Computer methods that can predict materials organization and behavior, combined with property measurements, have the potential to accelerate the search for new materials for emerging applications. This project will identify new materials built from both large polymer molecules and small-dimension liquid crystals that combine two kinds of conductivity within the same molecule. These materials can form regions that act like "wires" to transport charged atoms (ions) through one type of liquid-like wire, and electrons through another type of solid wire. These types of molecules allow the design of conducting channels of different sizes and shapes. Such materials combine a variety of features needed for future robotics as well as those for energy storage and production. This materials discovery research will be carried out by an interdisciplinary team skilled in computational science and engineering, polymer chemistry, polymer engineering and physical property studies. Workforce training in this DMREF program will provide a stimulating and constructive environment in which participants are exposed to a comprehensive materials design cycle. The focus of the project also provides a compelling and unique context to convey the importance of science and engineering to students, facilitated by partnerships with established programs for the general public and students in high school and university. DMREF students will collectively participate in research and teaching to underrepresented groups. The data, materials design and machine learning software developed under this project will contribute to the Materials Genome Initiative national infrastructure and be accessible by the broader scientific and industrial community.
Mixed ionic/electronic conductors have been shown using traditional cumbersome research approaches to have promise for energy storage materials and to possess unexpected synergy between conducting phases. This DMREF program will accelerate the discovery of new promising mixed ionic/electronic conductors by using a dual iterative cycle to study the general phase behavior and transport dynamics of self-assembling polymeric and oligomeric liquid crystal mixed conductors. A machine learning approach will combine a genetic algorithm to propose successive generations of candidate materials, and a neural network scheme to construct a regression model to correlate input (a library of chemical groups and structures) with output variables (conductivity properties). An important aspect of the intellectual merit of this project lies in the development of processes to integrate computation, experiment and data analysis for the design of these functional materials. The proposed research is envisioned to not only shed light on the role of structure on phase behavior and charge transport, and their effect on interface sharpness and conductivity between the solid-like electronically conducting phase and the liquid-like ionically conducting regions but also lead to new applications as energy storage, sensing and robotic materials. Collaborations will provide additional expertise to this program.
This project is supported by funds from the DMREF initiative and the Polymers Program in the Division of Materials Research.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
