What is it about?
Uncovering slow collective variables (CVs) is crucial for understanding the underlying mechanism of self-assembly processes and facilitating the design of advanced materials through the bottom-up approach. This work introduces GraphVAMPnets, a deep learning-based approach that was developed to discover the slow CVs of self-assembly dynamics and demonstrates it in two systems: the aggregation of two hydrophobic molecules and the self-assembly of patchy particles.
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Why is it important?
The key significances of GraphVAMPnets are: (1) it adopts the graph neural networks, which is capable of identifying the slow collective variables that are invariant to the permutations and rotations of the monomers involved in the self-assembly dynamics; (2) it is grounded in VAMP theory (variational approach for Markovian processes), which can effectively capture the slow dynamics from the off-equilibrium self-assembly processes.
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This page is a summary of: GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics, The Journal of Chemical Physics, September 2023, American Institute of Physics,
DOI: 10.1063/5.0158903.
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