This project will utilize HPC to generate knowledge and data necessary for accelerating new lightweight alloy discovery, important for the automotive industry to meet the increasingly stringent gas mileage regulations. The focus will be on systems that have not been explored due to experimental and manufacturing challenges, where limited data exists. Recent advances in fabrication might make the implementation of these systems viable, if justifiable. HPC may now be brought to provide reliable information that makes research time-, cost-effective and the expense defendable. Specifically, we propose to (1) utilize high-throughput calculations on important lightweight systems with limited data; (2) use and refine recently developed phase identification approaches including metastable states; (3) apply Monte Carlo (MC) approaches, including recently established direct first-principles based MC utilizing high-performance computing, for further refinement of finite-temperature behavior, and for development of databases of validated empirical models for faster exploration. The proposed work will establish important new data from rigorous, first principal based computational approaches, for systems that presently lack critical data. In particular, as mentioned, lightweight alloys are of interest due to their importance in energy-efficient applications for transportation and other areas. This work will directly provide information that enables the identification of the most promising candidate systems with significantly improved properties, amongst a large class of alloy compositions where data is limited. By providing input on this class of compositions, we will significantly reduce the effort, expense and energy of experimental explorations of these materials.
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