First-principles study of defects and phase transition in UO(2).
Jianguo Yu1, Ram Devanathan, William J Weber
1Pacific Northwest National Laboratory, PO Box 999, Richland, WA 99352, USA.
Summary
First-principles calculations reveal defect properties and phase transitions in uranium dioxide (UO2). The study models UO2 phase transitions using the (GGA)+U method, finding a phase transition pressure of 20 GPa.
Area of Science:
- Materials Science
- Computational Physics
- Nuclear Engineering
Background:
- Uranium dioxide (UO2) is a key material in nuclear fuel.
- Understanding its defect properties and phase transitions is crucial for reactor safety and performance.
- Accurate theoretical modeling is needed to complement experimental data.
Purpose of the Study:
- To investigate defect properties and phase transitions in UO2 using first-principles calculations.
- To model the phase transition process and determine the critical pressure.
- To analyze the formation energies of intrinsic and extrinsic defects in UO2.
Main Methods:
- All-electron projector-augmented-wave (PAW) method.
- Generalized gradient approximation with empirical self-interaction correction ((GGA)+U) formalism.
- Systematic examination of Hubbard parameter U(eff), magnetic ordering, chemical potential, and heat of formation.
Main Results:
- A U(eff) = 3.0 eV parameter accurately describes UO2 structural properties and models phase transitions.
- Calculated phase transition pressure for UO2 is approximately 20 GPa, improving upon LDA+U results.
- Formation energies of intrinsic defects show variability based on chemical potential and U-rich/O-rich conditions.
- Extrinsic defects, like Xenon (Xe), are found to be trapped by Schottky defects, consistent with experimental observations.
Conclusions:
- The (GGA)+U method provides a reliable framework for studying UO2 under pressure and its defect behavior.
- The calculated phase transition pressure offers valuable data for nuclear fuel modeling.
- Understanding defect interactions, particularly with fission products like Xe, is vital for predicting fuel behavior.
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