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Exploring particle dynamics during self-organization processes via rotationally invariant latent representations
[article]
2020
arXiv
pre-print
The dynamic of complex ordering systems with active rotational degrees of freedom exemplified by protein self-assembly is explored using a machine learning workflow that combines deep learning-based semantic segmentation and rotationally invariant variational autoencoder-based analysis of orientation and shape evolution. The latter allows for disentanglement of the particle orientation from other degrees of freedom and compensates for shifts. The disentangled representations in the latent space
arXiv:2009.00783v1
fatcat:fgdbshantrgddbudsdwso67i3i