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• W3033792303 abstract "Abstract 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." @default.
• W3033792303 created "2020-06-12" @default.
• W3033792303 creator A5007866163 @default.
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• W3033792303 date "2020-06-05" @default.
• W3033792303 modified "2023-10-15" @default.
• W3033792303 title "Neural network learns physical rules for copolymer translocation through amphiphilic barriers" @default.
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• W3033792303 doi "https://doi.org/10.1038/s41524-020-0318-5" @default.
• W3033792303 hasPublicationYear "2020" @default.
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