Much of modern society is built of polymers with applications ranging from car tires to food packaging, to vaccine stabilizing agents. Their continual improvement has remained a pressing pursuit given their global impact. The typical paradigm of polymer discovery is an iterative cycle of design, synthesis, and testing. The slow rate at which this process yields successful materials is limited by bottlenecks and inefficiencies at all stages. Ultimately, this high financial, labor, and time cost hinders the progression of polymer materials. To address these inefficiencies, the use of data science (polymer-informatics) can help us better select promising material candidates, thus significantly reducing the number of experiments needed. However, having access to a large amount of well-structured data in polymers is rare to non-existent. The complexity of polymer structures poses significant challenges in the formation of such datasets as there is no single representation that can capture the full molecular detail of a polymer material. Ultimately, data sets that do not completely capture all the relevant polymer, process, and characterization information have limited utility. Here, we discuss the development of CRIPT, and the underlying innovations that is making it possible to create a digital ecosystem for polymer data. The CRIPT ecosystem consists of a universal polymer data model, open-source community driven database, machine learning tools, and supporting software. The project adheres to open-source and FAIR data principles and seeks to serve as a community resource by allowing researchers to both: add their own data sets and retrieve any other data on the database. The CRIPT database hopes to provide polymer researchers with access to ‘big data’ to increase the community’s ability to develop better materials faster.

Applications of data science to polymer materials

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.