Georgia Tech's Institute for Materials (IMat) recently announced the winners of its 2017 seed funding program intended to foster a materials innovation ecosystem and associated development of competitive cross-cutting externally sponsored research in emerging areas of accelerated materials discovery and development.
The awards are meant to advance the strategic goals of the Materials Genome Initiative (MGI), which have been laid out over the past five years.
A total of five awards were given from 14 applicants in three programs.
MGI-Faculty Leadership Grant Award
· Michael A. Filler, Associate Professor, CHBE (Award $20k)
“Patternless Fabrication and Purification of High Performance Field Effect Transistors: A New System for Integrated Circuit Manufacturing”
IMat Graduate Student Fellows Award
· Nils Persson, ChBE-Advisor: Martha Grover (Award $5k)
“Computer Vision for Automated Materials Imaging and Analysis”
· Jordan Key, MSE-Advisor: Josh Kacher (Award $5k)
“In situ TEM investigation Correlating Microstructure to Corrosion Susceptibility in Fe Thin Films”
IMat Faculty Fellows Award
· Matthew McDowell, Associate Professor, ME (Award $10k)
“Harnessing Data Analytics to Unleash the Full Potential of Nanoscale In Situ Experiments”
· Josh P. Kacher, Assistant Professor, MSE; Mark D. Losego, Assistant Professor, MSE; Yao Xia, Assistant Professor, ISYE (Award $20k)
“Dynamically Responsive Scanning Diffraction for High-Throughput Analysis of Phase Assemblage in Functional Complex Oxides"
"These five seed funding awards demonstrate Georgia Tech's commitment to creating the next generation of materials workforce and research platforms,” said IMat Executive Director and Regents’ Professor David L. McDowell. “The diverse set of proposals reviewed were of very high quality, reflecting the depth and breadth of the GT materials innovation ecosystem involving experiments, computation and integration with data science."
These awards strengthen Georgia Tech's already strong position in Materials Genome-related research and fulfills a portion of Tech’s $10 million five-year commitment to the effort.
Mike Filler’s group will develop synthetic methods to fabricate large quantities of high performance electronic devices without top-down patterning (e.g., lithography). As-produced devices will then be purified based on their structural and electronic properties. Realization of this vision requires several high-throughput synthesis and characterization techniques, and associated data analytics.
“Our long-term goal is to produce electronics in much the same way we currently produce chemicals — with a sequence of reaction and separation steps,” he said.
Computer vision has contributed to many recent advances in modern technology, including robotics, facial recognition, and autonomous driving. Persson’s goal is to apply computer vision algorithms to a much smaller world: the world of nano-scale materials.
“The funding from IMAT will allow us to explore new ways to integrate data science with imaging and experimental materials research,” he said. “One potential scenario would have us leverage the 3D printing resources at Georgia Tech to simulate materials imaging. The possibilities are virtually endless in such a strong collaborative environment.
By combining in situ electron microscopy techniques with data science tools, Key hopes to develop a predictive phenomenological model that relates microstructure to corrosion properties. He said he believes such a tool would have great potential to significantly increase the speed and efficiency of corrosion-resistant materials design.
“This allows me access to sophisticated characterization equipment at DOE national laboratories, such as Oak Ridge or Sandia that can yield information with incredible spatial and temporal resolution,” he said. “Such a tool would have great potential to significantly increase the speed and efficiency of corrosion-resistant materials design. My goal is to publish the results in a high-impact journal as well as give a presentation at a technical conference such as M&M or MS&T.
Matthew McDowell and his group will use the grant to jump-start collaborations in developing new analytical tools for the automated analysis of in situ electron microscopy data. “My research group is grateful and honored to receive this grant, which will be used to advance our capabilities for observing and understanding real-time reaction processes in important new battery materials through the use of data analytics,” he said. “Our hopes are that this work will enable the development of new, safer, long-lasting batteries.”
Kacher, Losego and Xie are seeking to use advanced data analytics to close the loop between materials processing, structure, and properties. The three have been working together for the past year to develop high-speed scanning electron beam diffraction methods to gather spatially-dependent, statistically relevant, crystallographic information about materials.
“I think the most exciting part about this project is that each of us doesn't yet fully understand the other collaborators’ scientific expertise,” Losego said. “While we appreciate the potential power of our synergy, this seed funding will drive us to dig deeper into each other’s fundamental science pursuits and make unique connections that would not be possible as individuals.”
Please visit IMat’s website for more information regarding IMat’s seed funding program.
About MGI’s Strategic Goals
A key strategy of the MGI has been the coupling of advanced in situ and in operando experimental methods for synthesis and characterization, high throughput methods for exploring potential materials and performing early stage qualification, computational materials science, and modern data science. The ultimate goal is to increase the pace of materials discovery and development. This coupling is of course is too broad for a single initiative, as it reflects new scientific possibilities enabled by convergent advances in materials instrumentation, improved spatial and temporal resolution of measuring material structure and its evolution from atomic scale upward, predictive materials modeling and simulation via high performance computing, and data science and analytics. This convergence of experiment, theory and simulation, and data science offers new pathways to inform decision making as necessary to increase the pace of materials discovery and development. Indeed, the materials community is poised for a revolution in its ability to tailor and control structure and properties of new materials to address grand challenges in clean energy, sustainability, mobility, infrastructure, health, and security.