Autor segons l'article: Werner M; Guo Y; Baulin VA
Departament: Química Física i Inorgànica
Autor/s de la URV: Baulin, Vladimir
Paraules clau: Topology Simulations Prediction Polymer translocation Peptides Nonelectrolyte partition-coefficients Molecules Membrane Macromolecules Glass-transition temperatures
Resum: © 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.
Àrees temàtiques: Modeling and simulation Mechanics of materials Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) General materials science Computer science applications Chemistry, physical
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 20573960
Adreça de correu electrònic de l'autor: vladimir.baulin@urv.cat
Identificador de l'autor: 0000-0003-2086-4271
Data d'alta del registre: 2023-02-19
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.nature.com/articles/s41524-020-0318-5
URL Document de llicència: http://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Npj Computational Materials. 6 (1):
Referència 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 de l'article: 10.1038/s41524-020-0318-5
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2020
Tipus de publicació: Journal Publications
Repositori URV