DOI: 10.5061/dryad.69p8cz97c
title: Scaling regimes and fluctuations of observables in computer glasses approaching the unjamming transition

description: 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: 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.
description: ## 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. 1. chi_diluted_nets_N_dz.csv :
 System size, coordination, and shear modulus disorder quantifer data for
 ensembles of diluted spring networks. 2. chi_packing_nets_N8000_dz.csv :
 System size, coordination, and shear modulus disorder quantifier data for
 ensembles of packing derived spring networks. 3.
 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. 4.
 histograms_N8000_pressure_dz.csv : Distributions of excess contact numbers
 for ensembles of packings with N = 8000 particles and varying pressure. 5.
 histograms_N8000_pressure_mu.csv : Distributions of shear moduli for
 ensembles of packings with N = 8000 particles and varying pressure. 6.
 packing_dipole_response_N102400_p1e-1.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. 7.
 packing_dipole_response_N102400_p1e-2.csv 8.
 packing_dipole_response_N102400_p1e-3.csv 9.
 packing_dipole_response_N102400_p1e-4.csv 10.
 packing_dipole_response_N102400_p1e-5.csv 11.
 packing_dipole_response_N102400_p1e-6.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/&lt; dz &gt;:
 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/&lt; dz &gt;): Value of histogram at given dz/&lt;
 dz &gt; 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/&lt; mu &gt;: 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/&lt; mu &gt;): Value of histogram at given mu/&lt; mu
 &gt; 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 (so-called 'displacement
 response fields') on those packings. The packings are prepared at
 varying overall pressure p (represented in the file name by 'p&lt;
 SOME PRESSURE &gt;.'). 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&lt; SOME PRESSURE
 &gt;.csv: * r_i_x: The x-coordinate of particle i in the packing * r_i_y:
 The y-coordinate of particle i in the packing * R_i: The radius of
 particle i in the packing * v_i_x: The x-component of the displacement
 field on particle i * v_i_y: The y-component 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 open-source. 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:
 10.1063/5.0176713 * Recommended 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.
 ## 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 un-commenting 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.