论文标题
堆叠故障能量预测奥氏体钢：热力学建模与机器学习
Stacking fault energy prediction for austenitic steels: thermodynamic modeling vs. machine learning
论文作者
论文摘要
堆叠断层能（SFE）是控制奥氏体钢的变形机制和优化机械性能的最关键的微观结构属性，而没有用于对其进行建模的准确且直接的计算工具。在这项工作中，我们同时应用了热力学建模和机器学习，以预测300多个奥氏体钢的堆叠断层能量（SFE）。比较表明，需要提高低温calphad（相图的计算）数据库和界面能量预测以提高热力学模型可靠性。与热力学和经验模型相比，结合的机器学习算法提供了更可靠的预测。基于实验结果的统计分析，只有Ni和Fe对SFE具有中等单调的影响，而许多其他元素对SFE的影响可能随着合金组成而改变。
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.
