Modeling antiphase boundary energies of Ni3Al-based alloys using automated density functional theory and machine learning

• Main Authors: Asta, M. (Author), Chen, E. (Author), Epler, M.E (Author), Frolov, T. (Author), Tamm, A. (Author), Wang, T. (Author)
• Format: Article
• Language: English
• Published: Nature Research 2022
• Subjects: Alloy compositions; Alloy designs; Antiphase boundaries; Antiphase boundary energies; Computation theory; Computational workflows; Density functional theory; Density-functional-theory; Machine learning; Machine learning models; Materials properties; Ni-based superalloys; Nickel alloys; Nickel compounds; Planar defect; Superalloys; Tuning parameter
• Online Access: View Fulltext in Publisher

 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. © 2022, The Author(s).