New research led by researchers at the University of Toronto (U of T) and Northwestern University employs machine learning to craft the best building blocks in the assembly of framework materials for use in a targeted application.
January 12,2021 New research led by researchers at the University of Toronto (U of T) and Northwestern University employs machine learning to craft the best building blocks in the assembly of framework materials for use in a targeted application. The findings, published today in Nature Machine Intelligence, demonstrated that the use of artificial intelligence (AI) approaches can help in proposing novel materials for diverse applications. One example is the separation of carbon dioxide from industrial combustion processes. AI approaches promise the acceleration of the design cycle for materials. With the objective of improving the separation of chemicals in industrial processes, the team of researchers—including collaborators from Harvard University and the University of Ottawa—set out to identify the best reticular frameworks (e.g., metal organic frameworks, covalent organic frameworks) for use in the process. Such frameworks, which can be thought of as tailored molecular «sponges», form via the self-assembly of molecular building blocks into different arrangements and represent a new family of crystalline porous materials that have been proven to be promising in addressing many technology challenges (e.g., clean energy, sensoring, biomedicine, etc.) «We built an automated materials discovery platform that generates the design of various molecular frameworks, significantly reducing the time required to identify the optimal materials for use in this particular process,» says Zhenpeng Yao, a postdoctoral fellow in the Departments of Chemistry and Computer Science in the Faculty of Arts & Science at U of T, and lead author of the study.
