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5 results for “DFTB”
Zeolite Templated Carbon Materials - DFTB Structural Database
<p>Zeolite-templated carbon (ZTC) is a unique porous carbonaceous material in that its structure is ordered at the nanometre scale, enabling a representative periodic description at the atomistic level. A structural library for ZTC of varying compositions was created using density functional tight binding (DFTB) potentials parameterized for materials science applications (matsci-0-3). We provide here quantum chemical-refined structures of models with CH, CHO, CHON, CHOB, and CHOBN compositions with various degrees of heteroatom substitution. The "initial ZTC structure" files correspond to the initial model used in our work that was developed using molecular mechanics, empirical force fields. These structural models comprise the characteristic morphological features of highly porous carbon materials, such as open-blade surfaces, edges, saddles, and closed-strut formations, spanning a range of curvatures and characteristic sizes. The optimized structures in CIF and native DFTB file formats are organized in the "stationary structure" file based on the optimization pathways that lead to the stationary structures.</p> <p>Secondly, we carried out alternating compression and expansion of the CHO model unit cell to determine the lowest energy structure as well as to obtain the bulk modulus. The file "bulk modulus" contains two data sets that describe the deformational energy landscape of pure faujasite zeolite, Na-substituted zeolite, and the ZTC model structure.</p> <p>The file "analysis tools" is a representative compilation of utilities for file format conversion, fractional vs. Cartesian crystal coordinates, and structural analysis spreadsheets.</p> <p>The agreement between experimental measurements and the computational model is remarkable that demonstrates the power of approximate density functional theory as a cost-effective computational tool with chemical accuracy for the investigation of structure/property relationships in real-world carbon-based solids.</p>
Phenanthrene: TD-DFTB datasets, pre-trained SchNet models and initial coniditions for TSH
<p><em>Data associated with the paper entitled </em></p> <p><strong>On application of Deep Learning to simplified quantum-classical dynamics in electronically excited states</strong></p> <ol> <li>Three TD-DFTB datasets (<strong>sX_10_force.db</strong>) have been produced using the <a href="https://wiki.fysik.dtu.dk/ase/">Atomic Simulation Environment</a> (ASE) coupled to <a href="http://demon-nano.ups-tlse.fr/">deMon-Nano</a> code for the linear response Time-Dependent Density Functional based Tight-Binding (TD-DFTB) calculations. Each dataset contains 10000 TD-DFTB electronic structure calculations for a given excited singlet state (S<sub>2</sub>/S<sub>3</sub>/S<sub>4</sub>) of a neutral phenanthrene molecule. Each database entry contains Cartesian atomic coordinates as well as potential energy and atomic forces for a given excited state at a given geometry. Since ASE has been used, all physical quantities are stored in the corresponding units (e.g. eV for energy or eV/Å for forces). The file format is SQLite as provided by the ASE;</li> <li>Three pre-trained Deep Learning models (<strong>best_model_sX</strong>) for a given excited singlet state have been produced using <a href="https://schnetpack.readthedocs.io/en/stable/">SchNetPack</a> package, which implements the SchNet architecture for atomistic simulations. Each model has been trained using the corresponding TD-DFTB dataset from #1. The file format is binary as provided by the SchNetPack;</li> <li><a href="https://zenodo.org/api/files/f1925cb5-66a8-4c6f-809b-3414f0cbc1d5/500_init_conditions.tar.gz"><strong>500_init_conditions.tar.gz</strong> </a> contains 500 initial conditions (Cartesian coordinates and velocities), which can be used for Trajectory Surface Hopping (TSH) simulations with or without the pre-trained models from #2.</li> </ol> <p> </p>
Dataset for: Anniés et al., "Accessing structural, electronic, transport and mesoscale properties of Li-GICs via a complete DFTB-model with machine-learned repulsion potential"
<p>GPrep training data, GPrep jupyter notebook, .skf files.</p> <p>The GPrep code is available at https://doi.org/10.5281/zenodo.3697913</p>
DFTB study of silica glass under pressure (figures/samples trajectories)
<p>This deposit contains:</p> <p>1) Configurations (XYZ format) for selected densities/pressures (~100 configurations) extracted from the corresponding DFTB trajectories.</p> <p>(see configs-2-deposit.zip). The file readme-configs-2-deposit.txt gives details on configuration file and corresponding box size, pressure and density.</p> <p>2) xmgrace source files of the manuscript's figures (file figures.zip). Each .agr file contains the XY data of the plotted quantities.</p>
Progeny Project DFT and DFTB data of MW9 and MW10 surfactant molecules
<p>DFT and DFTB (see dftb.org) data for the MW9 and MW10 PDI based surfactant molecules of the Progeny project.</p> <p>See README.txt in each subdirectory for more details.</p>
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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.