ABSTRACT Stacking fault energy (SFE) is of the most critical microstructure attribute for controlling the deformation mechanism and optimizing mechanical properties of austenitic steels, while there are no accurate and straightforward computational tools for modeling it. In this work, we applied both thermodynamic modeling and machine learning to predict the stacking fault energy (SFE) for more than 300 austenitic steels. The comparison indicates a high need of improving low-temperature CALPHAD (CALculation of PHAse Diagrams) databases and interfacial energy prediction to enhance thermodynamic model reliability. The ensembled machine learning algorithms provide a more reliable prediction compared with thermodynamic and empirical models. Based on the statistical analysis of experimental results, only Ni and Fe have a moderate monotonic influence on SFE, while many other elements exhibit a complex effect that their influence on SFE may change with the alloy composition.

中文翻译：

---

奥氏体钢的堆垛层错能量预测：热力学建模与机器学习

摘要 层错能 (SFE) 是控制变形机制和优化奥氏体钢机械性能的最关键的微观结构属性，但没有准确和直接的计算工具对其进行建模。在这项工作中，我们应用热力学建模和机器学习来预测 300 多种奥氏体钢的堆垛层错能 (SFE)。比较表明，迫切需要改进低温 CALPHAD（PHA 酶图计算）数据库和界面能预测，以提高热力学模型的可靠性。与热力学和经验模型相比，集成机器学习算法提供了更可靠的预测。根据实验结果的统计分析，