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1,726 results for “simulated data”
Size scalability of Monte Carlo simulations applied to oxidized polypyrrole systems: Data and Codes
<p>This work generalizes our recently proposed coarse grained force field (CGFF) for halogen oxidized PPy in the condensed phases and introduces a novel implementation of the Nettropolis Monte Carlo (MMC) simulation based on the CGFF that enables simulations of polymer systems with more than<br>100000 particles. The MMC implementation utilizes a combination of CPU and GPUs and exploits a numerical approximation based on polynomial piecewise interpolation for the calculation of the CGFF pairwise additive terms. Our simulations evidence that the oxidized PPy thermodynamic and structural properties are consistent as the system size is scaled up. Predicted properties include density, enthalpy, potential energy, heat capacity, coefficient of thermal expansion, caloric curve, glass transition temperature range, compressibility, bulk modulus, radial distribution functions, and polymer chain characteristics.</p>
Data for: Simulated postfire tree regeneration suggests reorganization of Greater Yellowstone forests during the 21st century
Tree regeneration underpins forest resilience, but how postfire tree regeneration will change with future climate and fire regimes is difficult to anticipate. Areas of sparse and failed postfire tree regeneration have been documented in western US forests, but how future recovery pathways will unfold is uncertain. We conducted a simulation study in the Greater Yellowstone Ecosystem (GYE; United States) using a process-based model, iLand, to ask how rates, composition, and spatial patterns of postfire tree regeneration vary with 21st-century climate. Subalpine forest and fire dynamics were simulated through 2100 under four climate scenarios, 2 × 2 factorial of aridity (wet and dry) and temperature (warm and hot), in five GYE landscapes. We tallied postfire tree seedling density by species in simulated fires (> 400 ha) at five years postfire. This data set contains three data sets to reproduce analyses for changes rates of regeneration, proportion of burned cells with regeneration failure, and postfire reorganization pathways. We include the data and R scripts used for these three analyses in the publication associated with these data.
Rainfall data from WRF simulations for the Atacama Desert for present and mid-Pliocene climate
<p>We provide model output for rainfall from WRF experiments for the present-day and mid-Pliocene climate. These are netCDF files that contain processed data shown in figures of Reyers et al. (accepted). Details on the files and content are listed in the primary data information Reyers_et_al_primary_data_information.pdf Refer to Reyers et al. (2022) for the full information on the data production and interpretation.</p> <p>This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bb1198. The research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Projektnummer 268236062 – SFB1211 "Earth-evolution at the dry limit" (https://sfb1211.uni-koeln.de/).</p> <p><strong>Reference</strong></p> <p>Reyers, M., Fiedler, S., Ludwig, P., Böhm, C., Wennrich, V., and Shao, Y.: On the importance of moisture conveyor belts from the tropical East Pacific for wetter conditions in the Atacama Desert during the Mid-Pliocene, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2022-72, 2022, accepted.</p>
Simulated galaxy cluster data at z=0 demonstrating the entropy core problem with the SWIFT-EAGLE galaxy formation model
<p>Cluster simulated with the SWIFT hydrodynamic code with the Ref SWIFT-EAGLE model. This dataset contains the redshift 0 snapshot and the VELOCIraptor halo catalogue.</p> <p>Paper reference: https://arxiv.org/abs/2210.09978</p>
Data for: Can fire exclusion zones enhance postfire tree regeneration? A simulation study in subalpine conifer forests
Postfire tree regeneration in forests adapted to infrequent, stand-replacing fire is compromised by climate change and novel fire regimes. We used the individual-based forest simulation model iLand to ask whether mimicking spatial patterns of historical fire mosaics can sustain tree regeneration in a warmer future with more fire. We simulated forest and fire dynamics in Grand Teton National Park under four different climate scenarios, and with eight different scenarios (i.e. spatial configurations) of "fire exclusion zones" (Fx zones). Data were simulated for 2020 - 2100 period, and analyzed early (2026-2050) and late (2076-2100) in the simulation. Here, we present these simulated data and R-scripts to reproduce analyses presented in the associated manuscript (Keller et al. 2025, Ecological Applications). Specifically, our data deposit reproduces analyses for 1) differences in regeneration among scenarios at two different times in the simulation, 2) spatial patterns of regeneration in 2100 as a result of the operational fire exclusion zone scenario, and 3) supplemental analyses found in the appendixes.
Plant root simulator nutrient availability data in the black sand extended growing season experiment, 2018 - 2020.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites, each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows and a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot at each site by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to control plots after snow had naturally melted. We used open top warming chambers (OTCs) to increase summer temperature in three subplots within each of the 10 x 40 m plots. This dataset includes plant nutrient availability as measured using plant root simulator probes.
Labelled magnetic reconnection simulation data set
<p>Numerical simulations have been performed on Marconi at CINECA (Italy) under the ISCRA initiative. The corresponding data can be found at: <a href="https://doi.org/10.5281/zenodo.3935887">https://doi.org/10.5281/zenodo.3935887</a></p>
Compressible Hydrodynamics Simulation Data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This data is a 2D cross-section from a 3D compressible hydrodynamics simulation (Hyburn / AMRex code) of a rapid decompression / shock tube experiment at Special Technologies Laboratory. The simulated shot is a pure argon gas decompression from 1000Psi to atmosphere. </p> <p>This data is used in figures 3 and 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The data is saved as python numpy zipped archives numbered by the timestep in the simulation. Files starting with 'tube' contain data from inside the shock tube. Files starting with 'near_vent' contain data from the expansion chamber above the nozzle. All units are in SI.</p> <p>Each .npz file is an array file generated with python numpy.savez(). It can be opened with:</p> <p><em>import numpy as np</em></p> <p><em>data = np.load('<name>.npz')</em></p> <p>The data is an python dictionary. The dictionary keys can be displayed with:</p> <p><em>print(data.files)</em></p> <p>The numpy arrays can be accessed by keyname:</p> <p><em>print(data['keyname'])</em></p> <p>The key names correspond to physical quantities (density, temperature, etc.). All particle quantities are 0 as the simulation did not include particles.</p>
mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis
<p>All the data for 'mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis'</p> <p>sim.zip is stimulated data for intensity cutoff 0.05. simxcms.csv is peak intensity profiles for their simulated peaks.</p> <p>sim3.zip are simulated data for normal/leading/tailing peaks with tailing factor of 1, 0.8, and 1.5, respectively.</p> <p>All the csv files begin with sim3 are extracted peaks list from the sim3.zip with corresponding data analysis software.</p> <p>csv.zip recorded the m/z, retention time, intensity, and compounds name for simulated compound for each condition (sim.zip and sim3.zip).</p> <p>sep1.mzML: simulation for 8 isomers with similar m/z while different retention times. 7 peaks are non baseline separation peaks. Peaks profile is saved in spe1.csv file.</p> <p>xcms.csv, mzmine.csv, openms.csv: peaks found in sep1.mzML by xcms, mzmine 4.5 and openms, respectively.</p> <p>R code: <a href="https://github.com/yufree/democode/blob/master/meta/simfin.R">https://github.com/yufree/democode/blob/master/meta/simfin.R</a></p> <p>Website of mzrtsim package: https://yufree.github.io/mzrtsim/</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2016). For details see the README.md file.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 1: Land transport and shipping" (Righi et al., Atmos. Chem. Phys., 2015)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2015). For details see the README.md file.</p>
Numerical weather simulation using COSMOiso in June 2019 during L-WAIVE field campaign: selected model output and post-processed data.
<p>This dataset consists of extracts from a simulation with the isotope-enabled regional numerical weather prediction model COSMOiso, which covers the timespan of the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021).The simulation has a horizontal resolution of 0.1° (~10km) and 40 vertical levels.</p><p>This COSMOiso simulation is used in Thurnherr et al. (submitted) to compare stable water isotope measurements from various platforms. Here, we provide selected model outputs and post-processed data used in this comparison study. The post-processed data contain:</p><ol><li>COSMOiso output files for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Pressure weighted total and subcolumn averages for time steps 20190612_12, 20190613_12, 20190615_13, 20190616_13, 20190617_12, 20190622_12.</li><li>Vertical cross section of selected variables at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated time series of subcolumn and total column averages at Annecy, the location of the L-WAIVE field campaign, for the simulation time window.</li><li>Interpolated variables along the flight tracks from the L-WAIVE campaign (see Sodemann and Seidl, 2023).</li></ol><p>See also README files for more details on the provided data.</p><p>To access further model output and post-processed data, please contact the dataset authors.</p>
Programmable multi-photon quantum interference in a single spatial mode -- Data and code for simulations
<h2>Description of the data and file structure</h2> <p>This Dataset contains data files with experimental results for the manuscript "<strong>Programmable multi-photon quantum interference in a single spatial mode</strong>" (pre-print version at <a href="https://arxiv.org/abs/2305.11157">https://arxiv.org/abs/2305.11157</a>).</p> <p>The CSV files contain the measured output distributions of our time-bin interferometer, for the various experiments we run. In the first column is the number of counts detected and in the following columns the corresponding output modes. The counts were detected by post-processing the time-tags of the recorded single photon events (a detailed explanation can be found in the Supplementary Informations of the paper).The number of counts is reported for all possible combinations of output modes in order to reconstruct the entire output distribution of collisionless events.</p> <p>The text file contains the data points of the time-bin HOM histogram shown in the paper.</p> <p> </p> <h2>Code/Software</h2> <p>We also provide the Jupyter Notebook (LoopExperiment.ipynb) we used to simulate the experiments, developed by Dr. Tobias Guggemos.</p> <p>The Loop-based architecture is a photonic experiment, that allows scalable implementation of Boson Sampling and arbitrary unitaries on a photonic platform. It can be implemented as a single, sequenced or nested architecture.</p> <p>We use the python framework Perceval to simulate our experiments. We simulate the conversion of the time-bin encoded setup as path encoded photonic qubits.</p> <p>More details can be found in the Notebook.</p>
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Two-bubble simulation and gravitational wave spectrum codes and data
<p><span>Code and data used in the paper with title</span><a href="https://doi.org/10.1103/PhysRevD.104.075039"><span> <em>Vacuum bubble collisions: from microphysics to gravitational waves </em>by Oliver Gould, Satumaaria Sukuvaara, and David Weir</span></a><span> [</span><a href="https://arxiv.org/abs/2107.05657"><span>arXiv:2107.05657</span></a><span>]. </span></p> <p><span>The field simulation and gravitational wave spectrum calculation codes are based on Gravitational radiation from colliding vacuum bubbles by Arthur Kosowsky, Michael S. Turner and Richard Watkins [</span><a href="https://inspirehep.net/literature/324187"><span>Inspire</span></a><span>].</span></p> <p><span>Contains files:</span></p> <ul> <li> <p><span>two_bubbles_code-v1.0.1.zip is a snapshot of a</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_code/"><span> git repository</span></a><span>, corresponding to</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_code/-/tree/v1.0.1?ref_type=tags"><span> commit v1.0.1</span></a><span>. Contains the codes with which the majority of the data was produced.</span><span><br><br></span></p> </li> <li> <p><span>two_bubbles_data-v1.0.1.zip is a snapshot of a</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_data/"><span> git repository</span></a><span>, corresponding to</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_data/-/tree/v1.0.1?ref_type=tags"><span> commit v1.0.1</span></a><span>. It contains the majority of data used in the paper. Note however that the simulation pickle files are examples run on a coarser lattice due to Zenodo file size restrictions. Apart from few exceptions, the data in this file was produced by the codes in two_bubbles_code-v1.0.1.zip.</span><span><br><br></span></p> </li> </ul> <p><span>README.md files, specifying and explaining the contents and usage, are included within. The v1.0.1 of</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_code/-/blob/v1.0.1/README.md?ref_type=tags"><span> </span><span>code README.md</span></a><span> and the</span><a href="https://version.helsinki.fi/two_bubbles/two_bubbles_data/-/blob/v1.0.1/README.md?ref_type=tags"><span> </span><span>data README.md</span></a><span> can be found from the repositories as well.</span></p> <p><span>The update v1.0.1 updates the README and fixes a small error in the calculation of the gravitational wave spectrum. We thank Toby Opferkuch for pointing this out. The error in the code does not affect the results in two_bubbles_data-v1.0.0.zip or the paper as they were produced with a slightly earlier version of the code, before the appearance of this error. The version two_bubbles_data-v1.0.1 updates the README, clarifying some points.</span></p>
Data Files for Climate-based Maize Loss Rate Simulations
<p>This archive contains data files from an <a href="../records/13356711">open source pipeline</a> looking at how crop insurance rates may change in the future within the US Corn Belt using <a href="https://www.sciencedirect.com/science/article/pii/S0034425715001637">SCYM</a> and <a href="https://www.chc.ucsb.edu/data/chc-cmip6">CHC-CMIP6</a>. These are available under a Creative Commons license. Unless otherwise specified, these report on SSP245.</p> <p>See README for more details including column-level description of each resource. Funded by the <a href="https://dse.berkeley.edu/">Eric and Wendy Schmidt Center for Data Science and Environment</a> at the University of California, Berkeley.</p>
Mixed DG-FEM for the Darcy-Brinkman-Stokes model: supplementary simulation data
<p>This dataset contains simulation results used in the publication<em> "Stable across regimes: A mixed DG method for Darcy-Brinkman-Stokes type flows"</em>. Detailed descriptions of the individual cases can be found in the paper.</p> <p>The simulation outputs are enriched with the respective inputs used to set up the finite element simulations. Setups include definition of the mesh (sizes), material and numerical parameters. Setups are given as Python for scripted inputs (e.g. function definitions) and human-readable <em>.yaml</em> files for simple parameters. <br><br>Simulation outputs are written in paraview .vtk and .vtu files, which are contained in the <em>outputs/MODEL_NAME/paraview</em> folder of the respective simulation. <em>MODEL_NAME</em> corresponds to the model. See also the <em>readme.md.</em></p> <p>The additional folder <em>figure_collection</em> contains the raw result plots from the publication, along with the respective simulation inputs used to obtain the figure.</p>
Model simulation data used in "Exploring the uncertainties in the aviation soot-cirrus effect" (Righi et al., Atmos. Chem. Phys., 2021)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2021). For details see the README.md file and Table 1 in the paper.</p>
An experimental data set on the thermal and fluid dynamic performance of double skin facades (DSFs) subjected to various controlled boundary conditions through the use of a climate simulator facility
<p>Double skin facades (DSFs) are building envelope systems defined by complex phenomena and non-linear-processes that make characterizing their performance a non-trivial task. In an effort to enable the scientific community to access experimental data for further analysis or model validation purposes, we release together with the open-access paper entitled “<strong><em>Laboratory testbed and methods for flexible characterization of the thermal and fluid dynamic behavior of double skin facades” (</em></strong><a href="https://doi.org/10.1016/j.buildenv.2021.108700"><strong><em>https://doi.org/10.1016/j.buildenv.2021.108700</em></strong></a><strong><em>)</em></strong>, a set of experimental data collected during tests carried out with the use of the newly developed testbed. The data contains the results of a series of tests where various configurations of a full-scale DSF mock-up that have been subjected to different boundary conditions replicated in a climate simulator. The database contains a guide in the form of the file ‘Guide.pdf’, which explains how to read data, presents a schematic drawing of sensor layout, and provides more information on sensors’ positions. Further information on the original aims of the experiments, methods, and other data can be found in the article mentioned above, which becomes an essential tool to understand how to read and interpret the experimental data fully. The following collection of experimental data are provided:</p> <ul> <li>32 steady-state measurements where the following factors were changed: ventilation mode (indoor and outdoor air curtain), solar irradiance (0, 400, 600, and 800 Wm<sup>-2</sup>), outdoor chamber temperature (10, 20, 30, and 40 ℃), cavity depth (20, 30, 40 and 60 cm) and venetian blinds position (no blinds, closed blinds, θ=45 º, and open blinds) [file names: ‘Taguchi_4Lx4F_L16_I-I.csv’ and ‘Taguchi 4Lx4F_L16_O-O.csv’],</li> <li>Dynamic profile measurements corresponding to a typical hot summer day [Dynamic_profile_measurements.csv] and</li> <li>Calibration data [Callibration.csv].</li> </ul> <p>Any inquires on the experimental data<em> can be sent </em>to: aleksandar.jankovic@ntnu.no</p>
Supplementary data: Accurate large-scale simulations of siliceous zeolites by neural network potentials
<p><strong>Content</strong></p> <p><em>1. Zeolite databases</em></p> <ul> <li>Deem database containing 331170 hypothetical zeolite frameworks [Deem09, Pophale11] geometrically optimized at the NNPscan level (note, the first row of the database is alpha-quartz): "DEEM_NNPscan.db"</li> <li>Database of 236 exiting zeolite frameworks of the <a href="http://www.iza-structure.org/databases/">International Zeolite Association (IZA) </a>optimized at the NNPscan level: "IZA_NNPscan.db"</li> <li>Both databases are <a href="https://wiki.fysik.dtu.dk/ase/ase/db/db.html">ASE SQLite database files</a> of the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment</a> containing the ASE <a href="https://wiki.fysik.dtu.dk/ase/ase/atoms.html">Atoms objects</a> with energies and forces (NNPscan level); readable with ASE's <a href="https://wiki.fysik.dtu.dk/ase/ase/io/io.html">I/O module</a></li> <li>Additionally, relevant quantities can be extracted with, e.g., the following queries (further information: ase db --help):</li> </ul> <pre><code class="language-bash">ase db DEEM_NNPscan.db -c id,formula,natoms,volume,mass,density,energy_per_tsite,n_tsites,relative_energy # Output id|formula|natoms| volume| mass|density|energy_per_tsite|n_tsites|relative_energy 1|O6Si3 | 9|111.161|180.249| 26.988| -31.796| 3| 0.000 2|O16Si8 | 24|433.858|480.664| 18.439| -31.638| 8| 15.265 3|O16Si8 | 24|421.114|480.664| 18.997| -31.596| 8| 19.359 4|O16Si8 | 24|426.557|480.664| 18.755| -31.614| 8| 17.613 5|O16Si8 | 24|412.410|480.664| 19.398| -31.613| 8| 17.677 6|O16Si8 | 24|393.544|480.664| 20.328| -31.594| 8| 19.546 7|O16Si8 | 24|422.400|480.664| 18.939| -31.657| 8| 13.476 8|O16Si8 | 24|394.405|480.664| 20.284| -31.581| 8| 20.797 9|O12Si6 | 18|265.201|360.498| 22.624| -31.611| 6| 17.868 10|O16Si8 | 24|357.047|480.664| 22.406| -31.581| 8| 20.785 11|O16Si8 | 24|434.894|480.664| 18.395| -31.621| 8| 16.911 12|O16Si8 | 24|384.158|480.664| 20.825| -31.657| 8| 13.448 13|O12Si6 | 18|258.977|360.498| 23.168| -31.679| 6| 11.278 14|O16Si8 | 24|466.429|480.664| 17.152| -31.593| 8| 19.588 15|O16Si8 | 24|423.469|480.664| 18.892| -31.639| 8| 15.179 16|O16Si8 | 24|450.716|480.664| 17.750| -31.628| 8| 16.219 17|O16Si8 | 24|331.528|480.664| 24.131| -31.642| 8| 14.857 18|O16Si8 | 24|458.573|480.664| 17.445| -31.635| 8| 15.572 19|O16Si8 | 24|359.298|480.664| 22.266| -31.655| 8| 13.636 20|O16Si8 | 24|464.264|480.664| 17.232| -31.612| 8| 17.750 Rows: 331171 (showing first 20) Keys: density, energy_per_tsite, n_tsites, relative_energy ase db IZA_NNPscan.db -c id,formula,natoms,volume,mass,density,energy_per_tsite,n_tsites,relative_energy,iza_code # Output id|formula |natoms| volume| mass|density|energy_per_tsite|n_tsites|relative_energy|iza_code 1|O16Si8 | 24| 435.488| 480.664| 18.370| -31.676| 8| 11.594|ABW 2|O32Si16 | 48| 961.419| 961.328| 16.642| -31.645| 16| 14.612|ACO 3|O96Si48 | 144|3154.579|2883.984| 15.216| -31.664| 48| 12.810|AEI 4|O80Si40 | 120|2102.921|2403.320| 19.021| -31.703| 40| 9.021|AEL 5|O96Si48 | 144|2417.286|2883.984| 19.857| -31.666| 48| 12.586|AEN 6|O144Si72| 216|4075.300|4325.976| 17.667| -31.674| 72| 11.831|AET 7|O96Si48 | 144|2786.810|2883.984| 17.224| -31.675| 48| 11.716|AFG 8|O48Si24 | 72|1400.247|1441.992| 17.140| -31.690| 24| 10.268|AFI 9|O64Si32 | 96|1764.823|1922.656| 18.132| -31.653| 32| 13.809|AFN 10|O80Si40 | 120|2080.330|2403.320| 19.228| -31.707| 40| 8.632|AFO 11|O64Si32 | 96|2097.384|1922.656| 15.257| -31.655| 32| 13.622|AFR 12|O112Si56| 168|3820.116|3364.648| 14.659| -31.650| 56| 14.150|AFS 13|O144Si72| 216|4732.720|4325.976| 15.213| -31.664| 72| 12.793|AFT 14|O60Si30 | 90|1897.074|1802.490| 15.814| -31.659| 30| 13.268|AFV 15|O96Si48 | 144|3154.885|2883.984| 15.214| -31.664| 48| 12.776|AFX 16|O32Si16 | 48|1137.335| 961.328| 14.068| -31.591| 16| 19.790|AFY 17|O48Si24 | 72|1283.812|1441.992| 18.694| -31.620| 24| 17.034|AHT 18|O96Si48 | 144|2479.287|2883.984| 19.360| -31.681| 48| 11.155|ANA 19|O64Si32 | 96|1797.086|1922.656| 17.807| -31.662| 32| 12.924|APC 20|O64Si32 | 96|1751.393|1922.656| 18.271| -31.678| 32| 11.422|APD Rows: 236 (showing first 20) Keys: density, energy_per_tsite, iza_code, n_tsites, relative_energy # Filtering of the database, e.g., for structures with relative energies < 10 kJ/(mol Si) ase db IZA_NNPscan.db relative_energy\<10 -c density,energy_per_tsite,n_tsites,relative_energy,iza_code # Output density|energy_per_tsite|n_tsites|relative_energy|iza_code 19.021| -31.703| 40| 9.021|AEL 19.228| -31.707| 40| 8.632|AFO 19.385| -31.695| 24| 9.802|ATV 18.778| -31.702| 34| 9.061|DOH 19.570| -31.693| 24| 9.959|EWO 18.401| -31.698| 32| 9.451|GON 18.551| -31.695| 112| 9.807|IHW 17.778| -31.693| 288| 9.972|IMF 19.154| -31.695| 6| 9.762|JBW 18.187| -31.695| 96| 9.734|MFI 19.278| -31.709| 48| 8.443|MRE 18.035| -31.698| 90| 9.481|MSO 20.417| -31.724| 44| 7.003|MTF 19.227| -31.704| 136| 8.898|MTN 18.542| -31.693| 28| 9.966|MTW 19.137| -31.695| 60| 9.798|PCR 20.037| -31.709| 144| 8.464|PSI 18.843| -31.703| 64| 9.004|SAF 18.371| -31.703| 112| 8.975|STO 19.894| -31.706| 17| 8.671|VET Rows: 20 (showing first 20) Keys: density, energy_per_tsite, iza_code, n_tsites, relative_energy</code></pre> <ul> <li>The quantities shown above are available with the keys (besides standard ASE database keys):</li> </ul> <table> <thead> <tr> <th scope="col">Key</th> <th scope="col">Quantity</th> <th scope="col">Unit</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>Identifier</td> <td> </td> </tr> <tr> <td>formula</td> <td>Chemical formula of the unit cell</td> <td> </td> </tr> <tr> <td>natoms</td> <td>Number of atoms</td> <td> </td> </tr> <tr> <td>volume</td> <td>Unti cell volume</td> <td>Å<sup>3</sup></td> </tr> <tr> <td>mass</td> <td>Atomic mass of the unit cell</td> <td>amu</td> </tr> <tr> <td>density</td> <td>Framework density</td> <td>Si/nm<sup>3</sup></td> </tr> <tr> <td>energy_per_tsite</td> <td>NNPscan energy</td> <td>eV</td> </tr> <tr> <td>n_tsites</td> <td>Number of T-sites</td> <td> </td> </tr> <tr> <td>relative_energy</td> <td>Energy with respect to quartz</td> <td>kJ/(mol Si)</td> </tr> <tr> <td>iza_code</td> <td>only for 'IZA_NNPscan.db'</td> <td> </td> </tr> </tbody> </table> <ul> <li> Comma separated csv files for the quantities listed above: "DEEM_NNPscan.csv" and "IZA_NNPscan.csv"</li> </ul> <p><em>2. Neural network potentials (NNP) for silica</em></p> <ul> <li>SchNet [Schütt18,Schütt19] NNP files trained on DFT data at the PBE+D3 (NNPpbe) and SCAN+D3 level (NNPscan)</li> <li>Simulations can be performed using <a href="https://schnetpack.readthedocs.io/en/stable/getstarted/getstarted.html#references">SchNetPack</a> with its ASE calculator</li> <li>This example shows a simple single-point calculation</li> </ul> <pre><code class="language-python">import ase.io import torch from schnetpack.interfaces import SpkCalculator from schnetpack.environment import AseEnvironmentProvider # check if GPU(s) are available if torch.cuda.is_available(): device = "cuda" else: device = "cpu" # load the NNP model model = torch.load('SiOscan1', map_location=device) # read some structure atoms = ase.io.read( ... ) # define SchNetPack calculator calc = SpkCalculator(model=model, device=device, energy='energy', forces='forces', environment_provider=AseEnvironmentProvider(6.) ) # attach calculator to atoms object atoms.set_calculator(calc) # perform simulations, e.g., single-point calculation energy = atoms.get_potential_energy() print(energy)</code></pre> <p><em>3. Test set used for accuracy evaluation (ASE database: test_set_NNPscan.db)</em></p>
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