Modeling antiphase boundary energies of Ni_(3)Al-based alloys using automated density functional theory and machine learning

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 Ni_(3)Al-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.