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. 2020 Feb 26;10:3486. doi: 10.1038/s41598-020-60410-x

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© The Author(s) 2020

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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Overall workflow for the machine learning model. Four atomic or structural features are generated for the Random Forests Regression, in which “bootstrapping”⁵⁵ and performance evaluation are used to validate the model, leading to a predictive model for the SHG coefficient of NLO crystals.