Prof. Cathy Chin part of new academia-industry partnership to accelerate the search for materials for sustainable energy and smartphones

Professor Cathy Chin (ChemE) is one of six researchers to join a new consortium — featuring players from industry, academia and government — that will use the power of artificial intelligence (AI) to accelerate design of the next generation of high-performance materials, with applications from renewable energy to consumer electronics.

This is the paradigm-shifting goal of the Alliance for AI-Accelerated Materials Discovery (A3MD), which brings together world-leading researchers from the University of Toronto, McMaster University and the National Research Council of Canada, as well as industrial partners LG and TOTAL.

Together, the team aims to discover advanced materials, both to convert atmospheric CO2 into usable energy and to enhance the performance of consumer products such as bright and vivid displays.

In addition to Chin, A3MD co-investigators include:

  • Professor Alan Aspuru-Guzik (Chemistry, Computer Science, U of T)
  • Professor Drew Higgins (McMaster University)
  • Professor David Sinton (MIE)
  • Dr. Isaac Tamblyn (National Research Council of Canada)
  • Professor Alex Voznyy (Physical and Environmental Sciences, UTSC)

This multidisciplinary team will develop new strategies to address one of the key challenges in the discovery and synthesis of new materials: the immense size of the search space.

Historically, the discovery of functional material has involved informed trial and error — and many trial tests. Moreover, the design of those experiments was subject to human bias: researchers tend to focus in on combinations of elements that their own experience suggest would be interesting.

In 2017, Aspuru-Guzik and Professor Ted Sargent (ECE), along with several other collaborators, issued a call to action in the journal Nature, arguing that emerging tools from the fields of AI and machine learning could play a key role in speeding up the search for new high-performance materials.

Properly trained algorithms can sort through vast libraries of simulated materials and recognize promising combinations in a fraction of the time, pointing researchers in fruitful directions. Ultimately, these materials need to be synthesized and tested in the lab. And here too, AI can help: when combined with advanced robotics, it enables the use of high-throughput screening (HTS).

In the first year, A3MD will put in place the needed infrastructure — including precision robotics — for high-throughput experimentation. The consortium will also convene several machine learning and data science bootcamps, training a new generation of experts, and will also organize a speaker series with leading researchers in the relevant fields. Graduate students and post-doctoral fellows will drive key aspects of the research and professional development strategy for the alliance.

In its second year, A3MD will expand further, adding industry and academic partners who bring additional expertise and offer new avenues to commercialize the novel technologies that will be developed.

This story was originally published in U of T Engineering News. Read the full story »


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