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201487 https://hdl.handle.net/2286/R.2.N.201487 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2025 120 pages Doctoral Dissertation Academic theses en Yao, Yi Li, Lin Yan, Feng Zhuang, Houlong Hong, Qijun Arizona State University Partial requirement for: Ph.D., Arizona State University, 2025 Field of study: Materials Science and Engineering Refractory high entropy alloys (RHEAs) have emerged as a promising class of structural materials, demonstrating exceptional mechanical performance in aggressive environments. However, the complex atomic environments, significant lattice distortion, and vast compositional space of RHEAs present challenges to understanding the mechanisms that govern structure-property relationships. In this work, two machine-learning potentials (MLPs) for MoNbTaW and MoNbTaWV RHEAs were developed. The MLPs were rigorously validated against lattice constants, elastic constants, generalized stacking fault energies, thermodynamic properties and screw dislocation core structures. Molecular dynamics simulations with the developed MLPs were applied to study the microstructural evolution and deformation mechanisms in MoNbTaW and MoNbTaWV RHEAs. Atomistic simulations revealed the influence of the chemical composition and local ordering on the mobility of edge and screw dislocations, as well as the effects of lattice distortion and diffuse anti-phase boundary energy (DAPBE) on dislocation behaviors during nanostructural evolution. Notably, with the increase in Nb concentration in the MoNbTaW RHEAs, DAPBE and lattice distortion are simultaneously enhanced as the chemical short-range order evolves into nanoscale B2 precipitates. This evolution results in high lattice distortion due to the lattice mismatch between B2 precipitates and random matrix. Consequently, B2 nanoprecipitates provide a stronger pinning effect, hindering edge dislocation motion while promoting cross-slip of screw dislocations, leading to a reduced screw-to-edge ratio in slip resistance and mobility discrepancy. Based on the mechanisms, a high-throughput exploration of the MoNbTaW RHEA system was conducted to identify candidates with reduced screw-to-edge discrepancy. To accelerate these explorations, a graph neural network model was developed to enhance Monte-Carlo simulations for ordered states, enabling more efficient identification of stable atomic configurations. In the MoNbTaWV RHEAs, Monte-Carlo simulations reveal that Mo-Ta pairs are still the most favorable, with ordering limited to short-range interaction. Dislocation simulations indicate that adding V to MoNbTaW RHEAs reduces the mobility of both edge and screw dislocations. The increased lattice distortion accounts for the decreased edge dislocation mobilities. And the increased lattice distortion also promotes the diffusivity of alloys by decreasing the vacancy migration barriers. This increased diffusivity promotes kink-pair nucleation on multiple planes, leading to cross-kinking, which further slows screw dislocation mobility. Materials Science Mechanism-Based Data Driven Development of High-Entropy Alloys with Superior Mechanical Properties