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Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set

Abstract: from [1] <blockquote> Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites. </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4] Post-processing Matlab R2019b License: Creative Commons Attribution 4.0 International Context: Data set supplementing  journal paper: [1] M. Ries, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” Forces in Mechanics, vol. 12, p. 100 207, 2023. Content: structure of data set: <ul> <li>04_Equilibration folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content variation of filler content</li> <li>02_grafting_density variation of grafting density</li> <li>03_grafting_potential variation of grafting potential</li> <li>04_filler_size variation of filler size</li> <li>05_reference reference samples without grafting</li> </ul> </li> <li>05_UT folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content variation of filler content</li> <li>02_grafting_density variation of grafting density</li> <li>03_grafting_potential variation of grafting potential</li> <li>04_filler_size variation of filler size</li> <li>05_reference reference samples without grafting</li> </ul> </li> </ul> Each simulation directory contains: <ul> <li> lammps input file (*.in) of the specific simulation </li> <li> data file (*.data) containing the initial sample configuration </li> <li> input.prm: input parameters of the specific simulation (read by the input file) </li> <li> meta.info: meta data of the specific simulation run </li> <li> LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li> thermo_out.Dat: raw output  </li> <li> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter) </li> <li> thermo_out_STD.Dat: standard deviation of raw output </li> </ul> </li> </ul> Output quantities (columns of *.Dat files): 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]. <ul> <li> Step: time step  </li> <li> Time: time  </li> <li> TotEng: total energy  </li> <li> PotEng: potential energy </li> <li> KinEng: kinetic energy  </li> <li> E_pair: pair energy  </li> <li> E_bond: bond energy  </li> <li> E_angle: angle energy  </li> <li> E_dihed: dihedral energy  </li> <li> Temp: temperature </li> <li> Press: hydrostatic pressure </li> <li> Pxx: xx component of pressure tensor  </li> <li> Pyy: yy component of pressure tensor  </li> <li> Pzz: zz component of pressure tensor  </li> <li> Pxy: xy component of pressure tensor </li> <li> Pxz: xz component of pressure tensor </li> <li> Pyz: yz component of pressure tensor </li> <li> Volume: volume of simulation box  </li> <li> Lx: box length in x direction   </li> <li> Ly: box length in y direction   </li> <li> Lz: box length in z direction   </li> <li> Density: density   </li> <li> c_RG: radius of gyration scalar  </li> <li> c_RG[1]: squared radius of gyration tensor (xx component)   </li> <li> c_RG[2]: squared radius of gyration tensor (yy component)   </li> <li> c_RG[3]: squared radius of gyration tensor (zz component)   </li> <li> c_RG[4]: squared radius of gyration tensor (xy component)   </li> <li> c_RG[5]: squared radius of gyration tensor (xz component)   </li> <li> c_RG[6]: squared radius of gyration tensor (yz component)   </li> <li> c_bondave[1]: bond energy averaged over all atoms   </li> <li> c_bondave[2]: bond distance averaged over all atoms   </li> <li> c_bondave[3]: squared bond distance averaged over all atoms   </li> <li> c_angleave[1]: angle energy averaged over all atoms   </li> <li> c_angleave[2]: angle averaged over all atoms degree </li> <li> c_angleave[3]: cosine of angle  </li> <li> c_angleave[4]: squared cosine of angle  </li> <li> c_MSD[1]: mean squared displacement x-direction   </li> <li> c_MSD[2]: mean squared displacement y-direction   </li> <li> c_MSD[3]: mean squared displacement z-direction   </li> <li> c_MSD[4]: total mean squared displacement   </li> <li> c_COM[1]: x coordinate of center of mass   </li> <li> c_COM[2]: y coordinate of center of mass   </li> <li> c_COM[3]: z coordinate of center of mass   </li> <li> v_strain_xx: xx component of engineering strain tensor    </li> <li> v_strain_yy: yy component of engineering strain tensor     </li> <li> v_strain_zz: zz component of engineering strain tensor     </li> <li> v_vMisesequivstress: von Mises equivalent stress  </li> <li> v_Cauchy_xx: xx component of stress tensor   </li> <li> v_Cauchy_yy: yy component of stress tensor </li> <li> v_Cauchy_zz: zz component of stress tensor </li> <li> v_Cauchy_xy: xy component of stress tensor  </li> <li> v_Cauchy_xz: xz component of stress tensor  </li> <li> v_Cauchy_yz: yz component of stress tensor  </li> <li> v_strain_xy: xy component of engineering strain tensor    </li> <li> v_strain_xz: xz component of engineering strain tensor    </li> <li> v_strain_yz: yz component of engineering strain tensor    </li> </ul> References: [1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” Forces in Mechanics, vol. 12, p. 100 207, 2023. [2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19. [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. [4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, Zenodo, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a>

Data for The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica

This is the data used in the paper "The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica"

Dataset: Multidimensional surrogate modelling for Airborne TDEM data

DataHF contains the synthetic data for a SkyTEM 304 TDEM system, flying at 40 m height for a two-layered model with an interface at an angle, described in parameters.csv, via 3D simulations. DataLF contains the 1D data (without angle) with a 1D analytical forward model. Look out for the published PhD dissertation "Improving Airborne Time-Domain Electromagnetic Imaging with Applications to Groundwater Salinity Mapping" for a description of the dataset in Chapter 5.

opencc-zeroSep 2023View details →

dryad40/100

Data and code on the Moral Machine experiment on large language models (LLMs)

As large language models (LLMs) have become more deeply integrated into various sectors, understanding how they make moral judgments has become crucial, particularly in the realm of autonomous driving. This study utilized the Moral Machine framework to investigate the ethical decision-making tendencies of prominent LLMs, including GPT-3.5, GPT-4, PaLM 2, and Llama 2, to compare their responses to human preferences. While LLMs' and humans' preferences such as prioritizing humans over pets and favoring saving more lives are broadly aligned, PaLM 2 and Llama 2, especially, evidence distinct deviations. Additionally, despite the qualitative similarities between the LLM and human preferences, there are significant quantitative disparities, suggesting that LLMs might lean toward more uncompromising decisions, compared to the milder inclinations of humans. These insights elucidate the ethical frameworks of LLMs and their potential implications for autonomous driving.

Underlying data for: "Morphology as indicator of adaptive changes of model tissues in osmotically and chemically changing environments"

For our publication "Morphology as indicator of adaptive changes of model tissues in osmotically and chemically changing environments" (at <a href="https://doi.org/10.1016/j.bioadv.2023.213635">DOI: 10.1016/j.bioadv.2023.213635)</a> we here provide the raw data for the included plots. Due to the aggregate size of the numerous microscopy images, those are provided on request. Herein, we provide the data obtained by processing the microscopy images into cell nuclei positions, cell density profiles and morpological as well as topological parameters.

opencc-by-4.0Jun 2023View details →

A human genome editing-based MLL-AF4 acute lymphoblastic leukemia model recapitulates key cellular and molecular leukemogenic features. (Processed data)

The prognosis of infant B-cell acute lymphoblastic leukemia (iB-ALL) remains dismal, especially in patients harboring the MLL-AF4 (KTM2A-AFF1) rearrangement, which arises prenatally in early hematopoietic stem/progenitor cells (HSPCs) and accounts for 80% of iB-ALL and 10% of non-infant cases. MLL-AF4+ B-ALL shows a bimodal localization of the MLL gene breakpoint within the MLL break cluster region, and two subgroups of patients based on the gene expression pattern of the HOXA/MEIS cluster have been identified. The pathogenic mechanisms in MLL- AF4+ B-ALL are challenging to study functionally due to the absence of faithful human cellular models recapitulating the disease phenotype and latency. Here, we assess the molecular contribution and leukemogenic capacity of MLL breakpoints occurring in either intron 10 (MLL i10 , centromeric) or intron 12 (MLL i12 , telomeric) in ontogenically-different human HSPCs sourced prenatally (fetal liver) and neonatally (cord blood). CRISPR-Cas9-induced MLL-AF4 (MA) targeting either MLL i10 (M i10 A) or MLL i12 (M i12 A) causes MA-driven in vitro myeloid immortalization in both fetal liver- and cord blood-CD34+ HSPCs. The centromeric location of the MLL breakpoint, but not the cellular ontogeny, determined the expression of HOXA/MEIS1 genes in MLL-edited cells. Centromeric MLL breakpoints endowed  enhanced myeloid clonogenic replating to MLL- edited CD34+ HSPCs. The cellular ontogeny and the location of the MLL breakpoint also influenced the capacity of MLL-edited CD34+ HSPCs to initiate pro-B-ALL in vivo, which faithfully recapitulated the molecular, transcriptomic and methylome profiles of patients with primary MA+ iB-ALL. Our data provide key insights into the cellular and molecular leukemogenic determinants of MA+ iB-ALL. This dataset contains processed RNAseq and DNA methylation data from the abovementioned study.

Terrestrial laser scanning data of urban trees in Milton Keynes, UK: individual trees and Treegraph model outputs

TLS_point_clouds: - Data collection: Terrestrial laser scanning data acquired in leaf-off condition in April 2021. - Scanning instrument: We used a RIEGL VZ-400 with a wavelength of 1550 nm, 0.35 mrad beam divergence and 0.04˚ angular resolution. - Locations: The data were collected from three urban sites in Milton Keynes, UK: Avebury Blvd (Ave), Dansteed Way (Dan), and Overgate (Ove). - File information: Each file is an individual tree point cloud in Polygon File Format (.ply), which can be viewed in software such as CloudCompare.   Treegraph_outputs: - Description: Model outputs from Treegraph for individual trees. - File naming convention: [TreeID]-[downsample_voxel_length]-[tip_diameter_if_known].* <ul> <li>*.centres.ply: Skeleton nodes of individual trees.</li> <li>*.mesh.ply: Cylinder model of individual trees.</li> <li>*.json: Geometrical and topological attributes at varying scales, from internode and branch to the whole tree.</li> <li>*.txt: Summary of input parameters, logs of intermediate steps, and a statistical overview of whole-tree structural attributes.</li> </ul>

opencc-by-4.0Sep 2022View details →

Flux data kit, a comprehensive data set of ecosystem fluxes for land surface modelling

<blockquote> Newer versions (>= v3.0) are released by Benjamin Stocker and can be found at his Zenodo account at: https://zenodo.org/records/10885934 </blockquote> The Flux data kit is an effort to expand upon the existing work by Ukolla et a. (2022) to synthesize various sources of ecosystem flux data (i.e. the PLUMBER2 data set, gathered from all major networks). We further expand upon the original data set by integrating data which was either expanded upon (temporally) or where sites were added (e.g. the integration of ICOS data). The effort uses the FluxnetLSM package by the above mentioned authors, as well as their general workflow. In contrast to the PLUMBER2 data set we do not apply stringent quality control, and all quality control on the availability of variables and/or their duration should be done by the user. Furthermore, we include both leaf area index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) in the netcdf output, where PLUMBER2 only provided LAI. On all other parts the formatting and naming conventions as well as quality control specifications remain the same as in PLUMBER2. We therefore refer to Ukolla et al. (2022) for details. Data included The data included consists of three files, containing different versions of the same data. The FLUXDATAKIT_LSM.tar.gz file contains compressed netCDF files compatible with the ALMA scheme for land surface modelling. The FLUXDATAKIT_FLUXNET.tar.gz file contains data in a CSV format according to the FLUXNET specifications. And finally the rsofun_driver_data_clean.rds file is a compressed serialized R file containing data formatted for use with the `rsofun` R package. Data generation Data is generated using the FluxDataKit project. Although this project is not meant for continuous releases, and no support is provided in using this code with data provided AS IS, it might still be useful to some: <a href="https://github.com/geco-bern/FluxDataKit">https://github.com/geco-bern/FluxDataKit</a> The data can be further complimented using the FluxnetEO dataset, which is accessible through the package with the same name as found here: <a href="https://github.com/geco-bern/FluxnetEO">https://github.com/geco-bern/FluxnetEO</a> Acknowledgements The flux data kit is part of the LEMONTREE project and funded by Schmidt Futures and under the umbrella of the Virtual Earth System Research Institute (VESRI). References: <ul> <li>Ukkola, Anna M., Gab Abramowitz, and Martin G. De Kauwe. "A flux tower dataset tailored for land model evaluation." Earth System Science Data 14.2 (2022): 449-461.</li> </ul>

Supplementary Material to 'Exploring the power of data-driven models for groundwater system conceptualization: A case study of the Grazer Feld Aquifer, Austria'

This folder contains the supplementary materials to reproduce the results, tables, and figures from the following publication submitted to the Hydrogeology Journal:  Kokimova A., Collenteur, R.A. & Birk, S. Exploring the power of data-driven models for groundwater system conceptualization: A case study of the Grazer Feld Aquifer, Austria.

Graphic representation of data set for the project "IRI model performance evaluation for the Mexican region"

Here, we illustrate the modeling and experimental results for vertical Total Electron Content (TEC) over Mexico during the five year period 2018-2022. The results were obtained for the UCOE GNSS receiver station (geographic coordinates: 19.6°N; 101.68°W ). The calculations were made each two hours during the whole period under considerations. The modeling results were obtained using the "International Reference Ionosphere (IRI)" model, which is an empirical climatological model based on ground and space observations of the ionosphere [Bilitza et al., 2022].

Pretrained models and simulated data for MICCAI paper Unsupervised Domain Transfer with Conditional Invertible Neural Networks

Simulated data and the pretrained models used for the publication "Unsupervised Domain Transfer with Conditional Invertible Neural Networks", see https://link.springer.com/chapter/10.1007/978-3-031-43907-0_73 published at MICCAI 2023.

Data for ZIP Model, Tanzania Precision Mapping 2021

Socio-demographic data to be imported into the ZIP model

Data --- "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions"

Data to reproduce the results of the manuscript entitled "Optimization of Convolutional Neural Network models for spatially coherent multi-site fire danger predictions" submitted to Geophysical Research Letters. The companion jupyter notebook can be found in DOI: <a href="https://doi.org/10.5281/zenodo.8387558">10.5281/zenodo.8387558</a>