Achieving a balance between strength and ductility at room temperature remains a grand challenge in developing body-centered cubic (BCC) refractory high-entropy alloys (RHEAs). Facing the vast compositional design space, we propose an alloy design framework, GAMP, combining a Generative Adversarial Network (GAN) with machine learning (ML) prediction models to accelerate the screening of potential alloy compositions. Unlike common inverse design or image-based approaches, the GAN in this work functions as a generator within a forward search loop, specifically capturing the latent distribution of existing alloys to propose high-potential new candidates. Among various models, eXtreme Gradient Boosting (XGBoost) was identified as the most accurate for predicting ultimate tensile strength (UTS) and elongation (El) to failure. A designed alloy, Al9.6Ti38.0Nb20.5Hf16.5V15.4, was experimentally validated to exhibit a UTS of 1058.1 MPa and an El of 18.5% at room temperature. Interpretability analysis further revealed that shear modulus local mismatch (GLM) and atomic size difference (δ) are the dominant descriptors for UTS, while enthalpy of mixing (ΔHmix) is the key indicator for El. Microstructural analysis revealed that the superior strength-ductility synergy resulted from the activation of multiple deformation mechanisms, in which the formation of kink bands is a key factor enabling the excellent room-temperature ductility. This study presents an efficient framework for designing advanced alloys with complex property requirements.
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