Context <p>Understanding the shear response of metallic glasses is essential for predicting their mechanical performance and plasticity. Cu<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="894_2025_6307_Article_IEq1.gif" Format="GIF" Height="11" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(_{100-x}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow> <mn>100</mn> <mo>-</mo> <mi>x</mi> </mrow> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation>Zr<InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="894_2025_6307_Article_IEq2.gif" Format="GIF" Height="8" Rendition="HTML" Resolution="72" Type="Linedraw" Width="10" /> </InlineMediaObject> <EquationSource Format="TEX">\(_x\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mi>x</mi> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation> metallic glasses, in particular, exhibit complex shear-thinning behavior governed by atomic composition. Prior studies have highlighted the role of composition in influencing mechanical properties; however, the relationship between the structural characteristics of these alloys and their rheological behavior requires further investigation. This work focuses on the effects of Cu content on the plastic flow of CuZr metallic glasses, emphasizing how atomic-scale features influence yield stress, viscosity, and the onset of plasticity.</p> Methods <p>Molecular dynamics simulations using the embedded atom method potential in LAMMPS were conducted to study Cu<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="894_2025_6307_Article_IEq3.gif" Format="GIF" Height="11" Rendition="HTML" Resolution="72" Type="Linedraw" Width="37" /> </InlineMediaObject> <EquationSource Format="TEX">\(_{100-x}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow> <mn>100</mn> <mo>-</mo> <mi>x</mi> </mrow> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation>Zr<InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="894_2025_6307_Article_IEq4.gif" Format="GIF" Height="8" Rendition="HTML" Resolution="72" Type="Linedraw" Width="10" /> </InlineMediaObject> <EquationSource Format="TEX">\(_x\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mi>x</mi> <mrow /> </mmultiscripts> </math></EquationSource> </InlineEquation> metallic glasses. Samples were equilibrated at 2000&#xa0;K and quenched to 300&#xa0;K at 10<InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="894_2025_6307_Article_IEq5.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\(^{11}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>11</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>&#xa0;K/s. Shear tests at six rates (<InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="894_2025_6307_Article_IEq6.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="52" /> </InlineMediaObject> <EquationSource Format="TEX">\(5 \times 10^{7}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>5</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>7</mn> </msup> </mrow> </math></EquationSource> </InlineEquation> to <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="894_2025_6307_Article_IEq7.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="58" /> </InlineMediaObject> <EquationSource Format="TEX">\(1 \times 10^{10}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>10</mn> </msup> </mrow> </math></EquationSource> </InlineEquation>&#xa0;s<InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="894_2025_6307_Article_IEq8.gif" Format="GIF" Height="11" Rendition="HTML" Resolution="72" Type="Linedraw" Width="18" /> </InlineMediaObject> <EquationSource Format="TEX">\(^{-1}\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </mmultiscripts> </math></EquationSource> </InlineEquation>) were performed at 300&#xa0;K, with the flow stress modeled using the Herschel-Bulkley equation. Structural features were analyzed via Voronoi polyhedra, focusing on local five-fold symmetry and liquid-like polyhedra populations. Visualization and data analysis were conducted using OVITO and Scikit-learn library for the Python programming language.</p>

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Rheological characterization of CuZr metallic glasses at the atomic scale

  • Nicolás Amigo

摘要

Context

Understanding the shear response of metallic glasses is essential for predicting their mechanical performance and plasticity. Cu \(_{100-x}\) 100 - x Zr \(_x\) x metallic glasses, in particular, exhibit complex shear-thinning behavior governed by atomic composition. Prior studies have highlighted the role of composition in influencing mechanical properties; however, the relationship between the structural characteristics of these alloys and their rheological behavior requires further investigation. This work focuses on the effects of Cu content on the plastic flow of CuZr metallic glasses, emphasizing how atomic-scale features influence yield stress, viscosity, and the onset of plasticity.

Methods

Molecular dynamics simulations using the embedded atom method potential in LAMMPS were conducted to study Cu \(_{100-x}\) 100 - x Zr \(_x\) x metallic glasses. Samples were equilibrated at 2000 K and quenched to 300 K at 10 \(^{11}\) 11  K/s. Shear tests at six rates ( \(5 \times 10^{7}\) 5 × 10 7 to \(1 \times 10^{10}\) 1 × 10 10  s \(^{-1}\) - 1 ) were performed at 300 K, with the flow stress modeled using the Herschel-Bulkley equation. Structural features were analyzed via Voronoi polyhedra, focusing on local five-fold symmetry and liquid-like polyhedra populations. Visualization and data analysis were conducted using OVITO and Scikit-learn library for the Python programming language.