A hybrid organic-inorganic perovskite dataset

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2017-05-09 DOI:10.1038/sdata.2017.57

Chiho Kim, Tran Doan Huan, Sridevi Krishnan, Rampi Ramprasad

{"title":"A hybrid organic-inorganic perovskite dataset","authors":"Chiho Kim, Tran Doan Huan, Sridevi Krishnan, Rampi Ramprasad","doi":"10.1038/sdata.2017.57","DOIUrl":null,"url":null,"abstract":"Hybrid organic-inorganic perovskites (HOIPs) have been attracting a great deal of attention due to their versatility of electronic properties and fabrication methods. We prepare a dataset of 1,346 HOIPs, which features 16 organic cations, 3 group-IV cations and 4 halide anions. Using a combination of an atomic structure search method and density functional theory calculations, the optimized structures, the bandgap, the dielectric constant, and the relative energies of the HOIPs are uniformly prepared and validated by comparing with relevant experimental and/or theoretical data. We make the dataset available at Dryad Digital Repository, NoMaD Repository, and Khazana Repository ( http://khazana.uconn.edu/ ), hoping that it could be useful for future data-mining efforts that can explore possible structure-property relationships and phenomenological models. Progressive extension of the dataset is expected as new organic cations become appropriate within the HOIP framework, and as additional properties are calculated for the new compounds found. Machine-accessible metadata file describing the reported data (ISA-Tab format)","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"4 1","pages":"1-11"},"PeriodicalIF":5.8000,"publicationDate":"2017-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1038/sdata.2017.57","citationCount":"106","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://www.nature.com/articles/sdata201757","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 106

Abstract

Hybrid organic-inorganic perovskites (HOIPs) have been attracting a great deal of attention due to their versatility of electronic properties and fabrication methods. We prepare a dataset of 1,346 HOIPs, which features 16 organic cations, 3 group-IV cations and 4 halide anions. Using a combination of an atomic structure search method and density functional theory calculations, the optimized structures, the bandgap, the dielectric constant, and the relative energies of the HOIPs are uniformly prepared and validated by comparing with relevant experimental and/or theoretical data. We make the dataset available at Dryad Digital Repository, NoMaD Repository, and Khazana Repository ( http://khazana.uconn.edu/ ), hoping that it could be useful for future data-mining efforts that can explore possible structure-property relationships and phenomenological models. Progressive extension of the dataset is expected as new organic cations become appropriate within the HOIP framework, and as additional properties are calculated for the new compounds found. Machine-accessible metadata file describing the reported data (ISA-Tab format)

Abstract Image

查看原文本刊更多论文

有机-无机混合包晶数据集

有机-无机混合包晶石（HOIPs）因其电子特性和制造方法的多样性而备受关注。我们制备了一个包含 1,346 种 HOIPs 的数据集，其中包括 16 种有机阳离子、3 种 IV 族阳离子和 4 种卤化物阴离子。我们结合原子结构搜索法和密度泛函理论计算，统一制备了 HOIPs 的优化结构、带隙、介电常数和相对能量，并通过与相关实验和/或理论数据的比较进行了验证。我们在 Dryad 数字资源库、NoMaD 资源库和 Khazana 资源库 ( http://khazana.uconn.edu/ ) 上提供了该数据集，希望它能对未来的数据挖掘工作有所帮助，从而探索可能的结构-性质关系和现象学模型。随着新的有机阳离子在 HOIP 框架内变得合适，以及对发现的新化合物进行更多性质计算，预计该数据集将逐步扩展。描述报告数据的机器可访问元数据文件（ISA-Tab 格式）

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.