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Neural network learns physical rules for copolymer translocation through amphiphilic barriers

  • Datos identificativos

    Identificador: imarina:6406085
    Handle: http://hdl.handle.net/20.500.11797/imarina6406085
  • Autores:

    Werner M
    Guo Y
    Baulin VA
  • Otros:

    Autor según el artículo: Werner M; Guo Y; Baulin VA
    Departamento: Química Física i Inorgànica
    Autor/es de la URV: Baulin, Vladimir
    Palabras clave: Topology Simulations Prediction Polymer translocation Peptides Nonelectrolyte partition-coefficients Molecules Membrane Macromolecules Glass-transition temperatures
    Resumen: © 2020, The Author(s). Recent developments in computer processing power lead to new paradigms of how problems in many-body physics and especially polymer physics can be addressed. Parallel processors can be exploited to generate millions of molecular configurations in complex environments at a second, and concomitant free-energy landscapes can be estimated. Databases that are complete in terms of polymer sequences and architecture form a powerful training basis for cross-checking and verifying machine learning-based models. We employ an exhaustive enumeration of polymer sequence space to benchmark the prediction made by a neural network. In our example, we consider the translocation time of a copolymer through a lipid membrane as a function of its sequence of hydrophilic and hydrophobic units. First, we demonstrate that massively parallel Rosenbluth sampling for all possible sequences of a polymer allows for meaningful dynamic interpretation in terms of the mean first escape times through the membrane. Second, we train a multi-layer neural network on logarithmic translocation times and show by the reduction of the training set to a narrow window of translocation times that the neural network develops an internal representation of the physical rules for sequence-controlled diffusion barriers. Based on the narrow training set, the network result approximates the order of magnitude of translocation times in a window that is several orders of magnitude wider than the training window. We investigate how prediction accuracy depends on the distance of unexplored sequences from the training window.
    Áreas temáticas: Modeling and simulation Mechanics of materials Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) General materials science Computer science applications Chemistry, physical
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 20573960
    Direcció de correo del autor: vladimir.baulin@urv.cat
    Identificador del autor: 0000-0003-2086-4271
    Fecha de alta del registro: 2023-02-19
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.nature.com/articles/s41524-020-0318-5
    URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Npj Computational Materials. 6 (1):
    Referencia de l'ítem segons les normes APA: Werner M; Guo Y; Baulin VA (2020). Neural network learns physical rules for copolymer translocation through amphiphilic barriers. Npj Computational Materials, 6(1), -. DOI: 10.1038/s41524-020-0318-5
    DOI del artículo: 10.1038/s41524-020-0318-5
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2020
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Chemistry, Physical,Computer Science Applications,Materials Science (Miscellaneous),Materials Science, Multidisciplinary,Mechanics of Materials,Modeling and Simulation
    Topology
    Simulations
    Prediction
    Polymer translocation
    Peptides
    Nonelectrolyte partition-coefficients
    Molecules
    Membrane
    Macromolecules
    Glass-transition temperatures
    Modeling and simulation
    Mechanics of materials
    Materials science, multidisciplinary
    Materials science (miscellaneous)
    Materials science (all)
    General materials science
    Computer science applications
    Chemistry, physical
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