Accelerating the design of Ni-based single crystal (SX) superalloys with superior creep resistance at ultrahigh temperatures is a desirable goal but extremely challenging task. In the present work, a deep transfer learning neural network with physical constraints for creep rupture life prediction at ultrahigh temperatures is constructed. Transfer learning enables deep learning model breaks through the generalization performance barrier in the extrapolation space of ultrahigh temperature creep properties in the case of a very small dataset, which is the key to achieving the above design goal. Transfer learning is demonstrated to be effective in utilizing the prior compositional sensitivities information contained in the pre-trained model, and motivates the fine-tuned model to capture the particular relationship between composition and creep rupture life at ultrahigh temperature. Aiming to find advanced SX superalloys applied at 1200 °C, the proposed transfer learning-based model guides us to design a superalloy with a verified creep rupture life of ~170 h at 80 MPa, which exceeds the state-of-art value by 30%. The improved γ/γ′ interface strengthening, which is effectively regulated by the Mo/Ta ratio to form γ′ rafting with longer, flatter interfaces and achieve stronger interfacial bonding, is revealed as the dominant mechanism behind combining experiments and first-principles calculations. Moreover, the excellent extrapolation ability of the proposed model is further confirmed to enhance the efficiency of active learning by reducing its dependence on the initial dataset size. This study provides a pioneering AI-driven approach for the rapid development of Ni-based SX superalloys applied in advanced aero-engine blades.

加速超高温下具有优异抗蠕变性能的镍基单晶（SX）高温合金的设计是一个理想的目标，但也是一项极具挑战性的任务。在目前的工作中，构建了一个具有物理约束的深度迁移学习神经网络，用于超高温下的蠕变断裂寿命预测。迁移学习使得深度学习模型突破了极小数据集情况下超高温蠕变特性外推空间的泛化性能障碍，是实现上述设计目标的关键。事实证明，迁移学习可以有效地利用预训练模型中包含的先前成分敏感性信息，并激励微调模型捕捉超高温下成分与蠕变断裂寿命之间的特定关系。为了找到适用于 1200℃ 的先进 SX 高温合金，所提出的基于迁移学习的模型指导我们设计一种高温合金，其在 80MPa 下经验证的蠕变断裂寿命约为 170 小时，比现有技术值高出 30 %。结合实验和第一性原理计算，揭示了改进的γ/γ'界面强化，通过Mo/Ta比例有效调节，形成具有更长、更平坦界面的γ'筏，并实现更强的界面结合。此外，进一步证实了所提出模型出色的外推能力，通过减少对初始数据集大小的依赖来提高主动学习的效率。这项研究为快速开发应用于先进航空发动机叶片的镍基SX高温合金提供了一种开创性的人工智能驱动方法。