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• W2079080669 abstract "Nonlinear dimensionality reduction (NLDR) techniques are increasingly used to visualize molecular trajectories and to create data-driven collective variables for enhanced sampling simulations. The success of these methods relies on their ability to identify the essential degrees of freedom characterizing conformational changes. Here, we show that NLDR methods face serious obstacles when the underlying collective variables present periodicities, e.g., arising from proper dihedral angles. As a result, NLDR methods collapse very distant configurations, thus leading to misinterpretations and inefficiencies in enhanced sampling. Here, we identify this largely overlooked problem and discuss possible approaches to overcome it. We also characterize the geometry and topology of conformational changes of alanine dipeptide, a benchmark system for testing new methods to identify collective variables." @default.
• W2079080669 created "2016-06-24" @default.
• W2079080669 creator A5039944882 @default.
• W2079080669 creator A5041927899 @default.
• W2079080669 date "2015-01-22" @default.
• W2079080669 modified "2023-10-14" @default.
• W2079080669 title "Topological obstructions in the way of data-driven collective variables" @default.
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• W2079080669 doi "https://doi.org/10.1063/1.4906425" @default.
• W2079080669 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25637964" @default.
• W2079080669 hasPublicationYear "2015" @default.
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