DOI: 10.6084/m9.figshare.7941014
title: Plastic Heterogeneity Data of CuZr/AlSm/NiNb/FeP Metallic Glasses

description: This group of datasets contain the atomic-scale plastic heterogeneity data of a series of Cu-Zr (Cu<sub>65</sub>Zr<sub>35</sub>, Cu<sub>50</sub>Zr<sub>50</sub>, Cu<sub>80</sub>Zr<sub>20</sub>), Al<sub>90</sub>Sm<sub>10</sub>, Ni<sub>62</sub>Nb<sub>38</sub> and Fe<sub>80</sub>P<sub>20</sub> metallic glasses (in CSV format). In particular, a row of each CSV file corresponds to an atom, and includes the following information:<b>["id"]</b> atom id;<b>["type"]</b> atom type;<b>["x"]</b> coordinate x of the atom in the undeformed, quenched configuration;<b>["y"]</b> coordinate y of the atom in the undeformed, quenched configuration;<b>["z"]</b> coordinate z of the atom in the undeformed, quenched configuration;<b>["nonaffine_displacement"]</b> non-affine displacement of the atom at a compressive strain of 4.0% with reference to the original quenched configuration;<b>["note"]</b> denotes whether the atom is within the two compressive ends ("compr_ends") or close to the boundary ("bds"). After featurization, these atomic data could be eliminated from later machine learning.We simulate liquid melt quenching and deformation of Cu<sub>65</sub>Zr<sub>35</sub>, Cu<sub>50</sub>Zr<sub>50</sub>, Cu<sub>80</sub>Zr<sub>20</sub>, Ni<sub>62</sub>Nb<sub>38</sub>, Al<sub>90</sub>Sm<sub>10</sub> and Fe<sub>80</sub>P<sub>20</sub> metallic glasses using molecular dynamics simulations. We use 3 quenching rates of 5 × 10<sup>10</sup>, 5 × 10<sup>11</sup> and 5 × 10<sup>12</sup> K s<sup>-1</sup> for Cu-Zr metallic glasses, and 5 × 10<sup>10</sup> K s<sup>-1</sup> for the other metallic glasses. We construct 3 large slab samples for each Cu-Zr glass, each of which contains 345600 atoms with dimensions ~120 (X) × 24 (Y) ×240 (Z) Å<sup>3</sup>. Data from 2 glass samples are concatenated, equally undersampled and used in 5-fold cross-validation training the ML models, whereas the remaining sample is set-aside for rigorous generalization tests. For Ni<sub>62</sub>Nb<sub>38</sub>, Al<sub>90</sub>Sm<sub>10</sub> and Fe<sub>80</sub>P<sub>20</sub> metallic glasses, we construct samples of 131072 atoms. The timestep is 1 fs. During simulation, the initial configuration is built by randomly substituting into an fcc (Cu-Zr, N<sub>i62</sub>Nb<sub>38</sub> and Al<sub>90</sub>Sm<sub>10</sub>) or bcc (Fe<sub>80</sub>P<sub>20</sub>) lattice. The samples are annealed at 2000 K for 1 ns, quenched to 50 K with each quenching rate, and relaxed at 50 K for 1 ns. After quenching, the Cu-Zr MGs are compressed along Z axis under a strain rate of 2.5 × 10<sup>7</sup> s<sup>-1</sup> in a quasi-static mode (constantly apply a small strain and then relax, up to the strain of 10%) at a low temperature of 50 K (see Supplementary Figure 3 for typical stress-strain curves). Periodic boundary conditions (PBCs) are imposed in Y and Z axes and free surfaces are applied along X axis to allow shear offsets. For Ni<sub>62</sub>Nb<sub>38</sub>, Al<sub>90</sub>Sm<sub>10</sub> and Fe<sub>80</sub>P<sub>20</sub> we simulate both tensile and compressive deformation with strain rates of 2.5 × 10<sup>7</sup> s<sup>-1</sup> and 1.0 × 10<sup>8</sup> s<sup>-1</sup> as well as with PBCs in all directions (the data of compressive deformation under 2.5 × 10<sup>7</sup> s<sup>-1</sup> with PBC in all directions are presented here). After feature extraction, we select atoms of ~10 - 20 Å away from the surfaces or deformation ends to construct the ML datasets. We analyze non-affine displacement D<sup>2</sup> of each atom at a strain of 4.0% with reference to the undeformed configuration as a signature of plastic heterogeneity. Please see more details of D<sup>2</sup> in ML Falk, Phys. Rev. E 57, 7192-7205 (1998).<br> <b>Note on data source</b>This data is part of our paper:Qi Wang, Anubhav Jain. A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses. Nature Communications 10, 5537 (2019). https://doi.org/10.1038/s41467-019-13511-9. If you found this dataset useful and would like to cite it in your work, please be sure to cite its original sources below rather than or in addition to this page.