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Automated Identification of Collective Variables for Polymeric systems from Molecular Dynamics Data

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If you have a question about this talk, please contact Dr Christoph Schran.

Syndiotatic polysterene (sPS) exhibits complex polymorphic behaviour, resulting in rugged free energy landscapes (FELs). In these FELs, the high energy barriers that separate different polymorph basins hinder their systematic exploration by traditional molecular dynamics simulations. Enhanced sampling methods have the potential remedy this problem with prior knowledge of collective variables (CVs) that can resolve the relevant transition pathways, typically identified through physical or chemical expertise. Recently, data-driven methods have attracted considerable attention for learning the CVs without significant a priori insight. We aim to use different dimensionality reduction methods varying from linear methods like principal component analysis to more complex non-linear methods like uniform manifold approximation and projection for comparing the low dimensional embedding. In order to efficiently describe the local environment of sPS monomers, we adapt an atomic representation used in machine learning. One of the advantages of using these descriptors is that they do not require the incorporation of excessive system-specific intuition and demonstrate good transferability properties. Recently we applied these data driven methods to predict the glass transition temperatures for polymer melts.

This talk is part of the Lennard-Jones Centre series.

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