Accelerated discovery of nanotwinned alloy systems

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Part 1. NON-TECHNICAL SUMMARYThe search and development of new stronger and long-lasting metallic materials are of high importance for applications ranging from space exploration to biological implants. Metals with nanoscale features such as grain sizes and engineered grain boundaries provide unprecedented control to improve a material’s behavior. A nanotwin is a type of boundary at the nanoscale that can be introduced in a metal during processing, deformation or by a heat treatment. Nanotwin microstructures can enhance the strength, ductility, conductivity, thermal stability, and corrosion resistance of a given material. Understanding which materials and compositions can form nanotwins during processing is a very time intensive task when performed by traditional research methods. In this proposal, new capabilities are developed to allow the study of hundreds of compositions at once to link the probability of developing nanotwins with material composition. This innovative approach allows for faster, cheaper, and more effective discovery and deployment cycles for new materials. Data from this project are used in computational classes for machine learning for university students, thus seamlessly combining teaching and research. Outreach collaborations with a local K-5 school provide positive exposure and mentoring to our youngest future scientists. Part 2. TECHNICAL SUMMARYHighly nanotwinned (NT) microstructures, which are composed of growth twins formed during the synthesis process, provide grain boundary control and a heterogeneous microstructure which can improve a variety of properties such as strength, ductility, conductivity, thermal stability, and corrosion resistance. However, further understanding and the deliberate implementation of NT microstructures has been hindered by a lack of fundamental data on stacking fault energy (SFE), an intrinsic material property directly correlated to twin growth. This proposal aims to overcome the prohibitively large task of finding a SFE for every desired composition by implementing high-throughput magnetron sputtering and combinatorial characterization and property mapping that links twin formation with composition. This work focuses on building materials libraries to connect compositions, NT microstructures, and mechanical material properties which would greatly expand and expedite materials design, by developing an experimental methodology that can be applied to any alloy system. The broader impacts for this project include participation in education and outreach programs targeting K-12, undergraduate, and graduate students with the goals of building a more diverse workforce and a sustainable STEM pipeline at every level.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.