Publications

Abstract not provided.

Abstract not provided.

The complexity of radiation effects in a material's microstructure makes developing predictive models a difficult task. In principle, a complete list of all possible reactions between defect species being considered can be used to elucidate damage evolution mechanisms and its associated impact on microstructure evolution. However, a central limitation is that many models use a limited and incomplete catalog of defect energetics and associated reactions. Even for a given model, estimating its input parameters remains a challenge, especially for complex material systems. Here, we present a computational analysis to identify the extent to which defect accumulation, energetics, and irradiation conditions can be determined via forward and reverse regression models constructed and trained from large data sets produced by cluster dynamics simulations. A global sensitivity analysis, via Sobol’ indices, concisely characterizes parameter sensitivity and demonstrates how this can be connected to variability in defect evolution. Based on this analysis and depending on the definition of what constitutes the input and output spaces, forward and reverse regression models are constructed and allow for the direct calculation of defect accumulation, defect energetics, and irradiation conditions. This computational analysis, exercised on a simplified cluster dynamics model, demonstrates the ability to design predictive surrogate and reduced-order models, and provides guidelines for improving model predictions within the context of forward and reverse engineering of mathematical models for radiation effects in a materials’ microstructure.

The evolution and characterization of single-isolated-ion-strikes are investigated by combining atomistic simulations with selected-area electron diffraction (SAED) patterns generated from these simulations. Five molecular dynamics simulations are performed for a single 20 keV primary knock-on atom in bulk crystalline Si. The resulting cascade damage is characterized in two complementary ways. First, the individual cascade events are conventionally quantified through the evolution of the number of defects and the atomic (volumetric) strain associated with these defect structures. These results show that (i) the radiation damage produced is consistent with the Norgett, Robinson, and Torrens model of damage production and (ii) there is a net positive volumetric strain associated with the cascade structures. Second, virtual SAED patterns are generated for the resulting cascade-damaged structures along several zone axes. The analysis of the corresponding diffraction patterns shows the SAED spots approximately doubling in size, on average, due to broadening induced by the defect structures. Furthermore, the SAED spots are observed to exhibit an average radial outward shift between 0.33% and 0.87% depending on the zone axis. This characterization approach, as utilized here, is a preliminary investigation in developing methodologies and opportunities to link experimental observations with atomistic simulations to elucidate microstructural damage states.

The complexity of radiation effects in a material’s microstructure makes developing predictive models a difficult task. In principle, a complete list of all possible reactions between defect species being considered can be used to elucidate damage evolution mechanisms and its associated impact on microstructure evolution. However, a central limitation is that many models use a limited and incomplete catalog of defect energetics and associated reactions. Even for a given model, estimating its input parameters remains a challenge, especially for complex material systems. Here, we present a computational analysis to identify the extent to which defect accumulation, energetics, and irradiation conditions can be determined via forward and reverse regression models constructed and trained from large data sets produced by cluster dynamics simulations. A global sensitivity analysis, via Sobol’ indices, concisely characterizes parameter sensitivity and demonstrates how this can be connected to variability in defect evolution. Based on this analysis and depending on the definition of what constitutes the input and output spaces, forward and reverse regression models are constructed and allow for the direct calculation of defect accumulation, defect energetics, and irradiation conditions. Here, this computational analysis, exercised on a simplified cluster dynamics model, demonstrates the ability to design predictive surrogate and reduced-order models, and provides guidelines for improving model predictions within the context of forward and reverse engineering of mathematical models for radiation effects in a materials’ microstructure.

Abstract not provided.

Abstract not provided.