Structural alloys are crucial for the infrastructure and energy sectors, and have applications in the automotive, aerospace and nuclear industries. These alloy systems are usually metallic materials with certain impurity atoms added to improve their mechanical strength and thermal stability. One of the most commonly used strategy of improving mechanical strength, called precipitation hardening, relies on adding elements that form compounds, and impart excellent high-temperature strength. While a successful strategy, the temperature range at which these alloys can be utilized is limited. In particular, at high-temperatures, the strength degrades during the life-time of the component. This award supports research that will help increase the operating temperatures and thermal stability of precipitation-hardened structural alloys. New scientific knowledge about the behavior of these alloys during processing will enable the design of high-performance materials. The research approach combines computational simulation-based tools and machine-learning algorithms, and focuses on a model alloy system based on lightweight Magnesium, which has the potential to improve energy efficiency in the automotive industry. The broader impacts of this project thus combine commercial advances in structural materials, with a range of educational benefits in training secondary school teachers and graduate students involved with the project.
This work focuses on the development of a novel automated framework for simulating interphase boundaries using a machine learning approach to calculate interatomic potentials. The researchers will use the ternary Mg-Sn-Zn alloy for validating this framework but the approach can be generalized for screening potential ternary solute systems in any binary alloy system. This capability is crucial for designing precipitating systems with unprecedented thermal stability. This award also provides research opportunities to students from underrepresented groups, and the training of the next generation of secondary school teachers. Students from the NC State Education Department will participate in the scientific process through collaborative research projects on the themes of machine learning and atomistic simulations.
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.
