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 repor...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.展开更多
基金This work is partly performed under the auspices of the U.S.Department of Energy(DOE)by the Lawrence Livermore National Laboratory(LLNL)under Contract No.DE-AC52-07NA27344The authors are grateful for project funding from the High-Performance Computing for Materials(HPC4Mtls)Program of the DOE Vehicle Technologies Office under Cooperative Research and Development Agreement(CRADA)No.TC02309+2 种基金Computing support for this work comes from the LLNL Institutional Computing facilities,and the National Energy Research Scientific Computing Center(NERSC),a DOE Office of Science User Facility operated under Contract No.DE-AC02-05-CH11231E.C.acknowledges a fellowship through the National Science Foundation Graduate Research Fellowship Program under Grant No.DGE-1752814M.A.acknowledges support for his contributions by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,Materials Sciences and Engineering Division,under Contract No.DE-AC02-05-CH11231 within the Materials Project program(KC23MP).All figures are produced using matplotlib79.
文摘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.
