Engineering metals are used broadly for advanced energy, transportation, and defense applications. Intriguingly, nano-crystalline metals, which are made of tiny nanometer-sized crystallites packed tightly together, exhibits superior strength and improved durability. Within each crystallite, the metal atoms are arranged in a highly orderly manner. However, as the crystallites can have various spatial orientations with respect to each other, the atoms near the boundaries between neighboring crystallites are no longer well aligned and are viewed as defects. These defects play a decisive role in the enhanced mechanical strength. Thus, to understand how these defects improve the mechanical strength, the PI and his team will use synchrotron X-ray sources available at the Argonne National Laboratory complemented with advanced atomistic modeling techniques developed by the PI in order to determine the motion of these atoms near these defects during deformation under various stresses, temperatures, and rates of mechanical deformation. Such insights will be used to advance predictive theory of the nano-crystallite metals’ time-dependent and environment-sensitive mechanical performances. The outcome of this research will also be leveraged into classroom instruction using a virtual reality (VR)-assisted teaching innovation. In addition, this project aims for broader impacts by outreaching to a variety of platforms to mentor the next generation workforce in STEM, inspire under-resourced K-12 students and potential future engineers with diverse backgrounds, and engage the public and increase their awareness of material science.
Understanding the behavior of metastable grain boundaries at complex environments is crucial to develop cost-effective and high-performance nanocrystalline alloys demanded in advanced energy, transportation, and defense applications. The primary objective of this project is to establish a fundamental basis to describe the structural and metastability evolution of grain boundaries at extreme conditions, and to use this basis to explain and predict the grain boundary-mediated deformation and the resultant mechanical properties of nanocrystalline alloys taking place under non-equilibrium processing. The project will combine advanced atomistic sampling techniques, potential energy landscape theory, and machine learning tool to: (i) obtain the ensemble of elementary structural rearrangements and associated activation energy spectra in disordered atomic packing environments by inducing location-specific perturbations; (ii) enable an automated detection of kernel atoms prone to plastic deformation through the non-affine displacement field analysis using a random sample consensus machine learning algorithm; (iii) establish a self-consistent kinetic theory to construct the connectivity between grain boundaries’ various metastable states in the potential energy landscape and to predict the evolution of time-dependent metastability; and, (iv) discover the interaction mechanisms between metastable grain boundaries and pinning dislocations across multiple timescales using potential energy landscape-assisted novel atomistic modeling protocol. This project will also carry out designed hypothesis-driven experiments, including X-ray diffraction, sub-ablation femtosecond laser pulses, and nano-indentation, to validate the modeling and theoretical predictions. The proposed research will directly link the atomic level processes (e.g., short-range atomic rearrangements and non-affine atomic displacement) inside the grain boundaries with the macroscopic behavior of nanocrystalline alloys, which may facilitate exploiting new design/processing space of nanocrystalline alloys, identifying optimized routes to access novel states with enhanced performance and durability under complex environments.
The research program will be integrated with educational innovations and outreach activities, including: (i) utilizing virtual reality and other advanced techniques in the developed new course on the subject of grain boundaries and other defects in structural materials; (ii) mentoring the next generation workforce in STEM fields by participating in Summer Schools both at the University of Michigan and at National Laboratories; (iii) outreaching to under-resourced K-12 students and potential future engineers with diverse background through various platforms, and sharing the PI’s research and career path to inform and inspire them and to promote higher education; and, (iv) exploring the attractive venues such as museums to engage the public and increase their awareness of materials science.
This award is cofunded through the Metals and Metallic Nanostructures and Condensed Matter and Materials Theory programs in the Division of Materials Research.
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.
