# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## 5 Conclusions  
In summary, a machine learning-based computational framework has been developed for the quick screening of crucial features and accurate prediction of the creep rupture life of modified 9 Cr-1 Mo steels. First, a feature screening protocol based on correlation filtering and sequential feature selection techniques was developed for identifying critical features from a set of numerous descriptors, including material characteristics and environmental factors. The results show that the test stress, test temperature, tempering time, and the contents of S and Cr are identified as the five crucial features that greatly influence the creep life prediction of modified 9 Cr-1 Mo steels. The contributions of these features to creep rupture life are discussed and connected to the existing theoretical studies. Subsequently, various machine learning algorithms including Lasso regression, Ridge regression, support vector regression (SVR), Gaussian process regression (GPR), decision tree (DT), and random forest (RF) are employed for model training to explore their capability to map the intricate nonlinear interactions between multivariate variables and creep rupture life. Among the six algorithms, the GPR model with the five selected crucial features exhibits the highest prediction accuracy. Furthermore, the validation testing is performed using additional data out of model training and testing to further validate the efficacy of the constructed GPR model. The validated results show that most creep data of modified 9 Cr-1 Mo steels fall inside the two-factor band lines. Therefore, one can conclude that the developed GPR model with five crucial features can offer high accuracy and excellent adaptability in predicting creep life under various environmental conditions.  
The work described here creates a foundation for addressing the enormous challenge of the efficient and precise estimation of the creep rupture life of components at high temperatures. Though the effect of some material characteristics (e.g., the material form and the contents of C, Mo, N) on prediction accuracy is negligible in this work, the generative feature screening method offers an efficient way to identify the critical features and minimize the model complexity, thus allowing a significant number of candidates that may influence the creep properties to be identified without having to experimentally analyze them in the lab. As more creep data are provided to the dataset and additional features such as the inclusions and hardness are integrated, there is a growing opportunity for identifying robust features and improving the life prediction performance of high-temperature components.  
Figure 9: Prediction results of creep rupture life by GPR model with the validation dataset: (**a**) Prediction results of creep life from various literature in log scale; (**b**) Predicted creep life vs. measured creep life.  
**Author Contributions:** Conceptualization, M.C. and Y.H.; methodology, M.C. and Y.H.; software, Y.H.; validation, Y.L.; formal analysis, Y.S.; investigation, M.C.; resources, M.C. and Y.S.; data curation, Q.D.; writing--original draft preparation, M.C.; writing--review and editing, M.C.; visualization, M.C. and Y.H.; supervision, Y.S. and Z.Z.; project administration, M.C. and Z.Z.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by the National Natural Science Foundation of China (No. 22208258), China Postdoctoral Science Foundation (No. 2021M692532), the Fundamental Research Funds for the Central Universities (No. xzy012021044), and the SAMR project (No. 2020 MK182).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.