Advanced materials have the potential to solve many of the world’s challenges in energy, security, and medicine in much the same way that silicon revolutionized modern information and communication technologies. Yet, the process of advanced materials development in the United States is quite slow, averaging 10 to 20 years from initial research to first commercial or industrial use. In the summer of 2011, President Obama launched the Materials Genome Initiative for Global Competitiveness (MGI), a multi-agency $100 million effort designed to establish a new American research paradigm, building the infrastructure, knowledge base, and training to accelerate the process of advanced materials development.
The core of the MGI vision is to bring enhanced computational capabilities, data management, new experimental methods, and an integrated engineering approach to bear on all stages of materials development, from research through optimization, design, and certification, to manufacturing and deployment. Computational tools can expedite what is traditionally a time-consuming research process of hypothesis, experimentation, and characterization loops. Development of new algorithms for data management and sharing is seen as critical because there is no standardized method for scientists to share predictive algorithms and computational methods, and critical datasets and predictive models can sometimes be proprietary. Computational analysis and simulation, data development, and integrated experimental and engineering teams are the MGI toolset, designed to expedite development, and to allow design, engineering, and manufacturing systems to overlap.
Robert Hull, professor and head of materials science and engineering (MSE), calls the MGI approach, “the insertion of a holy trinity of advanced technologies: experimental, computational, and advanced data science” into materials science and “a bold and very well-conceived concept” for tackling what is “an undeniable problem and need,” he said. “Materials scientists have not traditionally thought of data as a tool at their disposal. This novel injection of data science into the materials science field needs to be sustained, with the necessary openness and accessibility.”
Most importantly, though, Hull notes that Rensselaer has strengths in all of the high-level expertise, component fields, and specialties that the MGI seeks to bring together to accelerate advanced materials development. His assertion is validated by the fact that Rensselaer currently has five Materials Genome Initiative projects underway.
Linda Schadler, Russell Sage Professor in Materials Science and Engineering, has a National Science Foundation-funded MGI project (with Cate Brinson and Wei Chen of Northwestern University) to develop both new data mining methods to enable underlying materials physics discoveries, and a resource (NanoMine) of polymer nanocomposite data. Schadler is also working with Curt Breneman, professor and head of chemistry and chemical biology, on a project funded by the Office of Naval Research that will develop a hybrid physics and machine-learning platform to predict the thermomechanical and electrical properties of polymer nanocomposites, and design new ones for better insulators and dielectric materials for high-voltage applications.
Breneman has another MGI project funded through the Office of Naval Research that is a multi-university collaboration with Columbia, University of Connecticut, University of Akron, and Penn State. The project’s goal is to create new power storage sources, using pure polymer dielectric materials for high-energy capacitors, for “pulse power” applications, or the storage and release of very large amounts of electrical energy in short periods of time.
Rensselaer is well placed to lead in this effort, with the new Rensselaer IDEA being a fertile ground of data science and predictive analytics, along with tremendous core strengths in materials science and engineering.”—Robert Hull
Daniel Gall, professor of materials science and engineering, has an NSF-funded project titled “Designing Materials to Revolutionize and Engineer our Future” (DMREF), which aims to develop a systematic method for determining the physical properties of nitrides used as hard protective coatings. Results from experiments and simulations are combined with a research-community-driven database to develop an online virtual tool that predicts the key materials properties.
Hull is working with John Wen, professor and head of industrial and systems engineering; Antionette Maniatty, professor of mechanical, aerospace, and nuclear engineering; and Daniel Lewis, associate professor of materials science and engineering, on an NSF-funded DMREF project that uses experimental, computational, and control tools to predict, measure, and control the evolution of materials microstructure during thermal processing of polycrystalline metals. Its aim is to use new experimental, simulation, feed-forward control, and data management methodologies toward a major new capability for control of materials processing: the ability to monitor, evaluate, and control a material’s structure as it evolves during thermal processing to obtain optimal properties.
Hull sees “the control and understanding of data” as a critical component overlaying these projects, and the MGI in general. “Rensselaer is well placed to lead in this effort, with the new Rensselaer Institute for Data Exploration and Applications (IDEA) being a fertile ground of data science and predictive analytics, along with tremendous core strengths in materials science and engineering.”