****Technical Abstract**** The discovery of thermoelectric materials is the critical bottleneck limiting the widespread use of thermoelectric generators for energy harvesting. To date, the search for such materials has been challenging due to the multitude of conflicting property requirements that must be simultaneously satisfied. The proposed research addresses these challenges through a high-throughput search for materials, enabled by the continued improvements in large-scale computing and the development of a thermoelectric performance metric suitable for high-throughput calculations. High accuracy measurements of electronic structure and majority carrier transport properties will be used to validate the calculated descriptors. In support of these efforts, rapid experimental validation approaches for theory-predicted thermoelectric materials will be developed. On-the-fly data mining of the resulting experimental/theoretical property database will yield material-property relationships pointing to new target materials. The resulting techniques and software tools will be well-documented and open-access. The resulting property database will serve as the seed for a long-term central, open repository for thermoelectric materials. This research program lays the groundwork for a new, computationally driven, paradigm in thermoelectric material research.
The development of advanced thermoelectric materials could have a profound impact on the nation's energy portfolio. Solar thermoelectric generators and waste heat recovery could provide a significant fraction of our electricity needs. This program will lead to the development and dissemination of a transformative methodology for the realization of new thermoelectric materials, which can be extended to other materials sub-disciplines. High throughput electronic structure calculations of known earth-abundant compounds will provide the critical descriptors to identify new materials. The veracity of these calculations will be continuously tested through experimental measurements. Adaptive data mining will be used to extract structure-property trends and organically grow the material database. In doing so, a new generation of students (community college, undergraduate, graduate, post-doc) will be trained, which are conversant with both theoretical and experimental approaches to science by immersing them in a fully integrated research program. This effort extends beyond the core students in the research group through workshops focused on integrated theory/experiment approaches to thermoelectric materials and working in a "big-data" environment. A suite of K-12 and community college outreach programs targets the recruitment of underrepresented groups in STEM. These innovative programs include teacher training modules, after school programs, and summer research opportunities for community college students.
This award is supported by the Divisions of Materials Research (DMR), of Mathematical Sciences (DMS), and of Computer and Network Systems (CNS).
