The lab at UB will help researchers to commercialize new materials, such as graphene (pictured), leading to societal benefits and economic security. Photo: University at Buffalo.Scientists are already using supercomputers and other technologies to create ever-growing libraries of data on the properties of metals, polymers, ceramics and other materials. Yet as large as these databases are, they contain just a fraction of the information and knowledge needed to rapidly discover or design new materials that could have a transformative impact on advancing technologies that solve pressing social and economic problems.
One of the reasons for this is current databases lack the ability to collect and interpret visual data such as graphs and images from countless scientific studies, handbooks and other publications. This limitation creates a bottleneck that often slows the materials discovery process to a crawl. This could, however, soon change.
The University at Buffalo (UB) has received a $2.9 million grant from the US National Science Foundation (NSF) to transform the traditional role of a database, from a repository for information to an automated computer laboratory that rapidly collects, interprets and learns from massive amounts of information.
The lab, which will also conduct large-scale materials modeling and simulations based upon untapped troves of visual data, will be accessible to the scientific community, and ultimately speed up and reduce the cost of discovering, manufacturing and commercializing new materials. These are all goals of the US government's Materials Genome Initiative.
"This pioneering and multidisciplinary approach to advanced materials research will provide the scientific community with tools it needs to accelerate the pace of discovery, leading to greater economic security and a wide range of societal benefits," said Venu Govindaraju, UB's vice president for research and economic development.
Govindaraju, a professor of computer science and engineering, is the grant's principal investigator. Co-principal investigators, all from UB, are: Krishna Rajan, chair of the Department of Materials Design and Innovation (MDI); Thomas Furlani, director of the Center for Computational Research; Srirangaraj ‘Ranga’ Setlur, principal research scientist; and Scott Broderick, research assistant professor in MDI.
The award, from NSF's Data Infrastructure Building Blocks (DIBBS) program, draws upon UB's expertise in artificial intelligence, specifically its ground-breaking work that began in the 1980s to enable machines to read human handwriting. This work has saved postal organizations billions of dollars in the US and worldwide.
UB will use the DIBBS grant to create what it's calling the Materials Data Engineering Laboratory at UB (MaDE @UB). This lab will utilize the tools of machine intelligence, including machine learning, pattern recognition, materials informatics and modeling, high-performance computing and other cutting-edge technologies. Its aim is to transform data libraries into a facility that not only stores and searches for information, but also predicts and processes information to discover materials that transform how society addresses climate change, national security and other pressing issues.
"Essentially, we're creating a system – a smart robot – with cognitive skills for scientific interpretation of text, graphs and images, " said Rajan of MDI, a collaboration between UB's School of Engineering and Applied Sciences and the College of Arts and Sciences. The MDI was launched in 2014 to apply information science methods to advanced materials research.
"This machine intelligence driven approach will open a new trajectory of data-intensive materials science research impacting both computational and experimental studies," added Rajan. The lab builds upon significant investments UB has made in recent years to build a hub for advanced manufacturing in Western New York.
This story is adapted from material from the University at Buffalo, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.