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88 results for “ab initio”
Data for "Ab initio studies of the Rg–NO+(X1Σ+) van der Waals complexes (Rg = He,Ne, Ar, Kr, and Xe)"
<p>This upload contains data files with tabulated values of Potential Energy surfaces calculated with CCSD(T)-F12 method for the Rg-NO(+)(r=re), where Rg=He, Ne, Ar, Kr and Xe published in the following paper: </p> <p>Citation: Cahit Orek, Jacek Kłos, François Lique and Niyazi Bulut, The Journal of Chemical Physics 144, 204303 (2016); <br> doi: 10.1063/1.4950813 <br> View online: http://dx.doi.org/10.1063/1.4950813</p> <p>Files:</p> <p>Readme_RgNOplus.txt<br> He-NO(+): HeNOplus_re_Eint.dat<br> Ne-NO(+): NeNOplus_re_Eint.dat<br> Ar-NO(+): ArNOplus_re_Eint.dat<br> Kr-NO(+): KrNOplus_re_Eint.dat<br> Xe-NO(+): XeNOplus_re_Eint.dat<br> Content of the files: <br> Column 1: r(NO)=re=2.0125 bohr<br> Column 2: Jacobi distance R in bohr describing distance of Rg from the center of mass of NO(+). Grid of ~40 points covers values from 3.5 or 4.5 for larger Rg to 30 bohr. <br> Column 3: Jacobi angle theta between r and R. Grid covers values from 0 to 180 degrees every 10 degrees.<br> Column 4: Interaction energy in cm-1, asymptotic value Eint=0 at infinite separation of Rg from NO(+)(r=re)</p>
Atomistic trajectories from ab-initio molecular dynamics simulations of wetted TiO2 nanoparticle
<p>This repository contains atomistic trajectories from ab-initio molecular dynamics simulations of water and TiO2 nanoparticle described in the paper:</p> <p>E. G. Brandt, L. Agosta and A.P.Lyubartsev, " Reactive wetting properties of TiO2 nanoparticles predicted by ab initio molecular dynamics simulations", Nanoscale, 8, 13385-13398 (2016) DOI: 10.1039/c6nr02791a</p> <p>The trajectories are saved in the .xtc format, and initial structures with specification of atom types are given in the .pdb format.</p> <p>The name of each file contains brief information about the simulated system:</p> <p>TiO2 : composition of the nanoparticle<br> n24 : number of TiO2 units in the nanoparticle<br> anatase/brookite/rutile : type of crystall structure<br> - a number 0 - 30 : number of water molecules in the simulation<br> 2fs - the time step</p> <p>For more details, see the referred paper</p>
Ab-initio molecular dynamics trajectories of fully hydrated TiO2 surfaces
<p>This data set contains trajectories of ab-initio molecular dynamics simulations of TiO<sub>2</sub> surfaces in water described in the paper:</p> <p>L.Agosta, E.G.Brandt and A.P.Lyubartsev<br> "Diffusion and reaction pathways of water near fully hydrated TiO<sub>2</sub> surfaces from ab initio molecular dynamics",<br> J.Chem.Phys., 147, 024704 (2107) doi: http://dx.doi.org/10.1063/1.4991381</p> <p>Trajectories of 6 fully hydrated TiO2 surfaces are stored under respective names. Each trajectory file contains 50 ps of simulation with frames saved every 0.0005 ps. Format: PDB, gzipped.</p> <p> </p>
Alphafold2_ab_initio iterative predictions for folding intermediate identification
<p>PDB ids starts from 1 and 8, rmsds, plddts, t-sne embeddings.</p> <p>Check related biorxiv preprint: AlphaFold2 knows some protein folding principles; DOI: https://doi.org/10.1101/2024.08.25.609581.</p>
Data for: On-the-fly ab initio semiclassical evaluation of third-order response functions for two-dimensional electronic spectroscopy
<p>Data for publication: T. Begusic, J. Vanicek, On-the-fly ab initio semiclassical evaluation of third-order response functions for two-dimensional electronic spectroscopy, <em>J. Chem. Phys.,</em> <strong>153</strong>, 184110 (2020).</p> <p>Contains simulated linear and two-dimensional spectra of the S<sub>1</sub> - S<sub>0</sub> electronic transition of phenol, excited-state and ground-state ab initio trajectories at the PBE0/6-311G(d,p) level of theory, and other supporting data related to the abovementioned publication.</p>
TREXIO files used for the validation tests in the paper entitled 'TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods'.
<p>The TREXIO files used for the validation tests in the paper entitled TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods. The detail about the TREXIO library is described in the JCP article [J. Chem. Phys. 158, 174801 (2023)] and the GitHub repository [https://github.com/TREX-CoE/trexio]. The TREXIO files were generated using TREXIO version 2.3.2 (and the corresponding Python API version 1.3.2).</p>
Ab Initio and Kinetic Modelling of β-D-xylopyranose under Fast Pyrolysis Conditions
<p>Zip file containing all the IRC connecting transition states to minima in our paper published in The Journal of Physical Chemistry A, <a title="DOI URL" href="https://doi.org/10.1021/acs.jpca.3c07063">https://doi.org/10.1021/acs.jpca.3c07063</a></p>
Realistic Ab Initio Predictions of Excimer Behavior under Collective Light-Matter Strong Coupling - Dataset
<p>Dataset complement to "Realistic Ab Initio Predictions of Excimer Behavior under Collective Light-Matter Strong Coupling" - includes outputs obtained using the <a href="https://etprogram.org/development-website/">eT program</a>, an open-source electronic (and molecular-polaritonic) structure program, and VIBROT (OpenMolcas).</p> <p> </p> <p>See the paper at <a href="https://journals.aps.org/prx/abstract/10.1103/PhysRevX.15.021040">https://journals.aps.org/prx/abstract/10.1103/PhysRevX.15.021040</a></p>
JSON files containing parameters of training gene models for ab-initio prediction software
<p>These are the JSON files containing parameters of training gene models for ab-initio prediction software. These training datasets are Phytophthora specific and can be further utilized for the gene prediction and annotation of other related Phytophthora strains.</p>
Data for: Search for long-lasting electronic coherence using on-the-fly ab initio semiclassical dynamics
<p>Data for publication: A. Scheidegger, J. Vanicek, and N. Golubev, Search for long-lasting electronic coherence using on-the-fly ab initio semiclassical dynamics, https://doi.org/10.1063/5.0076609</p> <p>Contains the data for reproducing the figures in the abovementioned publication.</p>
Strong piezoelectric response in stable TiZnN2, ZrZnN2, and HfZnN2 found by ab initio high-throughput approach
<p>The phase diagrams of the Ti-Zn-N, Zr-Zn-N, and Hf-Zn-N systems are determined using large-scale high-throughput density functional calculations. In total 12,815 relaxed structures are shared alongside their energy calculated using the VASP DFT code. The High-Throughput Toolkit was used to manage the calculations.</p> <p>A README file is included that describes how to load the data and the contents of the DataFrame.</p>
Data for Linking Experimental and Ab-initio Thermochemistry of Adsorbates with a Generalized Thermochemical Hierarchy
<p>Data and scripts for the preprint "Linking Experimental and Ab-initio Thermochemistry of Adsorbates with a Generalized Thermochemical Hierarchy".</p>
Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. Part I: Adaptive local approximate models
<p>The global search stage of Crystal Structure Prediction (CSP) methods requires a fine balance between accuracy and computational cost, particularly for the study of large flexible molecules. A major improvement in the accuracy and cost of the intramolecular energy function used in the CrystalPredictor II (Habgood, M., Sugden, I. J., Kazantsev, A. V., Adjiman, C. S. & Pantelides, C. C. (2015).<em> J Chem Theory Comput</em> <strong>11</strong>, 1957-1969) program is presented, where the most efficient use of computational effort is ensured via the use of adaptive Local Approximate Model (LAM) placement. The entire search space of relevant molecule’s conformations is initially evaluated using a coarse, low accuracy grid. Additional LAM points are then placed at appropriate points determined via an automated process, aiming to minimise the computational effort expended in high energy regions whilst maximising the accuracy in low energy regions. As the size, complexity, and flexibility of molecules increase, the reduction in computational cost becomes marked. This improvement is illustrated with energy calculations for benzoic acid and the ROY molecule, and a CSP study of molecule XXVI from the sixth blind test (Reilly <em>et al.</em>, (2016).<em> Acta Cryst. B, accepted</em>.), which is challenging due its size and flexibility. Its known experimental form is successfully predicted as the global minimum. The computational cost of the study is tractable without the need to make unphysical simplifying assumptions. </p>
Ab initio data to generates machine-learned force fields of ions in aqueous medium in VASP format.
<p>ML_AB_H: Datasets of 64 water molecules and a single proton with 64 water molecules.</p> <p>ML_AB_VP2: Datasets of a single V^2+ ion with 64 water molecules.</p> <p>ML_AB_VP3: Datasets of a single V^3+ ion with 64 water molecules.</p> <p>ML_AB_FeP2: Datasets of a single Fe^2+ ion with 64 water molecules.</p> <p>ML_AB_FeP3: Datasets of a single Fe^3+ ion with 64 water molecules.</p> <p>ML_AB_CuP1: Datasets of a single Cu^+ ion with 64 water molecules.</p> <p>ML_AB_CuP2: Datasets of a single Cu^2+ ion with 64 water molecules.</p> <p>ML_AB_RuP2: Datasets of a single Ru^2+ ion with 64 water molecules.</p> <p>ML_AB_RuP3: Datasets of a single Ru^3+ ion with 64 water molecules.</p> <p>ML_AB_AgP1: Datasets of a single Ag^+ ion with 64 water molecules.</p> <p>ML_AB_AgP2: Datasets of a single Ag^2+ ion with 64 water molecules.</p> <p>ML_AB_O2: Datasets of a single O2 ion with 64 water molecules.</p> <p>ML_AB_O2N1: Datasets of a single O2^- ion with 64 water molecules.</p> <p>ML_AB_water: Datasets of 64 water molecules presenting bulk water and 64 water molecules representing water slab.</p> <p>All datasets were generated by VASP using PAW, plane wave basis sets with cutoff energy of 520 eV and RPBE+D3 exchange-correlation functional with zero-damping. All ab initio calculations were done on extended systems with periodic boundary conditions. See details in <a href="https://doi.org/10.48550/arXiv.2409.11000">https://doi.org/10.48550/arXiv.2409.11000</a>.</p>
Research Data For "Structure and Interactions at the Mg(0001)/Water Interface: An ab initio Study" Paper
<p>This folder contains data used in the paper "Structure and Interactions at the Mg/Water Interface: An ab initio Study". Some details on the structure are as follows:</p> <p>The folder "figures" contains data to create various figures in the main paper.</p> <p>The folder "md_traj" contains the molecular dynamics trajectory. The file "traj.exyz" is a standard format which can be opened with various software. "traj.json" contains the same information in an in-house format used by the author. The "thermo_data.json" contains various thermodynamics properties over the simulation, such as temperatures and kinetic energies. Units are femtoseconds, Angstrom, electron-volts and Kelvin.</p> <p>The folders "mg_plus_clusters"/"mg_plus_monomer"/"misc_reference_structs" contain geometries for various structures used in the paper (in *.exyz format). These also contain *.json files; these contain information on how the calculations were carried out in a format used by the author (they are small files primarily included for the benefit of the author).</p> <p> </p>
A benchmark study of ab initio gene prediction methods in diverse eukaryotic organisms
<p>G3PO (Gene and Protein Prediction PrOgrams) Benchmark was designed to represent many of the typical challenges faced by current genome annotation projects. The benchmark is based on a carefully validated and curated set of real eukaryotic genes from 147 phylogenetically disperse organisms (from human to protists). <br> </p>
Ab initio umbrella sampling of a potassium ion at the aqueous graphene interface
<p>Ab initio molecular dynamics trajectories obtained with umbrella sampling of a potassium ion at the aqueous graphene interface, where in each trajectory the ion is at a different height from the graphene sheet.</p> <p>This repository contains supplementary data supporting the findings of the paper:</p> <p>L. Joly, R. H. Meissner, M. Iannuzzi, G. Tocci, "Osmotic Transport at the Aqueous Graphene and hBN Interfaces: Scaling Laws from a Unified, First-Principles Description", ACS Nano, 15, 9, 15249–15258 (2021), DOI: 10.1021/acsnano.1c05931.</p>
Ab initio molecular dynamics trajectories of liquid water at the interface with hBN sheets
<p>Ab initio molecular dynamics trajectories of water confined between hBN sheets for different confinement widths and system sizes.</p> <p>This repository contains data supporting the findings of the paper: </p> <p>G. Tocci, M. Bilichenko, L. Joly, M. Iannuzzi, <em>Ab initio</em> nanofluidics: disentangling the role of the energy landscape and of density correlations on liquid/solid friction, Nanoscale, 12, 10994-11000 (2020). DOI: 10.1039/D0NR02511A.</p>
Ab initio molecular dynamics trajectories of liquid water at the interface with graphene sheets
<p>Ab initio molecular dynamics trajectories of water in contact with graphene sheets for different confinement widths and system sizes</p> <p>This repository contains data supporting the findings of the paper: </p> <p>G. Tocci, M. Bilichenko, L. Joly, M. Iannuzzi, <em>Ab initio</em> nanofluidics: disentangling the role of the energy landscape and of density correlations on liquid/solid friction, Nanoscale, 12, 10994-11000 (2020). DOI: 10.1039/D0NR02511A.</p>
Ab initio umbrella sampling of a potassium ion at the aqueous hBN interface
<p>Ab initio molecular dynamics trajectories obtained with umbrella sampling of a potassium ion at the aqueous hBN interface, where in each trajectory the ion is at a different height from the hBN sheet.</p> <p>This repository contains supplementary data supporting the findings of the paper:</p> <p>L. Joly, R. H. Meissner, M. Iannuzzi, G. Tocci, "Osmotic Transport at the Aqueous Graphene and hBN Interfaces: Scaling Laws from a Unified, First-Principles Description", ACS Nano, 15, 9, 15249–15258 (2021). DOI: 10.1021/acsnano.1c05931</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.