Effect of solute distribution on the screw dislocation motion in bcc Fe-based systems

Barik, Rakesh Kumar; Biswal, Sankalp; Dutta, Ashudeb; Chakrabarti, Debalay; Ghosh, Abhijit

doi:10.1016/j.commatsci.2023.112211

Computational Materials Science

2023

DOI: 10.1016/j.commatsci.2023.112211

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Effect of solute distribution on the screw dislocation motion in bcc Fe-based systems

Rakesh Kumar Barik

Sankalp Biswal

Ashudeb Dutta

et al.

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2024

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Cited by 5 publications

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Understanding dislocation velocity in TaW using explainable machine learning

Kedharnath,

Kapoor,

Sarkar

2024

Tungsten

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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 ($${\tau }_{\text{app}}$$ τ app ), 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 $${\tau }_{\text{app}}$$ τ app as the most influencing parameter affecting velocity, followed by slip plane, temperature, and W addition. SHAP estimated the base velocity value ($${v}_{\text{b}}$$ v b ) to be 1376 m·s-1. $${v}_{\text{b}}$$ v b was calculated by training SHAP on a parameter-less model. $${v}_{\text{b}}$$ v b could be increased by applying $${\tau }_{\text{app}}$$ τ app 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 $${v}_{\text{b}}$$ v b on deformation was established.

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Understanding dislocation velocity in TaW using explainable machine learning

Kedharnath,

Kapoor,

Sarkar

2024

Tungsten

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Investigating the ductile to brittle transition phenomenon in binary Fe-Ni systems using molecular dynamics simulation

Barik,

Jayasree,

Biswal

et al. 2024

Journal of the Mechanics and Physics of Solids

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Modeling of shock wave propagation in porous magnesium based on artificial neural network

Latypov,

Fomin,

Krasnikov

et al. 2024

Mechanics of Materials

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Effect of solute distribution on the screw dislocation motion in bcc Fe-based systems

Cited by 5 publications

References 64 publications

Understanding dislocation velocity in TaW using explainable machine learning

Understanding dislocation velocity in TaW using explainable machine learning

Investigating the ductile to brittle transition phenomenon in binary Fe-Ni systems using molecular dynamics simulation

Modeling of shock wave propagation in porous magnesium based on artificial neural network

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