It is extremely likely there are new and potentially much better functional polymers out there waiting to be discovered. Our goal is to accelerate the discovery process by using virtual screening methods … so that potential new polymers may be identified before they are made. — Rampi Ramprasad<\/cite><\/p>\n<\/blockquote>\n UConn materials scientist Ramamurthy “Rampi” Ramprasad is leading the effort at UConn. Ramprasad\u2019s lab will work to identify new polymers using quantum mechanical computations and data-driven machine learning. Because of their flexible chemical compositions, polymers are impressive when used as insulators, semiconductors, and permeable membranes. They also are safe, inexpensive to produce, and light. As such, they hold the potential for broader use in energy storage applications such as rechargeable batteries and fuel cells.<\/p>\n \u201cGiven the nearly infinite chemical compositions for polymers, it is extremely likely there are new and potentially much better functional polymers out there waiting to be discovered,\u201d says Ramprasad, a professor in the School of Engineering. \u201cOur goal is to accelerate the discovery process by using virtual screening methods powered by advanced computations and machine learning so that potential new polymers may be identified before they are made.\u201d<\/p>\n Other institutions participating in the initiative include Stanford University, the Massachusetts Institute of Technology, the University of Michigan, and the University at Buffalo. The U.K.-based materials science company Ilika is also a research partner, and there may be others. TRI is in ongoing discussions with additional collaborators.<\/p>\n \u201cToyota recognizes that artificial intelligence is a vital basic technology that can be leveraged across a range of industries, and we are proud to use it to expand the boundaries of materials science,\u201d said TRI Chief Science Officer Eric Krotkov. \u201cAccelerating the pace of materials discovery will help lay the groundwork for the future of clean energy, and bring us even closer to achieving Toyota\u2019s vision of reducing global average new-vehicle CO2 emissions by 90 percent by 2050.\u201d<\/p>\n The research will merge advanced computational materials modeling, new sources of experimental data, machine learning, and artificial intelligence in an effort to speed up the development of new materials, a process that historically has been measured in decades. The institutional research programs will follow parallel paths, working to identify new materials for use in future energy systems as well as to develop tools and processes that can accelerate the design and development of new materials more broadly.<\/p>\n The Toyota Research Institute is focusing on three key areas as part of the initiative:<\/p>\n \u2022 The development of new models and materials for batteries and fuel cells; Ramprasad\u2019s lab<\/a> has spent the past several years painstakingly building an online polymer genome knowledge base for a data vault the research team created called Khazana<\/a>. The Khazana platform, which is publicly accessible, allows scientists to search for potential polymers with specific properties. Polymers are large molecules made up of many repeating chemical building blocks. A common example of a polymer type is plastics, which can be chemically altered and easily manufactured for a variety of different applications from soda bottles to deck chairs to garbage bags.<\/p>\n
\n\u2022 Broader programs to pursue novel uses of machine learning, artificial intelligence, and materials informatics approaches for the design and development of new materials; and
\n\u2022 New automated materials discovery systems that integrate simulation, machine learning, artificial intelligence, and\/or robotics.<\/p>\n![\"UConn](\"https:\/\/today.uconn.edu\/wp-content\/uploads\/2017\/03\/RamprasadGraphic-1024x399.jpg\")
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