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59 results for “Metal-Organic Framework”
Implementation of Genetic Algorithms to Optimize Metal-Organic Frameworks for CO2 Capture
<p>Dataset associated with the publication "Implementation of Genetic Algorithms to Optimize Metal-Organic Frameworks for CO2 Capture".</p> <p> </p> <p>Changelog:</p> <p>- Include sample input files for GCMC using RASPA2 and geometry optimization using LAMMPS.</p>
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks
<p>Solvent removal stability and thermal stability associated with structurally characterized metal organic frameworks.</p>
Isotope-selective pore-opening in a flexible metal-organic framework
<p>This dataset contains raw data underlying the results related to the paper "Isotope-selective pore-opening in a flexible metal-organic framework".</p> <p> </p>
Machine Learning Potentials for Metal-Organic Frameworks with Thermodynamic Transferability: training data
<p>This dataset contains potential energies, forces, and virial stress for a large set of reference configurations for UiO-66(Zr) and MIL-53(Al), computed at the PBE-D3 level using CP2K 7.1. The basis set contained both TZVP Gaussian basis functions as well as plane waves (cutoff energy 800 Ry for UiO-66(Zr) and 900 Ry for MIL-53(Al)). The sampling of the Brillouin zone was restricted to the gamma point.</p>
Facile synthesis of magnesium-based metal-organic framework with tailored nanostructure for effective VOCs adsorption
<p class="16">A novel Mg(II) metal–organic framework (Mg-MOF) was synthesised based on the ligand of 2,2'-bipyridine-4,4'dicarboxylic acid (Bpdc). Single-crystal X-ray structural analysis confirmed that 3D-nanostructure Mg-MOFs formed a monoclinic system with a channel size of 15.733 Å × 23.736 Å. The N<sub><span>2</span></sub> adsorption isotherm, Fourier-transform infrared spectroscopy, thermogravimetric analysis and high-resolution transmission electron microscopy were performed to characterise the thermal stability and purity of the Mg-MOFs. The adsorption studies on four typical volatile organic compounds (VOCs) emitted during wood drying showed that Mg-MOFs have noteworthy adsorption capacities, especially for benzene and β-pinene with adsorptions of 182.26 mg/g and 144.42 mg/g, respectively. In addition, the adsorption of Mg-MOFs mainly occurred via natural adsorption, specifically, multi-layer physical adsorption, accompanied by chemical forces, which occurred in the pores where the VOCs molecules combined with active sites. As an adsorbent, Mg-MOFs exhibit versatile behaviour for toxic-gas accumulation.</p>
Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning
<p>It contains reference training and test set configurations (and corresponding energy, forces, and virial stress values) for ZIF-8 and MOF-5.</p>
Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization
<p>This repository contains CIF files for metal-organic frameworks and Grand canonical Monte Carlo (GCMC) simulation results for the article <em>Multi-Scale Computational Design of Metal-Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization</em> by Zijun Deng and Lev Sarkisov.</p>
Digital Design and Discovery of Biological Metal-Organic Frameworks for Gas Signaling
<p>This repository contains the structures, features and compositions of Bio-hMOFs database.</p> <ol> <li>Fragments and Composition: Contains the building block fragments used to generate the Bio-hMOFs.</li> <li>Structures-CIFs: Contains the structures of Bio-hMOFs</li> <li>Geometric and RACs: Contains the geomtric and RACs features of Bio-hMOFs</li> <li>Adsorption Capacity: Contains the adsorption uptake of NO and CO adsorption simulated under 298K under 1 bar and 10 bar.</li> <li>Mechanical Properties; Computed mechanical properties</li> </ol>
Facile synthesis of magnesium-based metal-organic framework with tailored nanostructure for effective VOCs adsorption
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Scripts and Datasets for Photocatalytic Nanoscale Metal-Organic Framework for Proximity Labeling in Living Cells
<p>Please read data_process.ipynb for more details.</p>
Role of Counterions in the Structural Stabilisation of Redox-Active Metal-Organic Frameworks
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Research data for "Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks"
<p>This dataset supports the paper: "Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks".</p> <p>The coarse-grained scaled and unscaled structures are provided here in CIF format. The code introduced in this work is subject to continuing development (and can be found at: https://github.com/tcnicholas/coarse-graining), therefore we include here the version of the code used for this paper alongside the Python analysis scripts.</p> <p>The original unprocessed CIFs were extracted from the Cambridge Structural Database (CSD).</p>
Twinning in Zr-based Metal-organic Framework Crystals
<p>Cif files for three metal-organic framework compounds. </p>
Exploring the Chemical Design Space of Metal-Organic Frameworks for Photocatalysis
<p>In this work, we employ a chemical insights-based diversity-driven approach to search for metal-organic framework (MOF) photocatalysts. With an in silico design based on chemical insights, we populated areas in the chemical design space related to MOFs with photocatalytic potential. We selected a balanced dataset of DFT-based photocatalytic descriptors computed for 314 MOFs, comprising our in silico structures, a diverse subset of the QMOF database, and experimental MOF photocatalysts. With such a balanced dataset, we could fine-tune supervised machine-learning models from literature that allowed us to draw insights into relevant areas in the chemical design space for photocatalysis and potential bottlenecks.<br>Among our in silico MOFs, a few motifs stood out, such as Au-pyrazolate, Ti clusters and rod-shaped metal nodes, and a particular MOF designed with the Mn4Ca cluster, which mimics the OER center in the photosystem II of photosynthesis.<br>Overall, by combining three pillars --- the design of potential MOF photocatalysts guided by chemical insights, the DFT evaluation of photocatalytic descriptors, and the machine-learning approach --- we were able to gain insights into structure-property relationship, and identify trends in the chemical design space that can open new avenues for advancing the field of photocatalysis.</p>
Toward Understanding Drug Incorporation and Delivery from Biocompatible Metal-Organic Frameworks in View of Cutaneous Administration
<p>Although metal−organic frameworks (MOFs) have widely<br> demonstrated their convenient performances as drug-delivery systems, there is<br> still work to do to fully understand the drug incorporation/delivery processes<br> from these materials. In this work, a combined experimental and<br> computational investigation of the main structural and physicochemical<br> parameters driving drug adsorption/desorption kinetics was carried out. Two<br> model drugs (aspirin and ibuprofen) and three water-stable, biocompatible<br> MOFs (MIL-100(Fe), UiO-66(Zr), and MIL-127(Fe)) have been selected to<br> obtain a variety of drug−matrix couples with different structural and<br> physicochemical characteristics. This study evidenced that the drug-loading<br> and drug-delivery processes are mainly governed by structural parameters<br> (accessibility of the framework and drug volume) as well as the MOF/drug<br> hydrophobic/hydrophilic balance. As a result, the delivery of the drug under<br> simulated cutaneous conditions (aqueous media at 37 °C) demonstrated that<br> these systems fulfill the requirements to be used as topical drug-delivery systems, such as released payload between 1 and 7 days.<br> These results highlight the importance of the rational selection of MOFs, evidencing the effect of geometrical and chemical<br> parameters of both the MOF and the drug on the drug adsorption and release.</p>
Molecular diffusion enhanced performance evaluation of metal-organic frameworks for carbon dixoide capture
<p>This data set contains the process-level performance ranking of 982 metal-organic frameworks (MOF) which were down-selected from 10,143 structures contained in the puplic CoRE MOF 2019 data set. To rankorder MOFs for application in post-combustion carbon dioxide capture, we have used a computational workflow that combines active-learning based structure selection, molecular-level modeling, and process-level optimization. A detailed description of the repository content is provided in the README file which is included in the zip archive “Diffusion-MOF-Screening.zip”.</p>
Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach: Workflow and Data
<p>This repository contains input files, workflow scripts, and output datasets and interatomic potentials for a diverse set of metal-organic frameworks, as discussed in this <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6363dbf718a8ccae675d2ac8">preprint paper</a>. In addition, we provide the scripts to compute the extended Hessian and subsequently the elastic constants using automatic differentiation.</p>
Pore-engineered metal-organic frameworks (MOFs) for efficient delivery of diverse functional macromolecular cargoes
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Aptamer-modified 5-fluorouracil encapsulated metal-organic framework nanodrugs for enhanced therapy of colorectal cancer via peroxidative induced cell death
GEO Series GSE315569. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
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Allen Brain Atlas
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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.