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73 results for “zeolites”
Supporting datasets for "Dynamical Equilibrium between Brønsted and Lewis Sites in Zeolites: Framework-Associated Octahedral Aluminum"
<p>Tar of directory tree containing input files for VASP and CASTEP calculations of local geometries and NMR parameters (both static and dynamic) for systems described in the manuscript, including CHA and MOR in H form, various Si/Al ratios and water loadings.</p>
Unit-cell-thick zeolitic imidazolate framework films for membrane application
<p>raw data for the manuscript with title of Unit-cell-thick zeolitic imidazolate framework films for membrane application</p>
Raw Data for: "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances"
<p>This repository contains all the raw data to reproduce the manuscript:</p> <p>D. Schwalbe-Koda et al. "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". arXiv:2307.10935 (2023)</p> <p>The raw data should be used in combination with the code hosted on GitHub: <a href="https://github.com/dskoda/Zeolites-AMD">https://github.com/dskoda/Zeolites-AMD</a>.</p> <p><strong>Description of the data</strong></p> <p>The data in this link contains all necessary information to reproduce the manuscript. In combination with the code hosted on GitHub, it can be visualized and analyzed accordingly. The full description on the columns and results is available on the GitHub code.<br> The data files in this repository are:</p> <p>- `hparams_rnd_*.json`: results of the hyperparameter optimization of all classifiers studied in this work. The data was produced by randomly sampling the train-validation-test sets. In some cases, the data was normalized (`_norm_`), and the train set was kept `balanced` or `unbalanced`.<br> - `hyp_dm`: distance matrix of all hypothetical zeolites towards the known zeolites<br> - `hyp_predictions`: predictions of the synthesis conditions for all hypothetical zeolites<br> - `xgb_ensembles*`: pickle files containing the serialized ensemble models used in the evaluation of the data in this work. The models can be loaded with the `xgboost` Python package.</p> <p><strong>License</strong></p> <p>The data and all the content from this repository is distributed under the Creative Commons Attribution 4.0 (CC-BY 4.0)</p> <p>This work was produced under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.</p> <p>Dataset released as: LLNL-MI-854709.</p>
Supporting data for "Machine-Learnt Interatomic Potentials for Amorphous Zeolitic Imidazolate Frameworks"
<p>Trajectories from several ab initio molecular dynamics simulations of ZIF-4, produced by CP2K. They consist of NVT simulations of 60 to 80 ps, performed at various temperatures and volumes.</p><p>Volume deformations (from the reference crystal volume) are denoted as volume difference in % compared to the initial volume (e.g. volume_deformation2 has a volume (1 + 0.02) times larger than the initial volume).</p>
Selectivity improvement of polysulfone-zeolite templated carbon membrane by annealing and coating treatment for CO2/CH4 and H2/CH4 separation
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Effect of temperature in methanol conversion to dimethyl ether (DME) and light hydrocarbons with beta zeolite
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Non-crystalline Zeolitic Imidazolate Frameworks Tethered with Ionic Liquids as Catalysts for CO2 Conversion into Cyclic Carbonates
<p>This folder /final_logs/ contains the DFT-optimized geometries (in .xyz format together with the gas-phase energy, E) accompanying the paper</p> <p>"Design of Non-crystalline Zeolitic Imidazolate Frameworks Tethered with Ionic Liquids as Highly Active and Stable Catalysts for Mild CO2 Conversion into Cyclic Carbonates"</p> <p>where conformers occur, they are always named from the lowest Gibbs energy to the highest in ascending order from c1 (sometimes omitted), c2, c3, ...</p> <p> </p>
Supplementary data (CC BY-NC-SA 4.0): Migration of Zeolite-Encapsulated Subnanometre Platinum Clusters via Reactive Neural Network Potentials
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p> <ul> <li>Trajectory files containing structures, energies and forces of CHA, MWW (including MWW*), TON, MFI (Pt1, Pt3, Pt5 at 750, 1000, 1250 K) as (extended) xyz files readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE)</li> <li>Animated gif files of Pt1 migration between double-six rings in CHA, Pt3 jump through an eight-ring in CHA, and insertion of Pt1 into a t-pen unit in MFI</li> <li>Neural Network Potential (NNP) files readable by <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li> </ul>
Implementing Mesoporosity in Zeolitic Imidazolate Frameworks through Clip-Off Chemistry in Heterometallic Iron–Zinc ZIF-8
<p>Relevant data for publication with DOI: <a title="DOI URL" href="https://doi.org/10.1021/jacs.3c08017">10.1021/jacs.3c08017</a></p>
Multivariate sodalite zeolitic imidazolate frameworks: a direct solvent-free synthesis
<p>Relevant data for publication with DOI: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D1SC04779E">10.1039/D1SC04779E</a></p>
Supporting data for "Neural Network-Based Interatomic Potential for the Study of Thermal and Mechanical Properties of Siliceous Zeolites"
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Supplementary data (CC BY-NC-SA 4.0): Germanium Distributions in Zeolites Derived from Neural Network Potentials
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p> <p>This dataset contains supplementary data related to the Germanosilicate Project titled <br>"Germanium Distributions in Zeolites Derived from Neural Network Potentials". <br>In various subfolders, it hosts database, simulations and post-processing calculations.<br><br>Below is a brief overview of each subfolder:</p> <ol> <li><em>Post_Processing_Calculation:</em> <br>- Contains scripts and data for post-processing calculations such as coordination numbers, <br>- Pair distribution function, and various germanium distribution metrics.<br><br></li> <li><em>NNP_Simulation_DATA:</em> <br>- Stores simulation data for different zeolite structures along with setup files for neural network potentials (NNP) simulations.<br><br></li> <li><em>NNP_files_database</em>: <br>- NNP_files: NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)<br>- GeSiO_training.db: DFT (PBE+D3(BJ)) training database as SchNetPack1.0 database file readable by <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment (ASE)</a> and SchNetPack version 1.0<br><br><em> </em></li> <li><em>DFT_vs_NNP_Data:</em> <br>- ASE traj files storing structures subsampled from MCBH runs along with energies/forces at the PBE+D3(BJ) ("*_dft.traj") and NNP level (*_nnp.traj)<br><br></li> <li><em>Zeolite_Structurers_ALL</em>: <br>- Holds data for various zeolite structures, including optimized structures for both single-cell and supercell configurations.<br><br></li> <li>GSOs_DATA:<br>- The unoptimised Global Structure Optimas (GSOs) are provided<br>- Optimised GSOs are stored inside folders for both various DFT and NNP methods</li> </ol> <p> <br>Please refer to individual readme files in each subfolder for more detailed information.</p>
Structure Databases: Predicted Influence of Organic Structure Directing Agents on Al Distributions in CHA Zeolites
<p>Structures and energies associated with the paper: Predicted Influence of Organic Structure Directing Agents on Al Distributions in CHA Zeolites</p>
Raw spectroscopic data for "Cage effects control the mechanism of methane hydroxylation in zeolites"
<p>This Excel spreadsheet contains all Mössbauer and resonance Raman data presented in the main text of Snyder et al., Science 2021.</p>
Supporting datasets for "The need for Operando Modelling of 27Al NMR in Zeolites"
<p>tar of directory tree containing input files for VASP and CASTEP calculations of local geometries and NMR parameters (both static and dynamic) for systems described in the manuscript, including MOR and CHA in H and Na form and various water loadings.</p>
Structure Databases: Analysis and Augmentation of Guest-Host Interaction Energy Models as CHA and AEI Zeolite Crystallization Phase Predictors
<p>Structures and energies associated with the paper: Analysis and Augmentation of Guest-Host Interaction Energy Models as CHA and AEI Zeolite Crystallization Phase Predictors</p>
PMA-Zeolite-Clinoptilolite Effects in Crohn Disease
ClinicalTrials.gov study NCT04370535. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: Halloysite nanotube-based electrospun ceramic nanofibre mat: a novel support for zeolite membranes
Some key parameters of supports such as porosity, pore shape and size are of great importance for fabrication and performance of zeolite membranes. In this study, we fabricated millimetre-thick, self-standing electrospun ceramic nanofibre mats and employed them as a novel support for zeolite membranes. The nanofibre mats were prepared by electrospinning a halloysite nanotubes/polyvinyl pyrrolidone composite followed by a programmed sintering process. The interwoven nanofibre mats possess up to 80% porosity, narrow pore size distribution, low pore tortuosity and highly interconnected pore structure. Compared with the commercial α-Al2O3 supports prepared by powder compaction and sintering, the halloysite nanotube-based mats (HNMs) show higher flux, better adsorption of zeolite seeds, adhesion of zeolite membranes and lower Al leaching. Four types of zeolite membranes supported on HNMs have been successfully synthesized with either in situ crystallization or a secondary growth method, demonstrating good universality of HNMs for supporting zeolite membranes.
Implementing Mesoporosity in Zeolitic Imidazolate Frameworks through Clip-Off Chemistry in Heterometallic Iron-Zinc ZIF-8
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Multivariate sodalite zeolitic imidazolate frameworks: a direct solvent-free synthesis
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.