Probing multi-dimensional composition spaces in search of strong metallic alloys

Abstract Due to their exceptional high-temperature strength compared to pure metals and dilute metal alloys, refractory complex concentrated alloys (RCCA) have attracted significant attention in recent times. However, predictive theory capable of guiding design and compositional optimization of RCCA for mechanical strength is yet to emerge. Here we present a series of large-scale Molecular Dynamics (MD) simulations of crystal plasticity devised to explore the space of alloy compositions in order to maximize their mechanical strength. As test-bed materials we focus on the Fe-Ta-W and the Nb-Ta-Mo-W alloy families, modeled here using Embedded Atom Model (EAM) and Spectral Neighbor Analysis Potentials (SNAP) interatomic potentials. To efficiently navigate our search towards mechanically strong alloy compositions, we employ iterative optimization using Gaussian process regression. Many of the simulated RCCA compositions exhibit well pronounced cocktail strengthening displaying strengths markedly exceeding that of their strongest constituent metal (tungsten in this case). At variance with common expectations, our computational experiments suggest that the highest strength may be achievable on the binary edges of the RCCA composition space. Taking advantage of the fully resolved atomistic trajectories, our simulations indicate that, much like in pure BCC metals, screw dislocations play a primary role in the plastic response of RCCA. However at large strains well past the yield point, dislocation multiplication and dislocation interactions (Taylor hardening) take over as the dominant contribution to RCCA strength.