| Summary: | Despite widespread attentions on the HfNbTaTiZr refractory high-entropy alloy (RHEA) owing to various exceptional properties, insights into nanostructure-property relations are severely hindered by the lack of reliable interatomic potentials in atomistic simulations. In this paper, we propose a supervised machine learning framework powered by the distributed breeder genetic algorithm (DBGA) to train a charge transfer ionic potential (CTIP) for the multi-component Hf/Nb/Ta/Ti/Zr/O system. By combining the DBGA with extensive ab initio training data and a carefully designed objective function, a robust and extrapolative potential with massive internal parameters is effectively optimized. A cross-validation of the CTIP is rigorously performed to verify its accuracy in predicting energetic, structural and mechanical properties of metals, intermetallic compounds, alloys and oxides. Using the developed potential, we further study the atomic structure and its evolution in RHEA to reveal the deformation and oxidation mechanisms in response to mechanical loadings and oxygen atmospheres. The simulation results not only provide valuable atomistic information hardly accessible through experiments and thermodynamic calculations, but also display promising prospects for large-scale reactive simulations of the Hf/Nb/Ta/Ti/Zr/O complex-component system.
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