Results for “Solute-Defect Clustering”

DOE OSTI2025

The role of excess vacancies in stabilizing solute clusters in low-alloy steels

The formation of solute clusters in irradiated low-alloy steels, such as reactor pressure vessel steels, is a critical cause of radiation hardening and embrittlement. However, the chemical interactions among solute elements and excess vacancies remain to be fully understood. This study employs density functional theory, cluster expansion, and lattice-based Monte Carlo simulations to examine the stability and morphology of nano-size coherent solute clusters with excess vacancies. The findings reveal that excess vacancies are crucial in stabilizing and promoting the growth of Mn-Ni-Si-vacancy clusters. A minimum of seven vacancies is required for stable nucleation and growth of these clusters. These Mn-Ni-Si clusters act as defect sinks, effectively trapping and absorbing mobile vacancies generated under irradiation. Furthermore, phosphorus (P) preferentially dissolves in Mn-Ni-Si clusters due to chemical coupling with vacancies. In conclusion, this study offers new insights into solute-vacancy interactions, enhancing our understanding of solute-defect cluster formation and embrittlement in low-alloy steels.

36 - MATERIALS SCIENCE↗

DOE OSTI2022

Machine learning pipeline to predict defect behavior in metallic alloy systems

The interaction between defect and solute atoms is critical to the thermodynamic and kinetic behavior of metallic alloys under exposure to high-energy radiation, causing irradiation damage in materials. Radiation can generate non-equilibrium concentrations of point defects such as vacancies and interstitials. The excess point defects not only accelerate diffusional processes such as precipitation that cause radiation embrittlement, but also change the pathway of phase transformations, including nucleation processes. Understanding these defect behaviors is complicated by the challenge and complexity of addressing each possible local and discrete distribution of environments and chemical interactions around targeted defects-solute or solute-solute complexes. To resolve the challenge, machine learning regression techniques have emerged as powerful tools that can train and construct an energy model to accurately describe the chemical interactions of solutes and defects. In Fiscal Year 2022, the work focused on the workflow development and demonstration using machine learning regression, density functional theory, cluster expansion, and Monte Carlo simulation to predict the effects of ternary solute elements (e.g., aluminum and molybdenum) and point defects on the Cr-rich $\alpha^{\prime}$ precipitation in multicomponent FeCr model alloys. The computational outcomes include the prediction of the ternary phase diagram, vacancy formation energy for different compositions, and the effect of vacancies on the nucleation of Cr-rich clusters. The simulations predict a pronounced change of Cr solubility in bcc Fe by the addition of Al and the rejection of Al atoms from $\alpha^{\prime}$ precipitates. Additionally, the simulations show the formation of Cr-vacancy clusters as the initial nuclei for stable nucleation and growth of $\alpha^{\prime}$ particles. The results demonstrate important outcomes and applications of using machine learning pipeline to study model or commercial alloys with multicomponent solute species and point defects.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗