Understanding dislocation velocity in TaW using explainable machine learning

Abstract 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 ( $${\tau }_{\text{app}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>τ</mml:mi> <mml:mtext>app</mml:mtext> </mml:msub> </mml:math> ), 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 $${\tau }_{\text{app}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>τ</mml:mi> <mml:mtext>app</mml:mtext> </mml:msub> </mml:math> as the most influencing parameter affecting velocity, followed by slip plane, temperature, and W addition. SHAP estimated the base velocity value ( $${v}_{\text{b}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>v</mml:mi> <mml:mtext>b</mml:mtext> </mml:msub> </mml:math> ) to be 1376 m·s -1 . $${v}_{\text{b}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>v</mml:mi> <mml:mtext>b</mml:mtext> </mml:msub> </mml:math> was calculated by training SHAP on a parameter-less model. $${v}_{\text{b}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>v</mml:mi> <mml:mtext>b</mml:mtext> </mml:msub> </mml:math> could be increased by applying $${\tau }_{\text{app}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>τ</mml:mi> <mml:mtext>app</mml:mtext> </mml:msub> </mml:math> 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 $${v}_{\text{b}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>v</mml:mi> <mml:mtext>b</mml:mtext> </mml:msub> </mml:math> on deformation was established.

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Publication Details

Journal
Tungsten
Published
2024-12-19
DOI
https://doi.org/10.1007/s42864-024-00306-9
Citations
3
Primary Topic
Metal and Thin Film Mechanics
Type
article
Field-Weighted Citation Impact
0.81

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article

Understanding dislocation velocity in TaW using explainable machine learning

A. Kedharnath, Rajeev Kapoor, Apu Sarkar
3 citations
Tungsten
Metal and Thin Film Mechanics
0.81
article

Understanding dislocation velocity in TaW using explainable machine learning

A. Kedharnath, Rajeev Kapoor, Apu Sarkar
article en
3 citations

Abstract

Abstract 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 ( $${\tau }_{\text{app}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>τ</mml:mi> <mml:mtext>app</mml:mtext> </mml:msub> </mml:math> ), 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 $${\tau }_{\text{app}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>τ</mml:mi> <mml:mtext>app</mml:mtext> </mml:msub> </mml:math> as the most influencing parameter affecting velocity, followed by slip plane, temperature, and W addition. SHAP estimated the base velocity value ( $${v}_{\text{b}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>v</mml:mi> <mml:mtext>b</mml:mtext> </mml:msub> </mml:math> ) to be 1376 m·s -1 . $${v}_{\text{b}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>v</mml:mi> <mml:mtext>b</mml:mtext> </mml:msub> </mml:math> was calculated by training SHAP on a parameter-less model. $${v}_{\text{b}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>v</mml:mi> <mml:mtext>b</mml:mtext> </mml:msub> </mml:math> could be increased by applying $${\tau }_{\text{app}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>τ</mml:mi> <mml:mtext>app</mml:mtext> </mml:msub> </mml:math> 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 $${v}_{\text{b}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>v</mml:mi> <mml:mtext>b</mml:mtext> </mml:msub> </mml:math> on deformation was established.

TungstenVol. 7(2)
Bhabha Atomic Research Centre (IN), Homi Bhabha National Institute (IN)
Bhabha Atomic Research Centre
Openalex Percentile: Top 31%
Metal and Thin Film Mechanics
0.81
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