Thousands of new materials are created each year, but many new materials do not reach their maximum as their applications are not immediately clear. Researchers at the University of Toronto aim to tackle the use of artificial intelligence.
A study published in Nature Communications showed a team led by Seyed Mohamad Moosavi, a professor at Applied Science & Engineering Researcher, introduced AI tools that can predict how well new materials will work in real-world scenarios. The system focuses on a class of porous materials known as Metal Organic Frameworks (MOFs) with adjustable properties and a wide range of potential applications.
Moosavi points out that materials scientists have created more than 5,000 types of MOFs last year alone, highlighting the scale of the challenge.
“In material discovery, the typical question is, 'What is the best material for this application?'' says Moosavi, an assistant professor of chemical engineering and applied chemistry. “We turned the question upside down and asked, 'What is the best application for this new material?' With so many materials being made every day, I want to shift my focus from “which material to make next.” “What kind of evaluation should I give next?”
MOFs can be used, for example, to separate CO2 from other gases in the waste stream and to prevent carbon from reaching the atmosphere and contributing to climate change. It can also be used to supply drugs to specific areas of the body or to enhance the functionality of electronic devices.
It is often found that MOFs created for one purpose have ideal characteristics for completely different applications. Moosavi cites previous research that materials originally synthesized for photocatalysts were later found to be highly effective in carbon capture, only seven years after its creation.
The new AI-powered approach aims to reduce this time delay between discovery and deployment.
To achieve this, PhD student Sartaaj Khan developed a multimodal machine learning system trained with different kinds of data that are normally available immediately after synthesis.
“Multimodality is important,” says Khan. “By combining different kinds of material data, the models draw more complete pictures just as humans use different senses, such as vision and language, to understand the world.”
AI systems can use multimodal pre-training strategies to obtain insight into material geometry and chemical environments, allowing accurate property predictions without the need for post-synthesis structural characterization. This accelerates the discovery process and helps researchers identify promising material before it is overlooked or shelved.
To test the model, the team conducted a “time travel” experiment. AI was trained on available material data prior to 2017 and subsequently sought an evaluation of the synthesized materials. This system has successfully achieved several materials originally developed for other purposes as a powerful candidate for carbon capture. Some of them are currently undergoing experimental verification in collaboration with the National Research Council of Canada.
Musabi plans to integrate AI into the U of T's Acceleration Consortium's Autonomous Driving Laboratory (SDL).
“SDLS automates the process of designing, synthesising and testing new materials,” he says.
“When one lab creates new material, our system can evaluate it and potentially re-rout it to another lab equipped to assess its potential.
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