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7 results for “structural symmetries”
Data for "Unfolding the structural stability of nanoalloys via symmetry-constrained genetic algorithm and neural network potential"
<p><strong>PtNi_alloy_eam.db</strong> is the dataset (ase.db object) consisting of 55982 intially sampled Pt-Ni alloy structures with EAM energies and forces.</p> <p><strong>PtNi_alloy_dft.db</strong> is the dataset (ase.db object) consisting of the final 6828 resampled Pt-Ni alloy structures with DFT energies and forces calculated by VASP. This is the training set for the NNP, and could be very useful for fitting other machine learning models.</p> <p><strong>PtNi_nanoalloy_vertices_nnp.db</strong> is the dataset (ase.db object) consisting of all the vertices (stable structures) on the convex hulls obtained from NNP-based SCGA runs on 36 Pt-Ni nanoalloy systems. The energies are given by the NNP. Additional information such as mixing energy, motif and symmetry axis are also saved in the dataset and can be queried by the 'data' keyword. An xyz format trajectory of these stable structures is also uploaded.</p> <p>All the input files and scripts for hybrid MC-MD simulations, QBC resampling, DFT calculations, NNP training, NNP-based SCGA runs and convex hull analysis are provided in <strong>inputs_and_scripts.zip</strong>.</p>
Dataset for the manuscript "Observation of time-reversal symmetry breaking in the band structure of altermagnetic RuO2" in Science Advances Vol. 10, No. 5
<p>Dataset for publication "Observation of time-reversal symmetry breaking in the band structure of altermagnetic RuO2" in Science Advances Vol. 10, No. 5, https://doi.org/10.1126/sciadv.adj4883.</p> <p>The details corresponding to the dataset of the figures are given in a readme file in the corresponding folders.</p>
Ground-state structural disorder and excited-state symmetry breaking in a quadrupolar molecule
<p>The files contains all the data that are shown in the figures of the article:</p> <p>Soederberg, M.; Dereka, B.; Marrocchi, A.; Carlotti, B.; Vauthey, E. Ground-state Structural Disorder and Excited-state Symmetry Breaking in a Quadrupolar Molecule. J. Phys. Chem. Lett. 10 (2019), 10.1021/acs.jpclett.9b01024</p> <p> </p>
Data for "Tetramine Aspect Ratio and Flexibility Determine Framework Symmetry for Zn8L6 Self-Assembled Structures"
<p>In the following subdirectories are the input and outputs of cage and face analysis for:</p> <p>Published DOI: <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/anie.202217987">10.1002/anie.202217987 </a></p> <p>Code: <a href="https://github.com/andrewtarzia/sca_cage_assembler/tree/cubism-production">sca_cage_assembler</a></p> <p>Previously uploaded in 10.5281/zenodo.8432296 and <a href="https://github.com/andrewtarzia/citable_data" rel="noopener noreferrer">https://github.com/andrewtarzia/citable_data</a></p> <p>NOTES:</p> <ul> <li>the naming convention differs from manuscript:</li> </ul> <table> <tbody> <tr> <th>manuscript tetra-aniline</th> <th>computational label</th> <th>xtal-label</th> </tr> <tr> <td>A</td> <td>5</td> <td>370</td> </tr> <tr> <td>B</td> <td>16</td> <td>326</td> </tr> <tr> <td>C</td> <td>12</td> <td>235</td> </tr> <tr> <td>D</td> <td>3</td> <td>301</td> </tr> <tr> <td>E</td> <td>8</td> <td>257</td> </tr> <tr> <td>F</td> <td>2</td> <td>354</td> </tr> </tbody> </table> <ul> <li>computational labels are often preceded by `quad2_` or `cl1_quad2_`</li> <li>much of the analysis was not used in the manuscript but remains part of the accumulated data</li> </ul> <p> </p> <p>cage_library directory:</p> <ul> <li>_CS.json: information on all cages in the set of diastereomers - properties and whether they optimized successfully.</li> <li>_ligand_measures.json: information on the ligand associated with a set of cage diastereomers.</li> <li>_measures.json: represenets a cleaned up collation of all measures the diastereomers made from a given ligand</li> <li>C_NAME_optc.mol: optimized (at xTB level) structure of each cage.</li> <li>set_dft_run directory contains the input and output of the CP2K optimisations of one set of diastereomers</li> </ul> <p>complex_library directory:</p> <ul> <li>contains the optimised structures of both complexes</li> </ul> <p>ligand_library directory:</p> <ul> <li>contains `_opt.mol` input ligand structures for cage construction</li> <li>for cap, the input was provided manually in `manual/` directory</li> <li>in `face_analysis` directory: <ul> <li>contains manual_complex directory, with necessary input for face construction</li> <li>_long_properties.json files contains the measurements for the named face (in file name)</li> <li>_long_lopt.mol files contain the optimised structure of the named face, on which analysis was performed</li> <li>`long` corresponds to the longer restricted optimization discussed in the SI.</li> </ul> </li> </ul> <p>xray_structures directory:</p> <ul> <li>analysis directory: <ul> <li>contains input .pdb files for xray structure (as single molecules) used in analysis</li> <li>contains `all_xray_csv_data.csv`, which has all data needed on xray structures.</li> </ul> </li> </ul>
Experimental data and benchmarks used in the paper "Theoretical Foundations for Structural Symmetries of Lifted PDDL Tasks"
<p>This dataset contains both benchmarks and data used in the paper.</p> <p>PDDL benchmark files can be found in the files benchmarks.tar.gz and<br> bagged-benchmarks.tar.gz. The former contains all domains from all IPCs from the<br> repository https://bitbucket.org/aibasel/downward-benchmarks, without duplicate<br> domains that have been used in multiple IPCs. The latter contains the subset of<br> these tasks for which the reformulation in the "bagged representation" from the<br> following paper succeeded:</p> <p>Riddle, P.; Douglas, J.; Barley, M.; and Franco, S. 2016. Improving<br> performance by reformulating PDDL into a bagged representation.<br> In ICAPS 2016 Workshop on Heuristics and Search for Domain-<br> independent Planning, 28–36.</p> <p>We obtained the implementation of the baggy reformluation from the authors.</p> <p>All other files in this dataset contain raw and processed data of all<br> experiments, which were generated using Downward-Lab (see<br> https://doi.org/10.5281/zenodo.399255). The scripts used to run the experiments<br> can be found in the software bundle for this paper (see<br> https://doi.org/10.5281/zenodo.2621897).</p> <p>Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s). Some directories (not for<br> the ground experiments, where these files got too large) also contain a file<br> generators.py that contain all symmetry group generators in permutation<br> notation.</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.</p>
Nonlinear dielectric geometric-phase metasurface with simultaneous structure and lattice symmetry design
Open the record for dataset details and reuse information.
Dataset for "Precise Structure and Energy of Group 6 Transition Metal Dichalcogenide Homo- and Heterobilayers in High-Symmetry Configurations"
<p>The data that support the findings of "Precise Structure and Energy of Group 6 Transition Metal Dichalcogenide Homo- and Heterobilayers in High-Symmetry Configurations".</p>
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International Brain Laboratory public data
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