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CPP: Fachverband Chemische Physik und Polymerphysik
CPP 34: Emerging Topics in Chemical and Polymer Physics, New Instruments and Methods I
CPP 34.11: Talk
Thursday, March 21, 2024, 12:30–12:45, H 0106
Characterisation of the different polymorphs of syndiotactic polystyrene using data-driven methods — •Atreyee Banerjee1, Tristan Bereau2, and Joseph F. Rudzinski1, 3 — 1Max Planck Institute for Polymer Research, Mainz, Germnay — 2Heidelberg University, Germany — 3Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin
Syndiotatic polysterene (sPS) exhibits complex polymorphic behaviour, resulting in rugged free energy landscapes (FELs). Enhanced sampling methods have the potential to remedy to explore the rugged FEL with prior knowledge of collective variables (CVs) that can resolve the relevant transition pathways, typically identified through extensive physical or chemical expertise. Recently, data-driven methods have attracted considerable attention for learning relevant CVs without significant a priori insight. In this work, we adapt an atomic representation used in machine learning to efficiently describe the local environment of sPS monomers. These descriptors do not require the incorporation of excessive system-specific intuition and demonstrate good transferability properties. We then investigate low-dimensional projections from methods of varying complexity: Principal Component Analysis (linear projection), Uniform Manifold Approximation, and Projection (manifold learning). These dimensionality techniques, in conjunction with clustering, provide physically meaningful polymorphic states.
Keywords: Polymer Polymorphism; Data-driven Methods; Principal Component Analysis; Unsupervised Learning Methods; Free Energy Landscape