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. 2018 Aug 24;9:3405. doi: 10.1038/s41467-018-05761-w

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© The Author(s) 2018

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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Fig. 2 — HOIPs input dataset for training and testing. a 212 high throughput HOIP structures. The combination of 11 small organic molecular species (A-site) and 32 divalent metals (B-site) constitutes the input samples of our ML model. X is a typical halide. b Data visualization in training (blue dots) and test (red dots) of tolerance factor and bandgap $E_{g}^{PBE}$ of HOIPs. The entire dataset consists of metals, semiconductors, and insulators