Scaling regimes and fluctuations of observables in computer glasses approaching the unjamming transition
Data files
Jan 09, 2024 version files 77.44 MB

chi_diluted_nets_N_dz.csv

chi_packing_nets_N8000_dz.csv

chi_packings_N_pressure.csv

histograms_N8000_pressure_dz.csv

histograms_N8000_pressure_mu.csv

packing_dipole_response_N102400_p1e1.csv

packing_dipole_response_N102400_p1e2.csv

packing_dipole_response_N102400_p1e3.csv

packing_dipole_response_N102400_p1e4.csv

packing_dipole_response_N102400_p1e5.csv

packing_dipole_response_N102400_p1e6.csv

README.md
Abstract
This dataset can be used to reproduce the figures in the corresponding manuscript, in preparation for publication in the Journal of Chemical Physics. As described in the manuscript, the (mostly statistical) quantities presented here were computed from ensembles of simulated disordered solids (harmonic sphere packings, diluted spring networks, and packing derived networks). The individual simulations involve the minimization of the system's energy which is computed from positional degrees of freedom (of particles in a packing or nodes in a network). Macroscopic quantities (such as shear modulus or contact number/excess connectivity) are then computed for each sample. The displacement response feilds are computed by solving a linear matrix equation involving the Hessian of 2D packings of harmonic spheres. See the manuscript for more details.
README
Dataset: Scaling regimes and fluctuations of observables in computer glasses
approaching the unjamming transition
J. A. Giannini, E. Lerner, F. Zamponi, M. L. Manning
This dataset can be used to reproduce the figures in the corresponding
manuscript, in preparation for publication in the Journal of Chemical
Physics. As described in the manuscript, the (mostly statistical) quantities
presented here were computed from ensembles of simulated disordered solids
(harmonic sphere packings, diluted spring networks, and packing derived
networks). The individual simulations involve the minimization of the system's
energy which is computed from positional degrees of freedom (of particles in a
packing or nodes in a network). Macroscopic quantities (such as shear modulus
or contact number/excess connectivity) are then computed for each sample. The
displacement response feilds are computed by solving a linear matrix equation
involving the Hessian of 2D packings of harmonic spheres. See the manuscript
for more details.
Description of dataset and files
The dataset is comprised of 11 CSV files, each containing a different type of
data (histogram, disorder quantifier, displacement response fields). Each CSV
has a header which describes the data contained in each column. The variables in
each column are also described further below. The manuscript figures can be
plotted directly using the attached Python file. Short descriptions of each file
are below.
 chi_diluted_nets_N_dz.csv : System size, coordination, and shear modulus disorder quantifer data for ensembles of diluted spring networks.
 chi_packing_nets_N8000_dz.csv : System size, coordination, and shear modulus disorder quantifier data for ensembles of packing derived spring networks.
 chi_packings_N_pressure.csv : System size, pressure, coordination, contact number disorder quantifier, and shear modulus disorder quantifer for ensembles of 3D harmonic sphere packings.
 histograms_N8000_pressure_dz.csv : Distributions of excess contact numbers for ensembles of packings with N = 8000 particles and varying pressure.
 histograms_N8000_pressure_mu.csv : Distributions of shear moduli for ensembles of packings with N = 8000 particles and varying pressure.
 packing_dipole_response_N102400_p1e1.csv : This and the below files contain the positions and radii of particles in example 2D harmonic sphere packings with N = 102400 particles prepared at different pressures. Additionally, they contain the components of displacement vector fields that constitute the system's response to applied force dipoles.
 packing_dipole_response_N102400_p1e2.csv
 packing_dipole_response_N102400_p1e3.csv
 packing_dipole_response_N102400_p1e4.csv
 packing_dipole_response_N102400_p1e5.csv
 packing_dipole_response_N102400_p1e6.csv
Description of variables (in each file, by column)
NOTE: the following variables are reported in the natural
units of the simulations, described in the manuscript and references
therein.
chi_diluted_nets_N_dz.csv:
 N: Number of nodes in each realization in the ensemble of diluted spring networks studied at a given mean excesss connectivity dz.
 dz: Mean excess connectivity of spring networks in ensemble
 chi_G: Shear modulus disorder quantifier measured for ensemble of diluted spring networks at given system size N and mean excess connectivity dz (see manuscript for further details).
chi_packing_nets_N8000_dz.csv:
 N: Number of nodes in each realization in the ensemble of packing derived spring networks studied at a given mean excess connectivity dz.
 dz: Mean excess connectivity of spring networks in ensemble
 chi_G: Shear modulus disorder quantifier measured for ensemble of packing derived spring networks at given system size N and mean excess connectivity dz (see manuscript for further details).
chi_packings_N_pressure.csv:
 N: Number of particles in each realization in the ensemble of harmonic sphere packings studied at a given overall pressure p and mean excess connectivity dz.
 p: Overall pressure of sphere packings in ensemble
 dz: Mean excess connectivity of spehere packings in ensemble
 chi_Z: Contact number disorder quantifier measured for ensemble of sphere packings at given system size N, pressure p, and mean excess connectivity dz (see manuscript for further details).
 chi_mu_med: Shear modulus disorder quantifier measured for ensemble of sphere packings at given system size N, pressure p, and mean excess connectivity dz. Measured with median method as described in the manuscript.
histograms_N8000_pressure_dz.csv:
 p: Overall pressure of packings in ensemble of sphere packings with N = 8000 particles. Histograms are computed for ensembles at each pressure.
 dz/< dz >: Center of a given histogram bin of mean excess connectivity normalized by the mean for the entire ensemble of packings with given pressure p and N = 8000 particles.
 P(dz/< dz >): Value of histogram at given dz/< dz > bin for ensemble of packings with given pressure p and N = 8000 particles. The histograms are normalized to have unit area.
histograms_N8000_pressure_mu.csv:
 p: Overall pressure of packings in ensemble of sphere packings with N = 8000 particles. Histograms are computed for ensembles at each pressure.
 mu/< mu >: Center of a given histogram bin of shear modulus normalized by the mean shear modulus for the entire ensemble of packings with given pressure p and N = 8000 particles.
 P(mu/< mu >): Value of histogram at given mu/< mu > bin for ensemble of packings with given pressure p and N = 8000 particles. The histograms are normalized to have unit area.
NOTE: Data files with names of the following form are all organized the same
way, and contain data describing 2D sphere packings with N = 102400 particles
as well as fields of putative displacement (socalled 'displacement response
fields') on those packings. The packings are prepared at varying overall
pressure p (represented in the file name by 'p< SOME PRESSURE >.'). See the
manuscript for more details regarding the preparation of the sphere packings
and the computation of the displacement fields.
 packing_dipole_response_N102400_p< SOME PRESSURE >.csv:
 r_i_x: The xcoordinate of particle i in the packing
 r_i_y: The ycoordinate of particle i in the packing
 R_i: The radius of particle i in the packing
 v_i_x: The xcomponent of the displacement field on particle i
 v_i_y: The ycomponent of the displacement field on particle i
Sharing/Access information
The ensembles of packings and spring networks that were used to compute the
quantities in this dataset are too large to easily make opensource. This
ensemble data can be shared upon reasonable request to the authors, as can
related simulation and analysis code.
Reference for corresponding publication:
Giannini, J. A., Lerner, E., Zamponi, F., Manning, M. L., J. Chem. Phys.
160, (2024); doi: 10.1063/5.0176713Recommended citation for this dataset:
Giannini, Julia; Lerner, Edan; Zamponi, Francesco; Manning, M. Lisa
(2024). Scaling regimes and fluctuations of observables in computer
glasses approaching the unjamming transition [Dataset]. Dryad.
https://doi.org/10.5061/dryad.69p8cz97c
Plotting software
The attached Python file can be used to load and plot the data from the CSVs.
For ease of use and readability, some functionality is achieved by commenting
or uncommenting small blocks of code. The script is commented to describe how
the data is loaded, organized, and plotted. The figures that the script produces
are slightly more simplistic than those in the manuscript, to minimize the
number of necessary Python libraries.
Methods
This data was collected from large ensembles of individual C and Python simulations of disordered solids. The statistical quantites reported here were computed in Python (using mathematics and scientific computing packages (numpy, scipy, pandas)) from the ensembles of simulations, and exported to CSVs for ease of access.