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There is a growing demand for metallic alloys with high strength and corrosion resistance at elevated temperatures for applications in aerospace, power generation, and chemical industries. With the ad-vancement of modern technologies, the need for metallic materials capable of operating at higher tem-peratures than current Ni-based superalloys continues to increase. Refractory high-entropy alloys (RHE-As), which have emerged over the past decade, represent a highly promising class of materials for ex-treme environments due to their high melting points and exceptional high-temperature strength.
In this study, we present a systematic exploration of the hyperspace of RHEAs within the Cr–Mo–Nb–Ta–V–W system. A materials library (ML) was fabricated using physical vapor deposition (PVD) on a silicon wafer, designed to produce continuous compositional gradients of each element in the range of 30–45 at.%. Assuming a compositional resolution of 1 at. %, this corresponds to approximately 35,000 distinct alloys. Synthesizing the same number of alloys via conventional methods such as arc melting or powder metallurgy, at a rate of one alloy per day, would take roughly 136 years. The co-sputtering process was calibrated to achieve an equimolar composition and maximum configurational entropy at the center of the ML (Fig. 1).
In the first stage, selected regions of the ML were characterized by X-ray fluorescence (XRF) to determine their chemical composition. Structural characterization was performed using X-ray diffraction (XRD), and the resulting diffractograms were analyzed via Le Bail fitting - a full-profile method allowing extraction of crystallographic parameters such as lattice constants and crystallite size. Subsequently, high-throughput nanoindentation was employed to assess mechanical properties. The resulting materials dataset was then used to train an artificial neural network (ANN) to predict mechanical properties of alloys extending be-yond the compositional space directly explored in the ML.