Understanding dislocation velocity in TaW using explainable machine learning

A. Kedharnath

doi:10.1007/s42864-024-00306-9

Understanding dislocation velocity in TaW using explainable machine learning

2024-12-19

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DOI：10.1007/s42864-024-00306-9

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The present work calculated the velocity of edge dislocations in the Ta-W system using molecular dynamics (MD) simulations and through machine learning (ML), identified the key parameters influencing the velocity. To achieve this, MD simulations were conducted at various values of the extrinsic parameters-temperatures and applied stresses (tau app\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tau }_{\text{app}}$$\end{document}), and the intrinsic variables-slip systems and alloying contents of tungsten in tantalum. Configurations containing edge dislocations on {110}/{112}/{123} planes were employed, and dislocation velocities were subsequently estimated. The MD results were processed using ML models, specifically extreme gradient boosting and SHapley Additive exPlanations (SHAP). SHAP analysis identified tau app\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tau }_{\text{app}}$$\end{document} as the most influencing parameter affecting velocity, followed by slip plane, temperature, and W addition. SHAP estimated the base velocity value (vb\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{\text{b}}$$\end{document}) to be 1376 ms-1. vb\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{\text{b}}$$\end{document} was calculated by training SHAP on a parameter-less model. vb\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{\text{b}}$$\end{document} could be increased by applying tau app\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tau }_{\text{app}}$$\end{document} of at least 1 GPa, through slipping on the {112} and {123} planes, at temperatures of 0 and 300 K, and in configurations with 0 wt.% and 5 wt.% W. The importance of vb\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{\text{b}}$$\end{document} on deformation was established.

Keyword:

Dislocation

Slip planes {110} {112} {123}

Tungsten effect

Temperature

Resolved shear stress

Understanding dislocation velocity in TaW using explainable machine learning

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