce the bottom-up order parameters and explore theirdynamics with time and in response to external stimuli.

Exploring order parameters and dynamic processes in disordered systems via variational autoencoders