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1篇 您的检索式：作者名="Mario E.Epler"

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		题名	作者	年代	出处	被引量
	1	Modeling antiphase boundary energies of Ni_(3)Al-based alloys using automated density functional theory and machine learning显示文摘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.	Enze Chen Artur Tamm Tao Wang Mario E.Epler Mark Asta Timofey Frolov	2022	npj Computational Materials2022，，1：	0

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