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10 results for “glass transition”
A localization transition underlies the mode-coupling crossover of glasses
<p>This dataset is associated to "A localization transition underlies the mode-coupling crossover of glasses" by D. Coslovich, A. Ninarello and L. Berthier [<a href="https://arxiv.org/abs/1811.03171">https://arxiv.org/abs/1811.03171</a>].</p> <p>It includes post-processed data and workflow to reproduce the analysis and the figures of the article and of the supplemental information.</p> <p><strong>Supplementary information is available in the Supplement section of the project document (project.pdf).</strong></p> <p>The easiest way to reproduce the analysis and figures, and then check the results, is to use the make script:</p> <pre><code class="language-bash">./make all</code></pre> <p>Alternatively, the analysis and figures can be reproduced in any of the following ways</p> <ul> <li>following the workflow described in the <a href="https://orgmode.org">org-mode</a> project file project.org</li> <li>using the individual bash and gnuplot scripts in src/ and plots/</li> </ul> <p>Folders and files description:</p> <ul> <li>analysis/: post-processed data</li> <li>src/: bash, python and gnuplot scripts needed to reproduce the analysis</li> <li>plots/: eps figures that appear in the paper and supplemental information and associated gnuplot scripts</li> <li>make: convenience script to setup the python environment, analyze the data and reproduce the figures</li> <li>project.org: org-mode project file with workflow and supplemental information</li> <li>project.pdf: pdf project file with workflow and supplemental information</li> <li>project.bib: bibtex bibliography associated to the project</li> <li>project.setup: org-mode export configuration</li> </ul> <p>Dependencies:</p> <ul> <li>numpy (1.21.6)</li> <li>scipy (1.11.1)</li> <li>argh (0.26.2)</li> <li><a href="https://pypi.org/project/atooms/">atooms</a> (1.9.1)</li> <li>gnuplot (5.0.0)</li> </ul> <p>The analysis scripts have been tested with python 3.8. The org-mode project file has been tested with org version 9.1.13.</p> <p>Note: this dataset does not contain (at least yet) the particle configurations associated to saddle points, only the post-processed files containing selected properties of their normal modes.</p> <p>Changelog:</p> <ul> <li>1.2.2 <ul> <li>fix requirements</li> </ul> </li> <li>1.2.1 <ul> <li>fix ./src/adiff.py</li> <li>fix final check of ./make all</li> <li>improve pdf layout</li> <li>improve handling of org properties</li> </ul> </li> <li> <ul> </ul> </li> <li> <ul> </ul> </li> <li>1.2.0 <ul> <li>add analysis of eigenvector-following optimizations</li> <li>small changes and fixes to analysis scripts</li> </ul> </li> <li>1.1.0 <ul> <li>add "all" target to ./make</li> <li>fix ./make check</li> <li>improve setup description</li> </ul> </li> <li>1.0.0 <ul> <li>initial submission</li> </ul> </li> </ul>
Scaling regimes and fluctuations of observables in computer glasses approaching the unjamming transition
<p>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.</p>
X-ray scattering datasets associated with the publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation"
<p>X-ray scattering datasets for samples described in the 2020 publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation". This dataset includes both raw and processed X-ray scattering data for samples PTCHSiO-Pr, Bu, Hx, Oc and De, alongside background measurements files (BKG).</p>
Scaling regimes and fluctuations of observables in computer glasses approaching the unjamming transition
Open the record for dataset details and reuse information.
Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network
<p>Source files used to produce the publication "Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network."</p>
Dataset for "Predicting the influence of particle size on the glass transition temperature and viscosity of secondary organic material"
<p>The archive file predictingInfluenceParticleSize2020.zip contains scripts and data to generate the figures in the manuscript. Datafiles are in the src/Data folder. Scripts are written in the Julia language. </p> <p>The file predicting_influence_particle2020.tar.gz contains a Docker container. The docker container is a virtual machine that contains all software and dependencies needed to execute the code. The freely available Docker engine must be installed on the local computer (https://docs.docker.com/install/). To install the container run</p> <p>docker load < predicting_influence_particle2020.tar.gz</p> <p>It can be started through the command:</p> <p>docker run -it -p 8888:8888 mdpetters/predicting_influence_particle2020:final</p> <p>Please refer to the supplement of the paper for further instructions.</p>
The rehydration attributes and quality characteristics of 'Quick-cooking' dehydrated beans: Implications of glass transition on storage stability
<p>The data set was used to generate the figures.</p>
Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study - dataset
<p>Abstract:<br>from [1]</p> <p>Polymer nanocomposites (PNCs) have shown great potential to meet the ever-growing requirements of modern engineering applications. Nowadays, molecular dynamics (MD) simulations are increasingly employed to complement experimental work and thereby gain a deeper understanding of the complex structure–property relations of PNCs. However, with respect to the thermoplastic’s mechanical behavior, the role of its average molar mass is rarely addressed, and many MD studies only consider uniform (monodispersed) polymers. Therefore, this contribution investigates the impact that and the dispersity Đ have on the stiffness and strength of PNCs through coarse-grained MD. To this end, we employed a Kremer–Grest bead–spring model and observed the expected increase in the mechanical performance of the neat polymer for larger . Our results indicated that the unimodal molar mass distribution does not impact the mechanical behavior in the investigated dispersity range Đ. For the PNC, we obtained the same -dependence and Đ-independence of the mechanical properties over a wide range of filler sizes and contents. This contribution proves that even simple MD models can reproduce the experimentally well researched effect of the molar mass. Hence, this work is an important step in understanding the complex structure–property relations of PNCs, which is essential to unlock their full potential.</p> <p>Contact:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p>Software:</p> <p>All MD simulations were performed with LAMMPS [2,3], version: 23 Oct 2022 / 20220623</p> <p>Compiled with<br>Compiler: GNU C++ 11.2.0 with OpenMP not enabled<br>C++ standard: C++11</p> <p>Active compile time flags:<br>-DLAMMPS_GZIP<br>-DLAMMPS_SMALLBIG</p> <p>Installed packages:<br>CLASS2 DPD-BASIC EXTRA-DUMP INTEL KSPACE MANYBODY MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT PERI</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p>License:</p> <p>Creative Commons Attribution 4.0 International</p> <p>Context:</p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>Content:</p> <p>structure of data set:</p> <p> -01_neat <br> containing the neat polymer simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -02_distributed<br> containing samples with distributed chain lengths<br> -100-dist<br> samples with mean molar mass 100<br> -200-dist<br> samples with mean molar mass 200<br> -02_PNC<br> containing the polymer nanocomposite simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> -02_distributed<br> containing samples with distributed chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> </p> <p>naming convention for simulation folders</p> <p> - neat polymer simulations<br> example: GTP_UT_num_chains-80_num_beads_per_chain-500-8<br> * num_chains: number of polymer chains<br> * num_beads_per_chain: molar mass (chain length)<br> * distribution: standard deviation of gauss distribution govering dispersity<br> * "trailing number": batch number of sample<br> <br> - polymer nanocomposite simulations<br> example: GTP_rF-5_nF-10_chainlen-5_7-T_0.2<br> * rF: nanofiller radius<br> * nF: number of nanofillers<br> * chainlen: molar mass (chain length)</p> <p> </p> <p>Each simulation directory contains:</p> <p> lammps input file (*.in) of the specific simulation</p> <p> data file (*.data) containing the initial sample configuration</p> <p> input.prm: input parameters of the specific simulation (read by the input file)</p> <p> meta.info: meta data of the specific simulation run</p> <p> LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <p> thermo_out.Dat: raw output </p> <p> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> <p> thermo_out_STD.Dat: standard deviation of raw output</p> <p>Output quantities (columns of *.Dat files):<br>Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <p> Step: time step </p> <p> Time: time </p> <p> TotEng: total energy </p> <p> PotEng: potential energy</p> <p> KinEng: kinetic energy </p> <p> E_pair: pair energy </p> <p> E_bond: bond energy </p> <p> E_angle: angle energy </p> <p> E_dihed: dihedral energy </p> <p> Temp: temperature</p> <p> Press: hydrostatic pressure</p> <p> Pxx: xx component of pressure tensor </p> <p> Pyy: yy component of pressure tensor </p> <p> Pzz: zz component of pressure tensor </p> <p> Pxy: xy component of pressure tensor</p> <p> Pxz: xz component of pressure tensor</p> <p> Pyz: yz component of pressure tensor</p> <p> Volume: volume of simulation box </p> <p> Lx: box length in x direction </p> <p> Ly: box length in y direction </p> <p> Lz: box length in z direction </p> <p> Density: density </p> <p> c_RG: radius of gyration scalar </p> <p> c_RG[1]: squared radius of gyration tensor (xx component) </p> <p> c_RG[2]: squared radius of gyration tensor (yy component) </p> <p> c_RG[3]: squared radius of gyration tensor (zz component) </p> <p> c_RG[4]: squared radius of gyration tensor (xy component) </p> <p> c_RG[5]: squared radius of gyration tensor (xz component) </p> <p> c_RG[6]: squared radius of gyration tensor (yz component) </p> <p> c_bondave[1]: bond energy averaged over all atoms </p> <p> c_bondave[2]: bond distance averaged over all atoms </p> <p> c_bondave[3]: squared bond distance averaged over all atoms </p> <p> c_angleave[1]: angle energy averaged over all atoms </p> <p> c_angleave[2]: angle averaged over all atoms degree</p> <p> c_angleave[3]: cosine of angle </p> <p> c_angleave[4]: squared cosine of angle </p> <p> c_MSD[1]: mean squared displacement x-direction </p> <p> c_MSD[2]: mean squared displacement y-direction </p> <p> c_MSD[3]: mean squared displacement z-direction </p> <p> c_MSD[4]: total mean squared displacement </p> <p> c_COM[1]: x coordinate of center of mass </p> <p> c_COM[2]: y coordinate of center of mass </p> <p> c_COM[3]: z coordinate of center of mass </p> <p> v_strain_xx: xx component of engineering strain tensor </p> <p> v_strain_yy: yy component of engineering strain tensor </p> <p> v_strain_zz: zz component of engineering strain tensor </p> <p> v_vMisesequivstress: von Mises equivalent stress </p> <p> v_Cauchy_xx: xx component of stress tensor </p> <p> v_Cauchy_yy: yy component of stress tensor</p> <p> v_Cauchy_zz: zz component of stress tensor</p> <p> v_Cauchy_xy: xy component of stress tensor </p> <p> v_Cauchy_xz: xz component of stress tensor </p> <p> v_Cauchy_yz: yz component of stress tensor </p> <p> v_strain_xy: xy component of engineering strain tensor </p> <p> v_strain_xz: xz component of engineering strain tensor </p> <p> v_strain_yz: yz component of engineering strain tensor </p> <p>References:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022.</p> <p>[4] J. Roksvaag, M.Ries . “A fast self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, manuscript in preparation</p>
Supporting video for "Temperature Modulated Micromechanical Thermal Analysis with Microstring Resonators Detects Multiple Coherent Features of Small Molecule Glass Transition"
<p>Supporting video for "Temperature Modulated Micromechanical Thermal Analysis with Microstring Resonators Detects Multiple Coherent Features of Small Molecule Glass Transition" . 720p Version. </p>
LAMMPS input files and analysis for estimating the glass transition temperature using ensemble molecular dynamcs of a selection of epoxy resins using different protocols for computing the density-temperature behaviour.
<p>LAMMPS input files and analysis for estimating the glass transition temperature using ensemble molecular dynamcs of a selection of epoxy resins using different protocols for computing the density-temperature behaviour. </p>
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