Abstract: High/medium entropy alloys (H/MEAs) have shown unique strengthening behavior and mechanical properties because of the presence of massive local chemical orderings. Nevertheless, dynamic interactions between chemical short-range orders (CSROs) and dislocations, and the underlying atomic strengthening mechanism remain elusive. In this work, we first developed a novel machine learning-embedded atom method (ML-EAM) potential of the CoNiV system, trained on a comprehensive first-principles dataset, which enables accurate and efficient modeling of CSRO formation and dislocation dynamics. Then, we investigated the strengthening mechanisms of CSROs in CoNiV MEA through machine learning-augmented molecular dynamics (MD) simulations. Hybrid MD/Monte Carlo simulations reveal that CSRO domains possess an L1₂ (NiCo)₃ V structure, whose size increases with lowering annealing temperatures. These domains significantly enhance strength by impeding dislocation motion through complex energy pathways, increasing depinning forces, and reducing mobility. Moreover, the MD simulations combined with theoretical analysis elucidate the competition between CSRO-assisted strengthening (via antiphase boundary formation) and solid solution weakening (via reduced atomic misfit volume). Phonon-drag effects are also amplified by CSROs, further resisting dislocation glide. Our results demonstrate that L1₂-CSROs strengthen CoNiV MEA primarily through antiphase boundary and phonon-drag contributions, providing new insights for designing high-performance multi-principal-element alloys via tailoring CSROs.

Key words: CoNiV medium-entropy alloy, Chemical short-range order, Dislocation motion, Lattice distortion, Machine-learning potential