Abstract
Investigators publish new report on Machine Learning. According to news reporting from Mumbai, India, by NewsRx journalists, research stated, “The motivation of this work was to find the appropriate molecular dynamics (MD) and slip transmission parameters of dislocation-grain boundary (GB) interaction in tantalum that correlate with the stress required for the grain boundary to deform. GBs were modeled using [(11) over bar2], [(1) over bar 10], and [111] as rotation axes and rotation angle between 0 degrees and 90 degrees.” Financial support for this research came from Bhabha Atomic Research Centre. The news correspondents obtained a quote from the research from Bhabha Atomic Research Centre, “Dislocation on either {110} or {112} slip planes was simulated to interact with various GB configurations. Drop in shear stress, drop in potential energy, critical distance between dislocation and GB, and critical shear stress for dislocation absorption by the GB were the parameters calculated from MD simulations of dislocation-GB interactions. Machine learning models eXtreme Gradient Boosting and SHapley Additive exPlanations (SHAP) were used to find the correlation between the various parameters and yield stress of the GB configurations. Machine learning results showed that the MD parameters-critical distance between the dislocation and GB, drop in shear stress; and slip transmission parameter-m’ have a stronger correlation with yield stress. The SHAP results sorted the prominent slip plane and rotation axis affecting the yield stress.”
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NSTL
Springer Nature