Cooperative Research Teams
Click on the links below for a brief description of the scientific goals of each team.
- Resonant Inelastic X-Ray Scattering
- Dynamics and Cohesion of Materials Interfaces and Confined Phases Under Stress
- Multiscale Simulation of Thermo-mechanical Processes in Irradiated Fission-reactor Materials
- Predictive Capability for Strongly Correlated Systems
Resonant Inelastic X-Ray Scattering
Principal Investigators:
- Arun Bansil, Northeastern University
- Jim Freericks, Georgetown University
- Bob Markiewicz, Northeastern University
- Michel van Veenendaal, Northern Illinois University
Dynamics and Cohesion of Materials Interfaces and Confined Phases Under Stress
Principal Investigators:
- Mark Asta, University of California at Davis
- Alain Karma, Northeastern University
- Anthony Rollett, Carnegie Mellon University
This project brings together a team of researchers with a highly successful record of collaboration under previous CMSN support, to address new and outstanding scientific issues related to the properties of grain boundaries and associated confined interfacial liquid phases under stress. The project addresses challenging new methodological developments required to understand at a fundamental level the physics governing complex interface dominated processes associated with the breakdown of crystal cohesion and failure of stressed polycrystalline materials. This new focus represents a natural extension, and stronger unification, of the team's previous collaborations examining grain boundary and solid-liquid interface properties and their role in governing microstructure evolution. In the currrent project, development and validation of multiscale simulation methodologies are being undertaken in the context of four technologically important and thematically linked processes relevant to the processing and lifetime of materials for diverse DOE-related applications ranging from energy generation to stockpile stewardship. Specifically, we are seeking to gain new insights into the mechanisms of whisker growth, liquid-metal embrittlement, hot tearing and hot-ductility-dip cracking, with an overall goal of developing predictive models to aid in the prevention or control of these phenomena in practical applications.
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Multiscale Simulation of Thermo-mechanical Processes in Irradiated Fission-reactor Materials
Principal Investigators:
- Dieter Wolf, Idaho National Laboratory
- Blas Uberuaga, Los Alamos National Laboratory
- Ram Devanathan, Pacific Northwewst National Laboratory
- Simon R. Phillpot, University of Florida
Overview: The objective of this Computational Materials Science Network (CMSN) project on Multiscale Simulation of Thermo-mechanical Processes in Irradiated Fission-reactor Materials is to merge the expertise in the simulation of damage cascades in single crystals with the expertise in multiscale simulation of microstructural evolution in polycrystalline materials. This will enable us to elucidate the thermo-mechanical behavior of model fission-reactor materials under irradiation across all the relevant length- and time-scale regimes, with particular focus on the complex interplay between irradiation effects and materials microstructure. Phenomena to be studied include the coupling of irradiation, for example, with phase behavior and precipitation, diffusion creep, fission-gas bubble formation and migration through the microstructure, grain growth, embrittlement and crack propagation. The predictive, materials-physics-based simulation capability to be developed under this coordinated, multi-institutional thrust provides the methodology to systematically elucidate microstructural processes and parameters as input into higher-level, applied types of simulation codes for nuclear-fuel modeling.
Go to project web sitePredictive Capability for Strongly Correlated Systems
Principal Investigators:
- Richard T. Scalettar, University of California at Davis
- Warren Pickett, University of California at Davis
There are classes of materials that are important to DOE and to the science and technology community in general which have proven very difficult to understand and to simulate in a material-specific manner. These range from actinides, which are central to the DOE mission, to transition metal oxides, which include the most promising components of new spin electronics applications, to intermetallic compounds whose quantum critical behavior has given rise to some of the most active areas in condensed matter theory. After decades of study from a variety of often quite approximate viewpoints, a material-specific, predictive capability for many of these correlated electron systems is now a realistic goal. This exciting possibility is based on (1) new theoretical innovations, (2) coupling of experts in many-body theory with electronic structure practitioners, and (3) development of novel computational algorithms to solve the resulting equations. These new capabilities are arising at a time when there are extensive and novel experimental probes to provide data for a theory-computation-experiment feedback loop that enables the most rapid progress, and also extended computational power to bring to bear on solving the resulting numerical problem. The objective of the proposed cooperative research team is to assemble the required expertise into a coherent team and focus on the application of these new methodologies to the specific issue of Mott transitions, multi-electron magnetic moments, and dynamical properties of correlated materials. The goals are (i) to provide specific, detailed understanding of the complex correlation effects in strongly correlated systems, with specific emphasis on our compound of choice - MnO - through the application and further development of formal methods and numerical algorithms, and (ii) to make available to materials modelers efficient and accurate computer codes, which can then be used more widely for strongly correlated systems. Success in this undertaking will have a clear impact by moving the community toward the longer-term goal of opening up the entire periodic table to simulations with predictive capability.
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