A research initiative by researchers at Northwestern University and University of Toronto employs machine learning to create best building blocks for the assembly of framework materials for use in a targeted application.
The findings of the initiative is published in Nature Machine Intelligence. It demonstrates that the using artificial intelligence approaches can help to propose novel materials for diverse applications. The use of machine learning to separate carbon dioxide from industrial combustion process is an example. In fact, AI approaches are promising to accelerate the design process of materials.
Meanwhile, in a bid to improve segregation of chemicals in industrial processes, a team of researchers at Northwestern University and University of Toronto in collaboration with experts at the University of Ottawa and Harvard University set out to find the best reticular frameworks.
The frameworks can be viewed as tailored molecular sponges. The frameworks are formed via self-assembly of molecular building blocks put in different arrangements. Furthermore, the frameworks represent a new family of crystalline porous materials that have proven to be promising to address several technology challenges.
Demonstrated use of automated platform aided build-up of Design Frameworks
“Earlier, to build the frameworks, it involved building an automated discovery platform. The platform generates the design of various molecular frameworks, thus significantly reducing the time required to find optimal materials for use in this process,” said the lead author of the study.
In fact, in the demonstrated use of the platform, frameworks that are discovered are strongly competitive against some of best-performing materials used for the separation of CO2 till date.
Nonetheless, the unpredictable amount of time and massive trial-and-error efforts needed to find new materials are some perennial challenges for addressing CO2 separation.