BCC γ-U结构稳定性调控与AI赋能的U合金设计

doi:10.11896/cldb.25080179

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Abstract Body-centered cubic (BCC)γ-uranium (U) alloys are extensively employed in nuclear fuels due to their high density, outstanding thermal conductivity, and excellent irradiation resistance. However, retaining the γ-phase upon cooling from high temperatures is challenging because of the complex phase transitions in uranium alloys. Achieving phase stability requires the addition of BCC-stabilizing elements. Different alloying elements possess varying γ-stabilizing capabilities, and their complex interactions can induce phase separation or secondary intermetallic precipitation, making precise compositional control essential. This review synthesizes advancements in stabilizing the γ-phase via alloying strategies, focusing on U-Mo, U-Nb, U-Zr and U-Ti systems, as well as uranium-based high-entropy alloys. It also examines the cluster formula design approach and explores the growing role of artificial intelligence in accelerating uranium alloy development. Finally, the potential of integrating experimental, computational, and machine learning methodologies to optimize the composition and performance of high-performance uranium alloys is discussed.

Key words： γ-U alloys composition design alloying cluster-plus-glue-atom model machine learning

Published: 10 August 2026 Online: 2026-08-31

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TG146.8

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https://www.mater-rep.com/EN/10.11896/cldb.25080179 OR https://www.mater-rep.com/EN/Y2026/V40/I15/25080179

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