Chaka, Anne M. | Engineering Papers

DOE OSTI2022

First-Principles Study of Tritium Trapping by Point Defects in Fe-Al Aluminide Coating Phases

Density functional theory simulations have been carried out to investigate the potential for tritium trapping by metal vacancies in five different Fe-Al aluminide coating phases. It was found that tritiation of Fe and Ni vacancies is generally less favorable than the tritiation of Al vacancies. However, for the first tritiation, a trend in the defect formation energy can be obtained such that metal defects in the Fe 2 Al x family of materials (i.e., Fe 2 Al 4 , Fe 2 Al 5 , and Fe 2 Al 6 ) trap tritium species more favorably than metal vacancies in Fe 4 Al 13 and FeNiAl 5 . Further investigations using ab initio thermodynamics calculations confirmed that trend for a range of tritium partial pressure at a temperature of 700 K. Especially, it was shown that the energy difference between tritiated and non-tritiated metal vacancies is smaller and more favorable for the Fe 2 Al x family, followed by Fe 4 Al 13 , and FeNiAl 5 . While this study shows that tritium interacts differently in the various Fe-Al aluminide phases, it also suggests that tritium trapping and retention could be more efficient if metal defects are present in some Fe-Al phases.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Optimization of Thermal Conductance at Interfaces Using Machine Learning Algorithms

We report optimization of thermal transport across the interface of two different materials is critical to micro-/nanoscale electronic, photonic, and phononic devices. Although several examples of compositional intermixing at the interfaces having a positive effect on interfacial thermal conductance (ITC) have been reported, an optimum arrangement has not yet been determined because of the large number of potential atomic configurations and the significant computational cost of evaluation. On the other hand, computation-driven materials design efforts are rising in popularity and importance. Yet, the scalability and transferability of machine learning models remain as challenges in creating a complete pipeline for the simulation and analysis of large molecular systems. In this work we present a scalable Bayesian optimization framework, which leverages dynamic spawning of jobs through the Message Passing Interface (MPI) to run multiple parallel molecular dynamics simulations within a parent MPI job to optimize heat transfer at the silicon and aluminum (Si/Al) interface. We found a maximum of 50% increase in the ITC when introducing a two-layer intermixed region that consists of a higher percentage of Si. Because of the random nature of the intermixing, the magnitude of increase in the ITC varies. We observed that both homogeneity/heterogeneity of the intermixing and the intrinsic stochastic nature of molecular dynamics simulations account for the variance in ITC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗