CAREER: Unlocking Ductility in Magnesium: How to Replace Twinning and Impede Damage

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NONTECHNICAL SUMMARYThis CAREER award supports basic research designed to make magnesium a more useful material. Currently, magnesium alloys are too brittle for many applications, despite being lighter and stronger than steel or aluminum. Less brittle magnesium would be broadly useful to make lighter and more efficient vehicles. In this project, the PI will develop better computational models for magnesium alloys using artificial intelligence methods and use these models to investigate the origins of cracks at the atomic scale. The insights gathered from characterizing these simulations will be used to develop strategies for making magnesium less brittle by changing the recipe for alloying elements and temperatures used to create magnesium parts.The award also supports the PI’s educational activities at the undergraduate and graduate levels. In addition to training students in the areas of computational modeling and machine learning, a new educational software will be developed with powerful research capabilities for material property exploration and discovery by coupling well established computational tools and calibration code with a simple graphical interface. The PI has developed a split-level course to engage both undergraduates and graduate students in computational modeling techniques. By making advanced modeling tools available and understandable to a broader audience, this class will serve as an outreach tool for materials research, inspiring students to study questions that no one has yet answered.TECHNICAL SUMMARYThis CAREER award supports basic research activities designed to reveal mechanisms producing the lack of ductility of magnesium. A good understanding of how cracks form in magnesium and how to prevent them has eluded researchers so far. Mitigation strategies such as work hardening and alloying have only shown limited success which has prevented magnesium from achieving broad market use. This is largely due to magnesium’s plastic anisotropy, non-Schmid stresses required for activation, and the diversity of complex active plastic modes such as c+a slip.Using molecular dynamics and topological modeling, magnesium deformation and failure will be studied by using the isotropic approximation to insert dislocations and twins into the simulation box and characterizing reactions during plastic deformation. Since the behaviors of Mg’s plastic modes are critically affected by alloying elements, simulations including important additives in solid solution such as aluminum, zinc, yttrium, and bismuth will also be employed. To do this effectively, new interatomic potentials for these solid solution binary alloys will be developed using the rapid atomistic neural network method. As a result, the role of contraction twins and their interactions with other twins, dislocations, solutes, and grain boundaries will be quantified. This will enable the PI to develop strategies which enhance ductile mechanisms and thereby retard crack formation.The award also supports the PI’s educational activities at the undergraduate and graduate levels. In addition to training students in the areas of computational modeling and machine learning, a new educational software will be developed with powerful research capabilities for material property exploration and discovery by coupling well established computational tools and calibration code with a simple graphical interface. The PI has developed a split-level course to engage both undergraduates and graduate students in computational modeling techniques. By making advanced modeling tools available and understandable to a broader audience, this class will serve as an outreach tool for materials research, inspiring students to study questions that no one has yet answered.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.