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60 results for “density functional theory”
Dataset of "Asparagine-Modified Magnetic Graphene Oxide: An Efficient and Green Nanocatalyst for Synthesis of 5-oxodihydropyrano[3,2-c]chromenes and dihydropyrano[2,3- c]pyrazole derivatives and the Density functional theory calculation".
<p>The primary focus of this study involved the fabrication of a novel nanocatalyst Fe3O4-supported asparagine functionalized graphene oxide (Fe3O4@GO-N-(Asparagine)). The catalyst was synthesized through a four-step procedure.</p>
Density functional theory calculations of coherent bcc Fe-Cu interfacial energy densities
<p>File contains the data required to calculate interfacial energy densities of {100}, {110}, {111}, {210}, {211} and {221} orientated coherence bcc Fe-Cu interfaces.</p> <p>Data produced for the study detailed in: Cu nanoprecipitate morphologies and interfacial energy densities in bcc Fe from density functional theory (DFT) A.M. Garrett and C.P. Race.</p> <p>Submitted to Computational Materials Science.</p> <p>.txt files contain the total energies calculated for relaxed interface-containing and bulk simulation cells at a range of interfacial spacings. This data can be used to calculate the size independent interfacial energy densities for a range of Fe-Cu interface orientations using standard fitting approaches. Columns of the tables in the .txt files are no. atoms, interface-containing simulation cell length, interfacial area, total energy of the relaxed interface-containing simulation cell, total energy of the reference bulk Fe and total energy of the reference bulk Cu. Lengths are in Angstrom and energies are in eV.</p>
Supplementary CIF files for "Shedding Light on the Enigmatic TcO2 ⋅ xH2O Structure with Density Functional Theory and EXAFS Spectroscopy"
<p>Optimized geometries from the paper "Shedding Light on the Enigmatic TcO2 ⋅ <em>x</em>H2O Structure with Density Functional Theory and EXAFS Spectroscopy" (<a href="https://doi.org/10.1002/chem.202202235">https://doi.org/10.1002/chem.202202235</a>), provided in CIF format.</p> <p>All structures were fully optimized (lattice vectors and atomic coordinates) using AMS/BAND (<a href="https://www.scm.com/">https://www.scm.com/</a>) with the PBE density functional, scalar relativistic effects (ZORA), and numerical atomic orbitals (NAOs) augmented with a triple-zeta polarized (TZP) set of Slater-type basis functions. For the chains, D3 dispersion corrections were also included.</p> <p> </p>
High-Throughput Density Functional Theory Screening of Double Transition Metal MXene Precursors
<p>This dataset contains density functional theory results on a set of double-transition metal MXene precursors</p>
Dataset: Environment effects on X-ray absorption spectra with quantum embedded real-time Time-dependent density functional theory approaches
<p>This dataset collects the outputs from real-time TDDFT simulation of X-ray absorption of halides in model systems, using the frozen density embedding (FDE) and block-orthogonalized Manby-Miller embedding (BOMME), as well as processing tools and scripts used to carry out the calculations.</p>
Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.
<p>## Abstract</p> <p>from [1]:</p> <p>Mechanochemistry is a fast-developing field of interdisciplinary research with a growing number of applications. Therefore, many theoretical methods have been developed to quickly predict the outcome of mechanically induced reactions. Constrained geometries simulate External Force (CoGEF) is one of the earlier methods in this field. It is easily implemented and can be conducted with most DFT codes. However, recently, we observed totally different predictions for model systems of epoxy resins in different conformations and with different density functionals. To better understand the conformational and functional dependence in typical CoGEF calculations we present a systematic evaluation of the CoGEF method for different model systems covering homolytic and heterolytic bond cleavage reactions, electrocyclic ring opening reactions and scission of non-covalent interactions in hydrogen-bond complexes. From our calculations we observe that many mechanochemical descriptors strongly depend on the functional used, however, a systematic trend exists for the relative maximum Force. In general, we observe that the CoGEF procedure is forcing the system to high energetic regions on the molecular potential energy profiles, which can lead to unexpected and uncorrelated predictions of mechanochemical reactions. This is questioning the true predictive character of the method.</p> <p> </p> <p>## Contact</p> <p>Christian R. Wick</p> <p>Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Science, Department of Physics, PULS Group, Interdisciplinary Center for Nanostructured Films (IZNF), Cauerstrasse 3, 91058, Germany</p> <p> </p> <p>## License</p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p>## Context</p> <p>Dataset to paper [1]</p> <p> </p> <p>## Contents</p> <ul> <li>All COGEF trajectories in xyz format.</li> <li>All CoGEF distances and DFT Energies in csv format.</li> </ul> <p>The following DFT levels of theory were investigated:</p> <ul> <li>B3LYP/6-31G(d)</li> <li>B3LYP-D3BJ/def2-SVP</li> <li>BP86-D3/def2-SVP</li> <li>PBE1PBE/def2-SVP</li> <li>M06-D3/def2-SVP</li> </ul> <p> </p> <p>## Folder structure</p> <ul> <li>- compound_X : data set for compound number X (numbering corresponds to the numbering scheme in [1]) <ul> <li>the xyz trajectories follow the following naming convention: "DFT_method"_"unrestricted/restricted".xyz</li> <li>the csv files follow the naming convention: "DFT_method"_"unrestricted/restricted".xyz.csv</li> </ul> </li> </ul> <p>## Software</p> <p>### COGEFF calculations: COGEF.py v1.8.0</p> <p>Zenodo release:</p> <p>https://doi.org/10.5281/zenodo.7079733</p> <p>### DFT calculations:</p> <p>Gaussian 16 Rev B [2]</p> <p> </p> <p>## Funding</p> <p>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/1-2019 FRASCAL.</p> <p><br> ## References</p> <p>[1] C. R. Wick, E. Topraksal, D. M. Smith, A.-S. Smith, "Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.", Forces in Mechanics, 9, 100143; doi:10.1016/j.finmec.2022.100143</p> <p>[2] Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; et al. Gaussian 16 Rev. B.01, 2016.</p>
Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions
<p>For reproducing the results presented in "<strong>Hashemi, A., Peljo, P., & Laasonen, K. (2022). Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions</strong>", this database provides the input files and CDFT-AIMD trajectory information. Please refer to the publication if you wish to use these data.</p> <p>---------------------------------------**************************************************************************-------------------------------------------------</p> <p><em>This study was financed by the Horizon 2020 Framework Programme CompBat with project number 875565. We also thank CSC-IT Center for Science Ltd. and Aalto Science-IT project for generous grants of computer time.</em><br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>The content of a directory is shown in a tree-like format:</strong><br> ├── 1DMDQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 2MeVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── mevi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 3OHVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── ohvi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ ├── b_to_a.tar.gz<br> │ │ ├── b_to_c<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 4dBR5<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 52HNQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── hnq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> └── 6_n_H2O_effect_mevi<br> ├── 08h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 10h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 20h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 40h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 97h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> └── fig3.png</p> <p>74 directories, 301 files<br> -------------------------------------------------------<br> There are 6 directories: 1DMDQ, 2MeVi, 3OHVi, 4dBR5, 52HNQ, 6_n_H2O_effect_mevi. Except for "6_n_H2O_effect_mevi", we see 3 subdirectories named 1_md, 2_cdftaimd, and 3_cdft_wH2O_sccs. The input files and AIMD trajectories can be found in 1_md. While 2_cdftaimd contains the CDFT-AIMD input files and trajectories. To reproduce snapshots and input files of 3_cdft_wH2O_sccs, follow the README files in the subdirectories.</p> <p>The directory "6_n_H2O_effect_mevi" contains the number of water effects (Figure 3 of the publication). Users are guided by README files once again. </p>
Raw Data to "Density functional theory study of CO formation through reactions of polycyclic aromatic hydrocarbons with atomic oxygen (O(3P))"
<p>This data is a supplement to the publication <a href="https://doi.org/10.1016/j.fuel.2018.12.047">https://doi.org/10.1016/j.fuel.2018.12.047</a>. The data includes Turbomole input and output files. The calculations are performed using DFT/TPSSh-D3/TZVP method and Turbomole version 7.2. The equilibrium structures for the reactions of polyaromatics are named as following:<br> C<sub>X</sub>H<sub>Y</sub> (<strong>S1</strong>) + O -> C<sub>X</sub>H<sub>Y</sub>O (<strong>S2</strong>) -> C<sub>X</sub>H<sub>Y-1</sub>O (<strong>S3</strong>) + H (i) O addition and H abstraction<br> C<sub>X</sub>H<sub>Y-1</sub>O (<strong>S3</strong>) [-> <strong>S4</strong> -> <strong>S5</strong> ] -> CX-1HY-1 (<strong>S6</strong>) + CO (ii) Single, two, or three step CO elimination</p> <p>The transition state structures are named according to the naming of the corresponding reactant and product. For example, the transition state connecting the structure S3 to S5 is named as T35. Under some of the transition state directories, intrinsic reaction coordinate calculation output can be found under the directories named as "IRC".<br> <br> </p> <p><br> The LibreOffice Calc spreadsheet "SUPPINFO.ods" includes the activation and reaction energies to the reaction steps.</p>
Dataset to "Hydrogen in tungsten trioxide by membrane photoemission and density functional theory modeling"
<p>Dataset to "Hydrogen in tungsten trioxide by membrane photoemission and density functional theory modeling" as published in Physical Review B, 103 (2021), 205304</p>
Dataset for "Solving deep-learning density-functional theory via variational autoencoder"
<p>The dataset contains the ground state energies, the ground state density profiles, and the external potentials of a 3D single particle system with a Gaussian-like external potential.<br>The number of grid points for each dimension is \(N_g=18\), the linear length of the box is \(L=a_0\) with \(a_0\) the unit of length. The unit of energy is \(E_0=\frac{ \hbar^2}{(m a_0^2)}\).</p> <p> </p> <p> </p> <p>The dataset is zip file of a Python npz file with the following keys:</p> <p>- "density" that corresponds to the ground state density profile.<br>- "potential" is the external potential.<br>-"energy" is the ground state energy.</p> <p><br>The number of instances is 36000. </p> <p>-3D_gaussian.zip -> 3D_gaussian.npz</p> <p> a dictionary with three keys -density, potential, energy-.<br> The dimension of both potential and density is \([N_d,N_g,N_g,N_g]\).<br> The shape of energy is \([N_d]\).<br> \(N_d=36000\)</p> <p>-3D_gaussian_transfer_test_1.npz</p> <p> a dictionary with three keys -density, potential, energy-.<br> The dimension of both potential and density is \([N_d,N_g,N_g,N_g]\).<br> The shape of energy is \([N_d]\).<br> \(N_d=500\)</p> <p>-3D_gaussian_transfer_test_2.npz</p> <p> a dictionary with three keys -density, potential, energy-.<br> The dimension of both potential and density is \([N_d,N_g,N_g,N_g]\).<br> The shape of energy is \([N_d]\).<br> \(N_d=500\)</p>
Density functional theory calculations of 1D hybrid nanoobjects composed of alternating polycyclic hydrocarbon regions and double carbon chains
<p>It has been proposed recently based on molecular dynamics simulations that electron irradiation of graphene nanoribbons of alternating width can lead to creation of 1D hybrid nanoobjects composed of alternating double carbon chains and polycyclic hydrocarbon regions [1]. We have performed density functional theory calculations of such 1D hybrid nanoobjects using Quantum ESPRESSO [2]. Semi-local exchange and correlation functional of Perdew, Burke and Ernzerhof [3] and screened exchange hybrid density functional of Heyd, Scuseria and Ernzerhof [4] were used. The dependences of structure, magnetic and electronic properies on the length of chains and type of the polycyclic hydrocarbon region were studied.</p> <p>I.V.L acknowledges the IKUR HPC project "First-principles simulations of complex condensed matter in exascale computers" funded by MCIN and by the European Union NextGenerationEU/PRTR-C17.I1, as well as by the Department of Education of the Basque Government through the collaboration agreement with nanoGUNE within the framework of the IKUR Strategy, computer resources at MareNostrum and the technical support provided by Barcelona Supercomputing Center (RES grant nos. FI-2022-1-0023, FI-2022-2-0035, FI-2022-3-0048 and FI-2023-1-0037). A.M.P., and Y.E.L. acknowledge the support by the Russian Science Foundation grant No. 23-42-10010, https://rscf.ru/en/project/23-42-10010/. S.A.V. and N.A.P. acknowledge support by the Belarusian Republican Foundation for Fundamental Research (Grant No. F23RNF-049) and by the Belarusian National Research Program "Convergence-2025".</p> <p>[1] A. S. Sinitsa, I. V. Lebedeva, Y. G. Polynskaya, D. G. de Oteyza, S. V. Ratkevich, A. A. Knizhnik, A. M. Popov, N. A. Poklonski, and Y. E. Lozovik, “Transformation of a graphene nanoribbon into a hybrid 1D nanoobject with alternating double chains and polycyclic regions,” Phys. Chem. Chem. Phys. 23, 425–441 (2021).</p> <p>[2] P. Giannozzi et al., “Advanced capabilities for materials modelling with Quantum ESPRESSO,” J. Phys.: Condens. Matter 29, 465901 (2017).</p> <p>[3] J. P. Perdew, K. Burke, and M. Ernzerhof, “Generalized gradient approximation made simple,” Phys. Rev. Lett. 77, 3865–3868 (1996).</p> <p>[4] J. Heyd, G. E. Scuseria, and M. Ernzerhof, “Hybrid functionals based on a screened Coulomb potential,” J. Chem. Phys. 118, 8207–8215 (2003).</p>
Understanding Electrochemical Reversibility using Density Functional Theory: Bridging Theoretical Scheme of Squares and Experimental Cyclic Voltammetry
<p>#<strong> Scheme of Squares</strong></p> <p>## <strong>Overview</strong></p> <p>The `SchemeOfSquares.tar` archive contains essential data and examples related to our research on redox reactions. The contents are organized into two primary subfolders: `datasets` and `examples`.</p> <p>##<strong> Getting Started</strong></p> <p>### <em><strong>Extracting the Archive</strong></em></p> <p>To extract the contents of the `SchemeOfSquares.tar` file, use the following command in a Linux environment:</p> <p>```sh<br>tar -xvf SchemeOfSquares.tar<br>```</p> <p>### <strong><em>Directory Structure</em></strong></p> <p>After extracting, you will find the following structure:</p> <p>- **datasets/**<br> - **ET/**: Contains Gaussian input and output files for electron transfer (ET) redox reactions.<br> - **PET/**: Contains Gaussian input and output files for proton-coupled electron transfer (PET) redox reactions.<br> <br>- **examples/**: Includes sample cases discussed in the main paper, along with the corresponding scaling code.</p> <p>## <strong>Details</strong></p> <p>### <em><strong>Datasets</strong></em></p> <p>- **ET Subfolder**: Houses all the data files related to electron transfer reactions. Each file here represents a specific reaction and contains Gaussian input and output data.<br>- **PET Subfolder**: Contains data files for proton-coupled electron transfer reactions, similarly structured with Gaussian input and output data.</p> <p>### <em><strong>Examples</strong></em></p> <p>- The `examples` folder provides illustrative samples that were elaborated upon in the main research paper. This includes the scaling code necessary for replicating the results.</p> <p>## <strong>References</strong></p> <p>For a comprehensive understanding of the data and examples provided, please refer to the main paper associated with this repository.</p> <p>## <strong>Contact</strong></p> <p>For any questions or further information, please contact Amir Mahdian / Arsalan Hahsemi at firstname.lastname@aalto.fi.</p>
Supplementary data for "Stability and flexibility of Heterometallic Formate Perovskites with the Dimethylammonium Cation: Pressure-induced Phase transitions and Density Functional Theory Calculations"
<p>Optimized structures for DMANaCr, DMAKCr and DMAZn.</p> <p>For each structure there is a zip-file containing the force constants used for the phonon calculation, the phonon frequencies at the gamma point, the calculated thermal properties and the phonon partial density of states.</p> <p>For further information see the associated paper.</p>
Density Functional Theory Calculations of Segregation Tendency of Cu and Zn in Al3Zr Dispersoid Particles
<p>The .zip archive contains data related to DFT calculations published in the paper:</p> <p>Dispersoid Composition in Zirconium Containing Al-Zn-Mg-Cu (AA7010) Aluminium Alloy<br> A.M. Cassell, J. D. Robson, C. P. Race, A. Eggeman, T. Hashimoto, M. Besel.</p> <p>Submitted to Acta Materialia.</p> <p>Archive contains a set of .txt files, each of which contains the total energies of a series of simulations along with several other output fields and descriptive fields.</p> <p>The Archive also contains a .ipynb Jupyter (Python) Notebook, which contains descriptions of the .txt files, the code required to import them and the analysis required to produce the figure in the published paper.</p>
Data for "On the atomic structure of the β′′ precipitate by density functional theory"
<p>The dataset contains the DFT results which is the basis for the results and discussions in the related article, "On the atomic structure of the β′′ precipitate by density functional theory". The details of the DFT calculations are written in the article.</p> <p>The names of the OUTCAR files in enthalpy_study_OUTCARS.tar.gz are more or less self-explanatory, at least within the context of the journal article. The KPOINT tests have the following format for the KPOINTS "XYZ" where X is always a single digit, Y is first to get a double-digit, while Z gets a double-digit second. The max distance in reciprocal space is thus not a constant as the OUTCAR files would suggest.</p> <p> </p> <p>The LET_DATA is the linear-elastic theory displacement-field as explained in the article for different aspect ratios of the precipitate eye structure.</p>
Amorphous Niobium Oxide Structures Calculated from First Principles using Density Functional Theory and Molecular Dynamics
<p>The dataset contains fifteen different amorphous niobium oxide structures. Nine of the structures have the same stoichiometry as Nb2O5. The other six are defect structures containing 1 or 2 oxygen vacancies, or 1 or 2 interstitial oxygens, or 1 Nb vacancy. Each of the structure files is in the VASP POSCAR file format. Each structure was created using ab-initio molecular dynamics at 5000~K to liquidate the structure, then snapshots of the structure were taken every 2 ps, and geometry optimizations were performed on each individual snapshot. The naming convention is relatively simple: 'conf_x_POSCAR' is a stoichiometric POSCAR, and 'conf_x_oadd1_POSCAR' is a defect structure originating from structure 'x' with a single oxygen interstitial. The defect labels correspond to 1 oxygen interstitial (oadd1), 2 oxygen interstitials (oadd2), 1 oxygen vacancy (ovac1), 2 separated oxygen vacancies (ovac2), 2 nearest neighbor oxygen vacancies (ovac2nn), and 1 Nb vacancy (nbvac).</p>
Dataset: Core excitations and ionizations of uranyl in Cs2UO2Cl4 from relativistic embedded damped response time-dependent density functional theory and equation of motion coupled cluster calculations
<p>This dataset collects the unprocessed (= outputs from calculations) results discussed in the paper titled "Core excitations and ionizations of uranyl in Cs2UO2Cl4 from relativistic embedded damped response time-dependent density functional theory and equation of motion coupled cluster calculations", by Wilken Aldair Misael and Andre Severo Pereira Gomes. It also contains the figures used in the manuscript.</p>
Data from Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory and machine learning. Parts I and II.
<p>Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning; Parts I and II.</p> <p>This data-set is published in Refs. [1-2] and it is now made openly accessible. The data-set consists of computational X-ray spectroscopy fingerprints of plain and functionalized amorphous carbon. This data can be used in interpretation of experimental spectroscopy data (XAS and XPS). The spectra are averages of certain atomic environments, that are described in the publications. Standard deviation is included in the third column. Please, feel free to use the data-set, and if you do so, remember to cite Refs. [1-2] and this source. If there are any questions, please contact the corresponding author. </p> <p> </p> <p>[1] A. Aarva, V. L. Deringer, S. Sainio, T. Laurila, andM. A. Caro, “Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part I: Fingerprint spectra,” Chem. Mater. 31, 9243–9255 (2019).</p> <p>[2] A. Aarva, V. L. Deringer, S. Sainio, T. Laurila, andM. A. Caro, “Understanding X-ray spectroscopy of carbonaceous materials by combining experiments, density functional theory, and machine learning. Part II: Quantitative fitting of spectra,” Chem. Mater. 31, 9256–9267(2019).</p> <p> </p> <p>Funding and resources for the work are acknowledged as follows:</p> <p>Funding from the Academy of Finland (project no.285526) and the computational resources provided for this project by CSC – IT Center for Science are gratefully acknowledged. M. A. C. acknowledges personal funding from the Academy of Finland under project no. 310574.V. L. D. acknowledges a Leverhulme Early Career Fellowship and support from the Isaac Newton Trust. M. A. C.and V. L. D. are grateful for travelling support from the HPC-Europa3 program under the auspices of the European Union’s Horizon 2020 framework (grant agreement no. 730897). Use of the Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under contract no. DE-AC02-76SF00515. S. S. acknowledges personal funding from Instrumentarium Science Foundation and the Walter Ahlström Foundation.</p> <p> </p>
Research Data supporting "Linear-Scaling Density Functional Theory using the Projector Augmented Wave Method"
<p>Research Data supporting "Linear-Scaling Density Functional Theory using the Projector Augmented Wave Method" by Nicholas D. M. Hine</p>
Optical properties of monolayer and multilayer 1T' WTe2 calculated by density functional theory
<p>We investigate the optical properties of monolayer, bilayer, trilayer, and quadrilayer WTe<sub>2</sub> in the 1T' crystal phase by ab-initio calculations based on spin-polarized density functional theory (DFT). We used the Vasp package to calculate the real and imaginary part of the dielectric function, the real and imaginary part of the refractive index, as well as the optical absorbance from 0 eV to 5 eV and for different crystal directions.</p>
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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.