4.8 Article

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

Journal

NPJ COMPUTATIONAL MATERIALS
Volume 8, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41524-022-00755-1

Keywords

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Funding

  1. U.S. Department of Energy (DOE) by the Lawrence Livermore National Laboratory (LLNL) [DEAC52-07NA27344]
  2. HighPerformance Computing for Materials (HPC4Mtls) Program of the DOE Vehicle Technologies Office under Cooperative Research and Development Agreement (CRADA) [TC02309]
  3. National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility [DE-AC02-05-CH11231]
  4. National Science Foundation Graduate Research Fellowship Program [DGE-1752814]
  5. U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division [DE-AC02-05-CH11231, KC23MP]

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This study presents a computational workflow that uses machine learning models to automate the study of the effect of alloy composition on the APB energy in Ni3Al-based alloys. The transferability of these models is demonstrated by predicting APB energies in commercial superalloys.
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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