实现嵌段共聚物自组装的数据驱动设计。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Chiara Magosso, Irdi Murataj, Michele Perego, Gabriele Seguini, Debra J Audus, Gianluca Milano, Federico Ferrarese Lupi
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引用次数: 0

摘要

在这里,我们提出了一个由自组装嵌段共聚物的扫描电子显微镜图像组成的数据库。图像元数据中包含了制作工艺参数、结构特性和显微镜信息,使一组图像本身成为一个数据库。这种方法有许多优点,包括易于共享、信息的可重用性和对用户错误的弹性。该数据库遵循数字国际单位制原则,并辅以用于过程元数据插入的图形用户界面和用于图像分析的自动算法,以检索纳米结构的结构特性。像这样的数据库,加上数据驱动的方法,使用户能够通过理解制造参数和材料结构之间的关系,合理地设计具有所需性能的新材料。这里报告的数据库包含了大约1747张片层相和躺柱自组装嵌段共聚物的图像以及相关的元数据,因此它可以被研究团体不断扩展,包括不同嵌段共聚物形态的样本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enabling data-driven design of block copolymer self-assembly.

Enabling data-driven design of block copolymer self-assembly.

Enabling data-driven design of block copolymer self-assembly.

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.

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来源期刊
Scientific Data
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.
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