Npj Comput. Mater.: 高温合金—反向晶界能计算
镍基高温合金由于其优异的高温机械性能和耐腐蚀性，是飞机涡轮机、发电机等工业技术的首选材料。它通常具有多组化学成分，多达十种或更多元素，这种合金化进一步改善了镍基合金的高温性能。在镍基合金中，反相晶界能是合金设计中的一个重要参数。反相晶界能对合金成分非常敏感，这为优化合金成分以提供更高的屈服强度和抗蠕变性提供了丰富的设计空间。然而，由于材料系统有太多的自由度，仅依靠实验方法来探测反相晶界能量对成分的依赖性是非常困难的，且成本也高。近年来，随着计算水平的提高和计算手段的层出不穷，人们用密度泛函理论、相干势逼近、蒙特卡洛等方法对反相晶界能量进行了大量的计算研究，这也极大促进了对合金中反相晶界能量的理解，包括反相晶界能量与温度、自旋极化、成分和有序性之间的关联。
Antiphase boundaries (APBs) are planar defects that play a critical role in strengthening Ni-based superalloys, and their sensitivity to alloy composition offers a flexible tuning parameter for alloy design. Here, we report a computational workflow to enable the development of sufficient data to train machine-learning (ML) models to automate the study of the effect of composition on the (111) APB energy in Ni3Al-based alloys. We employ ML to leverage this wealth of data and identify several physical properties that are used to build predictive models for the APB energy that achieve a cross-validation error of 0.033 J m−2. We demonstrate the transferability of these models by predicting APB energies in commercial superalloys. Moreover, our use of physically motivated features such as the ordering energy and stoichiometry-based features opens the way to using existing materials properties databases to guide superalloy design strategies to maximize the APB energy.
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