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37 results for “Quantum mechanics”
Quantum mechanical modeling of the on-grain formation of acetaldehyde on H2O:CO dirty ice surfaces
<p>This Supporting Material contains:</p> <ul> <li>Cartesian coordinates of HF-3c optimized minima and transition state for the reaction in gas phase, in .xyz format, computed using <a href="https://gaussian.com/">Gaussian16</a> code;</li> <li>Fractional coordinates of HF-3c optimized minima and trasition state structures for crystalline periodic models in <a href="https://www.moldraw.unito.it/_sgg/m1m1s43_1.htm">.mol</a> format, editable with <a href="http://www.moldraw.unito.it/">MOLDRAW</a>, computed using <a href="http://www.crystal.unito.it/">CRYSTAL17</a> computer code.</li> </ul>
Quantum mechanical double slit for molecular scattering
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SAMPL7 Blind Challenge : Quantum-Mechanical Prediction of Partition Coefficients and Acid Dissociation Constants for Small Drug-like Molecules
<p>Global minima structures of <em>N</em>-acyl sulfonamides and sulfonamides derivatives (B3LYP-D3/6-311+G(d,p)) in gas phase.</p>
Geometries for "X-ray Absorption Spectra for Aqueous Ammonia and Ammonium: Quantum Mechanical versus Molecular Mechanical Embedding Schemes"
<p>195 clusters of ammonia and ammonium in water, used in <em>"X-ray Absorption Spectra for Aqueous Ammonia and Ammonium: Quantum Mechanical versus Molecular Mechanical Embedding Schemes"</em></p> <p>The geometries were first used in <em>J. Am. Chem. Soc.</em> 2017, 139, 36, 12773–12783, and later in <em>J. Phys. Chem. Lett.</em> 2021, 12, 36, 8865–8871</p> <p> </p> <p> </p>
Mechanisms of Andreev reflection in quantum Hall graphene
<p>Source code and datasets for the manuscript "Mechanisms of Andreev reflection in quantum Hall graphene".</p>
Data for article "Quantum Correlations of Light from a Room-Temperature Mechanical Oscillator"
<p>Figures data, data processing code and sample fabrication details for article "Quantum Correlations of Light from a Room-Temperature Mechanical Oscillator", </p> <p>Phys. Rev. X <strong>7</strong>, 031055 – Published 26 September 2017</p>
Data for "Ab initio quantum-mechanical predictions of semiconducting photocathode materials"
<p>Input and output files of the calculations presented in the publication <em>"Ab initio quantum-mechanical predictions of semiconducting photocathode materials"</em>.</p> <p>The zip-archives contain the data relevant for subsections <em>3.1 Electronic structure</em>, <em>3.2 Optical Spectroscopy</em> and <em>3.3. Core-level Spectroscopy. </em>The aiida-archives (suffix <em>".aiida"</em>) contain the calculation and provenance details for the high-throughput workflow described in subsection <em>3.4 High-throughput material screening</em>.<br> </p>
Datasets for "Towards probing for hypercomplex quantum mechanics in a waveguide interferometer"
<p>━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━<br> DATA DESCRIPTION README FILE FOR<br> "Towards probing for hypercomplex quantum mechanics in a waveguide interferometer"</p> <p> Sebastian Gstir<br> ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</p> <p> 2021-09-07</p> <p><br> Table of Contents<br> ─────────────────</p> <p>1) Peres/Sorkin Measurements<br> 2) Interference Contrast</p> <p>With the given raw data, all figures and tables of our publication can be recreated. The data is saved in the Hierarchical Data Format (HDF5), which can be opened with various programms and programming languages like Mathematica and Python.</p> <p>The code used to process this data and perform the given shown simulations is not contained in this repository, but can be requested from the authors if required.</p> <p><br> 1) Peres/Sorkin Measurements<br> ══════════════════════════════</p> <p> The files '23degrees measurement.h5' and '30degrees measurement.h5' hold the measured raw data for a housing temperature of 23°C and 30°C, respectively (see chapter 4).<br> We measured the light passing through our chip for different shutter combinations and recorded multiple values for each combination. The set shutter combination is recorded in "/shuttercombination" as an integer (0 to 7), whose binary representation describes the state of each shutter ('0' ... close, '1' ... open). E.g. the shutter setting '3' is '011', which states that shutter A and B are open and shutter C is closed. The set of all eight settings is in this context called a cycle. As stated in the publication, we recorded multiple cycles with randomly ordered shutter settings.<br> For each shutter setting we recorded the temperature of the chip housing in "/temperature-housing" and multiple successive signals of the photodiode in "/PD signal", with the corresponding timestamps in "/time". Therefore, all datasets (PD signal, time, temperature housing and shutter setting) are three-dimensional. The first dimension is the number of shutter combinations, the second the index of measurements per combination and the third dimension is specific for each datatype (desribed by the 'unit'-attribute field of each dataset).<br> Product names of the used devices are stated in the corresponding attribute fields.<br> <br> In order to create the data given in our publication, like figure 2 and B1, we filtered the recorded data for shutter errors and outliers. The following gives the excluded cycle indices as a list sorted by shutter combination (from 0 to 7) for each measurement.<br> "23°C"-measurement: {{204, 222, 226}, Range[1, 80], {}, {}, {45, 65, 156, 226, 333, 366}, {121, 227, 341}, {46, 63, 252, 335, 376}, {}}<br> "30°C"-measurement: {{}, {186, 371}, {}, {47, 67, 149, 271, 335, 393}, {272, 444, 446}, {40, 424, 439}, {301, 410}, {51}}<br> If one cycle includes a shutter combination with an outlier or shutter error, we excluded the whole cycle.<br> <br> Note that in case of the "23°C"-measurement no housing temperature was logged, as for this specific measurement we logged the set temperature to investigate its stability. Therefore, the temperature stability given in our publication is calculated from the "30°C" measurement and an additional measurement at a housing temperature of 23°C.<br> <br> To convert the measured temperature in V to °C, we used the following specs of the used NTC:<br> R25 = 1E4, B25 = 3988, Ibias = 101.055931 1E-6</p> <p><br> 2) Interference Contrast<br> ══════════════════════════════</p> <p> The file 'interference contrast_21to35.h5' holds the measured raw data for determining the interference contrast of the setup as described in Appendix A.3.<br> This file has the same structure as described in 1) and for our analysis, we only used the thermalisation with the surrounding, which starts at cycle 27.</p> <p> </p>
Data from: Stationary quantum entanglement between a massive mechanical membrane and a low frequency LC circuit
<p>Source data for figures.</p>
Structuran and NMR Characterization of Hexamer and Octamer Foldamers in Chloroform and Water: A Molecular Dynamics and Quantum Mechanics Approach
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Animations for 'Self Interacting Wave Fields - A Physical wave basis for Quantum Mechanics'
<p>Three animations which accompany DOI 10.5281/zenodo.5807650</p>
Quantum Flagship benchmarks QIA/MPQ "reflection mechanism"
<p>Data and scripts for the benchmarks QIA.E.MPQ.REF.02a, QIA.E.MPQ.REF.04, QIA.E.MPQ.REF.08.</p>
Data from two dimensional electronic spectroscopy showing that photosynthesis tunes quantum-mechanical mixing of electronic and vibrational states to steer exciton energy transfer
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Data related to: MACHINE LEARNING AND QUANTUM MECHANICS APPROACH TO MORE CHEMICALLY-AWARE MOLECULAR DESCRIPTORS FOR MEDICINAL CHEMISTRY APPLICATIONS
<p>DEMIN VS QM ELECTRONIC POTENTIAL CORRELATIONS FOR THE N:= ATOM TYPE AND THE N1 ATOM TYPE</p> <p>dEmin versus H-bond basicity scale and H-bond acidity scale </p>
On the relation between the non-vacuous vacuum and quantum mechanics—A self-made double slits device
<p>The movie (Time-lapse at 4X with 0.12-second interval) shows the so-called interference pattern of particles with a self-made apparatus. Small disks with nail are driven into the wooden wall in staggered order forming a hexagonal network, a two-dimensional Galton board. Next, attach double-sided adhesive tapes to the center of the board to form a two slits system, each about 2 gaps wide separated by a distance of 3 gaps and to the bottom of the board to form a collection system. Then dropping small black beads from the top one by one, we can clearly see three distribution peaks rather than two, like interference patterns, which is in good coincidence with the theoretical prediction of this scheme.</p>
Coupling microwave photons to a mechanical resonator using quantum interference
<p>This contains the data and processing scripts used for the figures of the manuscript "Coupling microwave photons to a mechanical resonator using quantum interference"</p>
QuantumStinks: Quantum-Mechanical Properties for 3.5k Olfactory Molecules
<p>Quantitative Structure-Odor Relationships are critically important for studies related to the function of olfaction. Current literature datasets contain expert-labeled molecules but lack feature data. This paper introduces QuantumStinks, a quantum-mechanics augmented derivative of the Leffingwell dataset. QuantumStinks contains 3.5k structurally and chemically diverse molecules ranging from 2 to 30 heavy atoms (CNOS) and their corresponding 3D coordinates, total PBE0 energy, molecular dipole moment, and per-atom Hirshfeld charges, dipoles, and ratios. The authors demonstrate that Hirshfeld charges and ratios contain sufficient information to perform molecular classification by training a Message Passing Neural Network with <code>chemprop</code> to predict scent labels. The QuantumStinks dataset is freely available on Zenodo along with the authors' code, example models, and dataset generation workflow.</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.