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59 results for “Metal-organic frameworks”
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset
<p>High-throughput computational screening of metal-organic frameworks rely on the availability of<strong><em> </em></strong>atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web.</p> <p><strong>Nomenclatures:</strong></p> <p>LCD (Largest Cavity Diameter), PLD (pore limiting diameter), LFPD (Largest Sphere along the Free Path), ASA (Accessible Surface Area), NASA (Non-accessible surface area), AV_VF (Void Fraction, 0 - 1), NAV (Non Accessible Volume)</p> <p><strong>Dataset Directory Organization</strong></p> <p>CoREMOF2019_public_v2.zip: dataset with CR and NCR classifications</p> <p>1. CR dataset: computaion-ready (<em>N</em> = 10,367)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 6,603)</li> <li> FSR: free solvent removed (<em>N</em> = 3,764)</li> </ul> <p>2. NCR: not computaion-ready (<em>N</em> = 8,714)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 5,417) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 2,597)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 958)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,859)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> <li> FSR: free solvent removed (<em>N</em> = 3,297) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 1,646)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 463)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,185)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> </ul> <p>2. NCR_detail.xlsx: details of all structures by mofchecker and Chen_Manz for each NCR cases</p> <p><strong>November, 24 2024</strong></p> <ul> <li>Re-ordering of folders such that top level directory is based on computation-ready and not-computation ready classification.</li> </ul> <p><strong>November, 13 2024</strong></p> <ul> <li>Classification of Computation-Ready (CR) and Not Computation-Ready (NCR) Structures based on <a href="https://pubs.rsc.org/en/content/articlelanding/2020/ra/d0ra02498h">Chen & Manz</a> (RSC Adv., 2020,10, <a>26944-26951</a>) and <a href="https://github.com/kjappelbaum/mofchecker">MOFChecker </a>program by <a href="https://github.com/kjappelbaum">Kevin M. Jablonka</a>)</li> <li>ML-predicted DDEC6 partial atomic charges based on <a href="https://github.com/mtap-research/PACMAN-charge">PACMAN</a></li> </ul> <p><strong>Acknowledgements</strong></p> <ul> <li>This reserach is supported by the National Research Foundation of Korea (No. 2016R1D1A1B3934484, NRF-2020R1C1C1010373, RS-2024-00449431)</li> <li>This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment)</li> </ul>
Toward a Generalizable Machine-Learned Potential for Metal-Organic Frameworks
<ul> <li>This repository contains the dataset used in the publication<br> `Toward Generalizable Machine Learned Potential for Metal-Organic Frameworks` Yue Yifei, Saad Aldin Mohammed, Loh Duane*, Jiang Jianwen*<br> <br> Please each the README.md within each subfolder. For brevity, the data is organized into three sections<br> <br> 1. The dataset in DATASET<br> - The training and testing dataset, including structures of MOFs in extxyz format<br> <br> 2. The training output files and logs in NEQUIP-TRAIN<br> - The conda environment details, training scripts and logs<br> - Also Training and testing metrics in csv files<br> - This is split into two zip files NEQUIP-TRAIN1 and NEQUIP-TRAIN2 due to their size<br> <br> 3. Examples of using the developed models in MD simulations<br> - Including LAMMPS scripts, data file and environment details used in our scalability tests<br> - The complied Nequip-patched LAMMPS version is also provided<br> - Details on how to use our models - we used a default model that is slower but more accurate in our study but faster models are also developed.</li> </ul>
Dataset for 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination'
<p>Associated data for the manuscript 'Room-temperature monitoring of CH4 and CO2 using a metal-organic framework-based QCM sensor showing inherent analyte discrimination' (doi://10.26434/chemrxiv-2023-djhp2)</p> <p> </p> <p> </p>
DFT-optimized Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014
<p>There are two folders inside the zipped file:</p> <p>- 838 structures (without DDEC partial atomic charges)</p> <p>- 502 structures (with DDEC partial atomic charges)<br> </p> <p> </p>
A Ligand Field Molecular Mechanics Study of CO2 Induced Breathing in the metal-organic framework DUT-8(Ni)
<p>Raw Data, scripts and processed data for the publication "A Ligand Field Molecular Mechanics Study of CO2 Induced Breathing in metal-organic framework DUT-8(Ni)"</p>
Data: Homochiral metal-organic frameworks coated double-plasmon active optical fiber for in-situ enantioselective detection
<p>This dataset is focused on utilization of optical fiber with double-plasmon activity (ensured by a spatially separated gold and silver nanocoating of the fiber core) and subsequent surface grafting by HMOFs for enantioselective capture of organic enantiomers.</p>
Supplementary data for "Heterometallic perovskite-type metal-organic framework with an ammonium cation: structure, phonons, and optical response"
<p>Optimised structures of [NH<sub>4</sub>][Na<sub>0.5</sub>M<sub>0.5</sub>(COOH)<sub>3</sub>] (M = Al, Cr)</p> <p>Phonon output for [NH<sub>4</sub>][Na<sub>0.5</sub>Cr<sub>0.5</sub>(COOH)<sub>3</sub>]</p> <p>Gif of the T’(NH<sub>4</sub><sup>+</sup>) mode (no. 23). The c-axis is the vertical direction.</p> <p>For further information please see the associated publication.</p>
Supplementary files for "Pressure-enhanced ferroelectric polarisation in polar perovskite-like [C2H5NH3]Na0.5Cr0.5(HCOO)3 metal-organic framework"
<p>DFT optimised structures for the paper: Pressure-enhanced ferroelectric polarisation in polar perovskite-like [C2H5NH3]Na0.5Cr0.5(HCOO)3 metal-organic framework. See the paper for additional information on the computational setup.</p> <p>Files are named HP or LP for high-pressure and low-pressure phases, followed by the pressure as calculated by DFT. The HP structure was optimized in two different space groups and are labelled accordingly. The DFT optimized structure without volume restrictions is found in opt-vol.POSCAR</p> <p> </p>
Metal-organic frameworks as regeneration optimized sorbents for atmospheric water harvesting
<p>Dataset for 'Metal-organic frameworks as regeneration optimized sorbents for atmospheric water harvesting' article published at <em>Cell Reports Physical Science, </em><a href="https://doi.org/10.1016/j.xcrp.2023.101252">https://doi.org/10.1016/j.xcrp.2023.101252</a>.</p> <p>Code used for data analysis, visualization and kinetics modelling can be found at <a href="https://github.com/AndreyBezrukov/Water_Sorption_Kinetics">AndreyBezrukov/Water_Sorption_Kinetics (github.com)</a></p>
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
<p>The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.</p>
London Dispersion Governs the Interaction Mechanism of Small Polar and Non-Polar Molecules in Metal-Organic Frameworks
<p>Raw data set relating to publication.</p>
Characterization data for the manuscript "A data-driven perspective on the colours of metal-organic frameworks"
<p>Visualize the data in this dataset: <a href="https://www.c6h6.org/zenodo/record/4044212">open entry</a>. </p>
Supplementary information for "Anharmonic origin of large thermal displacements in the metal-organic framework UiO-67"
<p>Supplementary information for DOI: 10.1021/acs.jpcc.7b04757</p> <p>POSCAR-XXX: DFT optimised structures</p> <p>Phonons-XXX.zip: Folders containing the force constants (FORCE_SETS), the resulting phonon frequencies (mesh.yaml), phonon partial density of states (partial_dos.dat), animations of all phonon modes (anime.ascii) e.g. to be visualized in VMD and gifs of selected phonon modes. </p> <p>XDATCAR-XXX: MD trajectories</p>
Computational results for the publication "Evolution of water structures in metal-organic frameworks for improved atmospheric water harvesting"
<p>This upload contains the computationally obtained atomic coordinates for MOF-303 and MOF-333 at different water loadings.</p>
Performance of GFN1-xTB for periodic optimization of Metal-Organic Frameworks
<p>GFN-xTB optimised structures of CoRE 2014 and CoRE 2019 structures, with and without lattice optimisation.</p>
Supplementary data for "Vacancy defect configurations in the metal-organic framework UiO-66: Energetics and electronic structure"
<p>Optimised structures for UiO-66 with various defect configurations in VASP POSCAR format. For naming see the associated paper (DOI:10.1039/c7ta11155j).</p>
Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs
<p>This dataset is associated with the study <em>"Predicting Partial Atomic Charges in Metal-Organic Frameworks: An Extension to Ionic MOFs."</em> Detailed instructions for installation, usage, and example scripts for the PACMOF2 models can be found on our GitHub <a href="https://github.com/snurr-group/pacmof2">repository</a>.</p> <div> <div> <div> <div> <p>The dataset includes the following files:</p> <ul> <li><strong>DDEC6_data.zip</strong>: Contains the crystal structures of MOFs with DDEC6 partial charges.</li> <li><strong>PACMOF2_prediction_cifs.zip</strong>: Contains the crystal structures of MOFs with DDEC6 charges as predicted by the PACMOF2 models.</li> <li><strong>PACMOF2_ionic.gz</strong>: A machine learning model designed to predict charges in ionic MOFs with a non-zero formal charge.</li> <li><strong>PACMOF2_neutral.gz</strong>: A machine learning model designed to predict charges in neutral MOFs.</li> </ul> <p>For more details about each file, please refer to the accompanying <code>README.md</code> file.<br><br><br>Updates: <br>- September 2024 (version 1.0.1): Added README.md file.</p> </div> </div> </div> </div>
Characterization Data for the Manuscript: "Unraveling Metal Effects on CO2 Uptake in Pyrene-based Metal-Organic Frameworks through Integrated Lab and Computer Experiments"
<p>This entry contains characterization data for the manuscript "Unraveling Metal Effects on CO2 Uptake in Pyrene-based Metal-Organic Frameworks through Integrated Lab and Computer Experiments".</p>
Dataset for "Single-Step Selective Oxidation of Methane by Iron-Oxo Species in the Metal-Organic Framework MFU-4l"
<p>Dataset belonging to publication "Single-Step Selective Oxidation of Methane by Iron-Oxo Species in the Metal-Organic Framework MFU-4l"</p>
Dataset for "An air-stable Cu(I) metal-organic framework for hydrogen storage"
<p>Dataset for "An air-stable Cu(I) metal-organic framework for hydrogen storage"</p> <p>Raw data set of the electronic structure calculations and inputs for the GCMC calculations relating to above publication.</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.