The Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability and dependence on the constant C often result in sub-optimal performance. In this work, we show that the direct rupture life parameterization without intermediate LMP parameterization, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pearson correlation coefficient >0.9 for 9–12% Cr and >0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Finally, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9–12% Cr ferritic–martensitic alloys and austenitic stainless steel datasets.

Larson-Miller参数（LMP）提供了一种高效，快速的方案来估算高温应用中合金材料的蠕变断裂寿命。但是，泛化性差且对常数C的依赖性通常会导致性能欠佳。在这项工作中，我们表明，使用梯度提升算法，无需中间LMP参数化即可直接进行断裂寿命参数化，从而可以训练ML模型，从而非常准确地预测各种合金的断裂寿命（Pearson相关系数> 0.9，对于9 –12％的铬和> 0.8（对于奥氏体不锈钢）。此外，Shapley值用于量化特征的重要性，通过识别各种特征对模型性能的影响，使模型可解释。最后，通过对实验数据集进行条件处理，建立了基于变分自编码器的生成模型，以从在9-12％Cr铁素体-马氏体合金和奥氏体不锈钢数据集中均不存在的学习到的接头分布中对假设的合成候选合金进行采样。