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Researchers at the 91�Թ� and Northwestern University are using machine learning to craft the best materials for different industrial uses.

The findings, , demonstrated that the use of AI can help propose novel materials for diverse applications, helping to speed up the design cycle for materials. One example is the separation of carbon dioxide from industrial combustion processes.

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 – for example, metal organic frameworks and 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 technology challenges in fields that range from clean energy and sensors to biomedicine.

“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 post-doctoral researcher in the departments of chemistry and computer science in 91�Թ�’s Faculty of Arts & Science who is lead author of the study.

“In this demonstrated employment of the platform, we discovered frameworks that are strongly competitive against some of the best-performing materials used for CO2 separation known to date.”

The perennial challenges in addressing CO2 separation and other problems like greenhouse gas reduction and vaccine development, however, are the unpredictable amount of time and extensive trial-and-error efforts required in the pursuit of such new materials. The occasionally infinite combinations of molecular building blocks available in the construction of chemical compounds can mean the exhaustion of significant amounts of time and resources before a breakthrough is made.

“Designing reticular materials is particularly challenging, as they bring the hard aspects of modelling crystals together with those of modelling molecules in a single problem,” says senior co-author Professor Alán Aspuru-Guzik, Canada 150 Research Chair in Theoretical Chemistry in the departments of chemistry and computer science and a Canada CIFAR AI Chair at the Vector Institute for Artificial Intelligence.

“This approach to reticular chemistry exemplifies our emerging focus at 91�Թ� of accelerating materials development by means of artificial intelligence. By using an AI model that can ‘dream’ or suggest novel materials, we can go beyond the traditional library-based screening approach.”

The researchers focused on the development of metal-organic frameworks (MOFs) that are now considered the ideal absorbing material for the removal of CO2 from flue gas and other combustion processes.

“We began with the construction of a large number of MOF structures on the computer, simulated their performance using molecular-level modelling and built a training pool applicable to the chosen application of CO2 separation,” said study co-author Randall Snurr, the John G. Searle professor and chair of the department of chemical and biological engineering in the McCormick School of Engineering at Northwestern University.

“In the past, we would have screened through the pool of candidates computationally and reported the top candidates. What’s new here is that the automated materials discovery platform developed in this collaborative effort is more efficient than such a ‘brute force’ screening of every material in a database. Perhaps more importantly, the approach uses machine learning algorithms to learn from the data as it explores the space of materials and actually suggests new materials that were not originally imagined.”

The researchers say the model shows great prediction and optimization capability in the design of novel reticular frameworks, particularly in combination with already known candidates in specific functions, and that the platform is fully customizable in its application to address many contemporary technology challenges.

The research was supported by the Office of Science at the U.S. Department of Energy, the Canadian Network for Research and Innovation in Machining Technology, and the Natural Sciences and Engineering Research Council of Canada.