most citedAccelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning

14 citations · 14 across the 1 of their papers we have counts for

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1 paper

cond-mat.mtrl-sci2024★ 14 cited

Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning

Albert Linda, Md. Faiz Akhtar, Shaswat Pathak +1

Stacking fault energies (SFEs) are vital parameters for understanding the deformation mechanisms in metals and alloys, with prior knowledge of SFEs from ab initio calculations bein…