UC Davis

Computational models can support materials development by identifying the key factors that a ect materialproperties and by guiding the search for optimal chemical and processing conditions. However, the success of this venture assumes the ability to accurately model material structures and the relationship of those structures to material properties. State-of-the-art tools in high-performance computing and machine learning are continually improving the performance of these models, thereby furthering their integration into the process of developing better materials including advanced alloys. This dissertation includes three projects that use atomic simulations to support the development of advanced alloys for applications in extreme conditions. First is the construction of a novel framework for machine learning potentials (MLPs). MLPs could dramatically accelerate simulations of atomic systems while providing the accuracy of electronic structure techniques through the use of supervised regression algorithms. MLPs do still have a higher computational expense than empirical potentials though, both during their construction and for every evaluation of the potential energy. With the purpose of reducing these costs and alleviating the necessity for enormous training data sets, our framework for producing MLPs combines an e cient implementation of a sparse Gaussian process algorithm with a novel set of descriptors for atomic environments. Second, molecular dynamics is used to investigate energy storage and heat evolution during high-strain-rate deformation of the refractory metal Ta. This is encapsulated in a quantity known as the Taylor{Quinney coe cient, which is critical to models of material failure in conditions where direct experimental measurement of the temperature is infeasible. Other than developing a phenomenological model for the energy stored in the material, this chapter identi es that a signi cant amount of the energy is stored in the form of point defects. Third, molecular dynamics is used to study the defect structures that evolve in irradiated materials in the low temperature and high radiant ux regime. The algorithm used involves the successive insertion of Frenkel pairs and relaxation of the simulation cell, and allowed the study of Fe, equiatomic CrCoNi, and a  ctitious metal with identical bulk properties to the CrCoNi up to the equivalent of 2.0 displacements per atom (dpa). Several areas requiring further research are identi ed, including the mechanisms by which Shockley partials develop in FCC metals at low dpa and robust ways to measure point defect concentrations in heavily-damaged FCC materials.