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. 2019 Jun 19;9:8739. doi: 10.1038/s41598-019-45344-3

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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(a) Accuracy (as captured by the RMSE value) of the artificial neural network models as a function of the number of neurons considered in each model (see Methods section)—as obtained for the training and test set, respectively. The optimal number of neurons is determined as that for which the RMSE value of the test set is minimum. (b) Comparison between the Young’s modulus values predicted by artificial neural network (with 5 neurons) and computed by molecular dynamics simulations.