{"title":"实现嵌段共聚物自组装的数据驱动设计。","authors":"Chiara Magosso, Irdi Murataj, Michele Perego, Gabriele Seguini, Debra J Audus, Gianluca Milano, Federico Ferrarese Lupi","doi":"10.1038/s41597-025-05379-w","DOIUrl":null,"url":null,"abstract":"<p><p>Here we present a database composed of scanning electron microscope images of self-assembled block copolymers. The fabrication process parameters, structural properties and microscope information are all contained in the image metadata, making a group of images a database on its own. This approach has numerous advantages including ease of sharing, reusability of information and resilience against user errors. This database follows the digital International System of Units principles and is complemented by a graphical user interface for process metadata insertion and an automated algorithm for image analysis to retrieve structural properties of the nanostructures. Databases such as this one, together with data-driven approaches, enable users to rationally design new materials with the desired properties by understanding the relationship between fabrication parameters and material structure. The here reported database, that contains around 1747 images of lamellar phase and lying down cylinders self-assembled block copolymers along with associated metadata, is structured so it can be continuously expanded by the research community including also samples with different block copolymers morphologies.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"1055"},"PeriodicalIF":6.9000,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12182569/pdf/","citationCount":"0","resultStr":"{\"title\":\"Enabling data-driven design of block copolymer self-assembly.\",\"authors\":\"Chiara Magosso, Irdi Murataj, Michele Perego, Gabriele Seguini, Debra J Audus, Gianluca Milano, Federico Ferrarese Lupi\",\"doi\":\"10.1038/s41597-025-05379-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Here we present a database composed of scanning electron microscope images of self-assembled block copolymers. The fabrication process parameters, structural properties and microscope information are all contained in the image metadata, making a group of images a database on its own. This approach has numerous advantages including ease of sharing, reusability of information and resilience against user errors. This database follows the digital International System of Units principles and is complemented by a graphical user interface for process metadata insertion and an automated algorithm for image analysis to retrieve structural properties of the nanostructures. Databases such as this one, together with data-driven approaches, enable users to rationally design new materials with the desired properties by understanding the relationship between fabrication parameters and material structure. The here reported database, that contains around 1747 images of lamellar phase and lying down cylinders self-assembled block copolymers along with associated metadata, is structured so it can be continuously expanded by the research community including also samples with different block copolymers morphologies.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"1055\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12182569/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-05379-w\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-05379-w","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}
Enabling data-driven design of block copolymer self-assembly.
Here we present a database composed of scanning electron microscope images of self-assembled block copolymers. The fabrication process parameters, structural properties and microscope information are all contained in the image metadata, making a group of images a database on its own. This approach has numerous advantages including ease of sharing, reusability of information and resilience against user errors. This database follows the digital International System of Units principles and is complemented by a graphical user interface for process metadata insertion and an automated algorithm for image analysis to retrieve structural properties of the nanostructures. Databases such as this one, together with data-driven approaches, enable users to rationally design new materials with the desired properties by understanding the relationship between fabrication parameters and material structure. The here reported database, that contains around 1747 images of lamellar phase and lying down cylinders self-assembled block copolymers along with associated metadata, is structured so it can be continuously expanded by the research community including also samples with different block copolymers morphologies.
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.