DMREF/Collaborative Research: Computationally Driven Targeting of Advanced Thermoelectric Materials
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. Likewise, the resulting property database will serve as the seed for a long-term central, open repository for thermoelectric materials. In sum, this research program will lay the groundwork for a new, computationally driven, paradigm in thermoelectric material research. This computationally-led project has been awarded $1.6M by the NSF-DMR through the Materials Genome Initiative.
Vienna Ab initio Simulation Package (VASP)
LaDa toolkit for high-throughput ab-initio calculations. LaDa is an open-source, python based software developed at NREL within the Center for Inverse Design (Stevanovic is one of the developers), with the main purpose of facilitating performance of large number of simultaneous calculations. LaDa is currently undergoing the preparation for being released to public.