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16 results for “Au(111)”
Dataset supporting the paper "Transmitting Stepwise Rotation among Three Molecule-Gear on the Au(111) Surface. J.Phys.Chem.Lett. 11 6892 (2020)"
<p>Dataset corresponding to figure 3 of the paper "Transmitting Stepwise Rotation among Three Molecule-Gear on the Au(111) Surface. J.Phys.Chem.Lett. 11 6892 (2020), DOI: <a href="https://doi.org/10.1021/acs.jpclett.0c01747">10.1021/acs.jpclett.0c01747</a>"</p> <p>List of files:<br> There are two folders corresponding to brominated and debrominated structures:</p> <ul> <li>.siesta files: STM simulated images in WsXM format (<a href="http://www.wsxm.eu/">http://www.wsxm.eu/</a>) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> </ul>
Dataset supporting the paper "Supramolecular chemistry based on 4-acetylbiphenyl on Au(111). Phys. Chem. Chem. Phys. 22: 15208 (2020)"
<p>Dataset corresponding to theoretical calculations in the paper "Supramolecular chemistry based on 4-acetylbiphenyl on Au(111)" Phys. Chem. Chem. Phys. 22: 15208 (2020), <a href="http://dx.doi.org/10.1039/d0cp01657h">http://dx.doi.org/10.1039/d0cp01657h</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <ul> <li>.siesta files: STM images in WsXM format (<a href="http://www.wsxm.eu/">http://www.wsxm.eu/</a>) simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>).</li> <li>CONTCAR and POSCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>)</li> <li>.agr: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
Dataset supporting the paper "Thioetherification of Br-Mercaptobiphenyl Molecules on Au(111). Nano Letters 23, 1350 (2023)"
<p>Dataset corresponding to theoretical calculations in the paper "Thioetherification of Br-Mercaptobiphenyl Molecules on Au(111). Nano Letters 23, 1350 (2023)" DOI: <a href="https://doi.org/10.1021/acs.nanolett.2c04619">https://doi.org/10.1021/acs.nanolett.2c04619</a></p> <p>List of files:</p> <p>Several folders corresponding to the figures of the paper. They contain:</p> <ul> <li>.dat files: STM images simulated using STMpw (<a href="https://doi.org/10.5281/zenodo.3581159">https://doi.org/10.5281/zenodo.3581159</a>). They can be processed with the programs and scripts in the Utils directory of STMpw.</li> <li>CONTCAR files: relaxed structures in VASP format. They can be visualized with VESTA (<a href="https://jp-minerals.org/vesta/en/">https://jp-minerals.org/vesta/en/</a>).</li> <li>.agr: grace files (<a href="https://plasma-gate.weizmann.ac.il/Grace/">https://plasma-gate.weizmann.ac.il/Grace/</a>).</li> </ul>
Data & Codes used in: Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)
<p>Here we provide the data, codes, and outline the procedure to reproduce the results presented in the paper "Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)" by E. Ventura-Macias, P. M. Martinez, Rubén Pérez, and J. G. Vilhena.</p> <p>The following sections contain a detailed description of the data and codes. At the end of this README, you will find instructions on how to generate the classical force-field parameters (Morse and Lennard-Jones) from the potential energy surfaces (PES) computed at the DFT level.</p> <p>The procedure is general and applies to any given pair of molecule and surface. The provided codes will allow you to swiftly generate the PES at the DFT level, fit the Lennard-Jones and Morse potentials, and test them in a LAMMPS MD simulation.</p> <p>For a thorough explanation of the procedure and the relevance of these results, please refer to the original publication (ARTICLE_DOI).</p> <p>If you find this helpful, please consider citing the article (ARTICLE_DOI).</p> <h2>Data structure</h2> <p>The data is organized in the following way:</p> <ol> <li> <p>DFT</p> <ul> <li>The equilibrium adsorption geometry of methanethiol (MTH), propanethiol (PTH), and cysteine (CYS) for the Au-mol configuration with PBE+DFT-D3.</li> <li>Potential Energy Surface (PES) computed at the DFT level (Figure 3 of the main manuscript).</li> <li>The scripts used to generate the PES.</li> </ul> </li> <li> <p>MD</p> <ul> <li>Fitting code and general instructions on how to use it.</li> <li>General input scripts used to generate MD data within LAMMPS.</li> </ul> </li> </ol> <h3>DFT Data</h3> <p>The DFT data in <code>DFT.zip</code> is organized in the following way:</p> <ul> <li> <p><code>DFT_PES/</code></p> <p>This folder contains the DFT potential energy surfaces (PES) for the interaction of the sulfur atom of methanethiolate (<code>mth</code>), propanethiolate (<code>pth</code>), and cysteine (<code>cys</code>) with the Au(111) surface and the necessary scripts to reproduce it.</p> <ul> <li> <p><code>results/</code></p> <p>The PES are given in one csv file per molecule named as <em><code>mol</code></em> + <code>_PES_S-Au111.csv</code>, where <em><code>mol</code></em> is the molecule name. Columns are as follows:</p> <table> <tbody> <tr> <td><strong>Label</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td><em>i</em></td> <td>calculation number</td> </tr> <tr> <td><em>site</em></td> <td>adsorption site</td> </tr> <tr> <td><em>z</em></td> <td>distance of the S atom to the surface</td> </tr> <tr> <td><em>deltaz</em></td> <td>distance difference from the minimum energy position of the S atom</td> </tr> <tr> <td><em>pbed3</em></td> <td>PBE+D3 binding energy</td> </tr> <tr> <td><em>pbe</em></td> <td>PBE component of the binding energy</td> </tr> <tr> <td><em>d3</em></td> <td>DFT-D3 component of the binding energy</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li> <ul> <li> <p><code>mth/</code> | <code>pth/</code> | <code>cys/</code></p> <p>Each folder contains the CONTCAR (VASP) file for the optimized geometry of the molecule adsorbed on the Au(111) surface with PBE+D3.</p> </li> <li> <p><code>setup_grid.py</code></p> <p>Python script to set up the POSCAR files for the PES calculations.</p> </li> <li> <p><code>read_results.py</code></p> <p>Python script to read the results of the PES calculations.</p> </li> <li> <p><code>sub_array.sh</code></p> <p>Bash script to submit the PES calculations to an SLURM-based cluster.</p> </li> <li> <p><code>INCAR</code> | <code>KPOINTS</code> | <code>surf.CONTCAR</code></p> <p>VASP input files for the PES calculations.</p> </li> </ul> </li> </ul> <h3>MD fitting</h3> <p>The MD data in MD.zip is are organized as:</p> <ul> <li> <p><code>Fitting/</code></p> <ul> <li><code>optimize_Morse.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of Morse potential.</p> <ul> <li><code>optimize_LJ.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of LJ potential.</p> </li> <li> <p><code>Histogram/</code></p> <ul> <li><code>in.test</code></li> </ul> <p>LAMMPS input script to extract an XY file for the position of the S atom in an NVT simulation.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>sheng.eam</code></li> </ul> <p>EAM potential file in case Au dynamics are wished to be included.</p> </li> <li> <p><code>Single_point_scan/</code></p> <ul> <li><code>in.scan</code></li> </ul> <p>LAMMPS input script to perform single-point energy scan of a given molecule.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>launch.sh</code></li> </ul> <p>Launches the single point scan.</p> <ul> <li><code>plot_scan.py</code></li> </ul> </li> <li> <p><code>Molecules</code></p> <p>LAMMPS sample geometries for the 3 molecules.</p> </li> </ul> <h2>Steps to reproduce the results</h2> <p>The PES calculations were performed using the VASP code and two Python scripts for setup and results parsing.</p> <h3>Requirements</h3> <ul> <li>VASP (tested with version 5.4.4) <ul> <li><code>PAW_PBE</code> pseudopotentials set version 5.4</li> </ul> </li> <li>Python3 with packages: <ul> <li>ASE (Atomic Simulation Environment)</li> <li>Numpy</li> <li>Pandas</li> <li>matplotlib (optional)</li> </ul> </li> </ul> <h3>Steps</h3> <p>Each molecule has its own directory with the necessary files to reproduce the results. The following steps are for the methanethiolate molecule (<code>mth</code>).</p> <ol> <li>Set up the grid of points for the PES calculations by running the <code>setup_grid.py</code> script. It will create a subfolder <code>run/</code> inside the molecule's directory with the POSCAR files for each point in the grid.</li> </ol> <blockquote> <p>python setup_grid.py mth</p> </blockquote> <ol> <li> <p>Create the corresponding <code>POTCAR</code> file and place it in the molecule's directory.</p> </li> <li> <p>Change the <code>sub_array.sh</code> script to match the number of calculations in the array numbers and the MOL variable.</p> </li> </ol> <pre><code>#SBATCH --array=1-number of calculations MOL=mth</code></pre> <ol> <li>Submit the calculations to a SLURM-based cluster by running the <code>sub_array.sh</code> script from the <code>DFT_PES</code> directory.</li> </ol> <blockquote> <p>sbatch sub_array.sh</p> </blockquote> <ol> <li>After the calculations are finished, run the <code>read_results.py</code> script to parse the results and generate the PES csv files. It takes the arguments <code>--surf</code> and <code>--mol</code> to specify the surface and molecule PBE+D3 and D3 total energies.</li> </ol> <blockquote> <p>python read_results.py mth --surf -128.5228 -17.7147 --mol -22.6026 -0.0083</p> </blockquote> <h3>Reference values for the PBE+D3 and D3 surface and molecule energies</h3> <table> <tbody> <tr> <td> </td> <td><strong>Surface</strong></td> <td> </td> <td><strong>Molecule</strong></td> <td> </td> </tr> <tr> <td><strong>Molecule</strong></td> <td><strong>PBE+D3<br></strong></td> <td><strong>D3</strong></td> <td><strong>PBE+D3</strong></td> <td><strong>D3</strong></td> </tr> <tr> <td>mth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-22.6026</td> <td>-0.0083</td> </tr> <tr> <td>pth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-55.8615</td> <td>-0.0086</td> </tr> <tr> <td>cys</td> <td>-128.5228</td> <td>-17.7147</td> <td>-74.0338</td> <td>-0.1611</td> </tr> </tbody> </table> <h3>Fitting procedure</h3> <p>Both the .xyz and .csv files should be located at the same folder as the script. Then, simply run the code (<code>mth</code> is used as an example):</p> <blockquote> <p>python optimize_Morse.py mth</p> </blockquote> <p>or</p> <blockquote> <p>python optimize_LJ.py mth</p> </blockquote> <p>The script will print the optimized parameters: [De, re, α] or [ϵ, σ] for Morse or LJ respectively. It will also plot a fitting plot and a birdview of the resulting PES.</p> <h3>Using the potential. Histogram example</h3> <p>The code will run for the optimized <code>mth</code> Morse parameters and extract a <code>occ.lammpstrj</code> containing the (x,y) positions of the S atom throughout the NVT simulation. Note that only Au-S interaction is included. Start as:</p> <blockquote> <p>lmp -in in.test</p> </blockquote> <p>This is easily adaptable to other routines or molecules and is thought to be a generic LAMMPS starting input.</p> <h3>Single-point energy scan</h3> <p>Go to the 2C) folder and launch the scan with:</p> <blockquote> <p>./launch.sh</p> </blockquote> <p>This will create a folder named <code>fine_scan</code> containing 128 folders. Each folder is assigned to an (x,y) position. Then, inside each folder, a single-point energy evaluation is performed at various Z heights around the absolute minima.</p> <p>The <code>in.scan</code> file should be modified accordingly with the appropriate potentials. It is set to perform the scan with the optimized Morse potential by default.</p> <p>The output is gathered in the <code>E_readout.dat</code> folder with the following structure (all energies in eV):</p> <table> <tbody> <tr> <th>Total Energy</th> <th>Intramolecular energy</th> <th>Au-mol vdW interaction energy</th> <th>Au-S interaction energy</th> </tr> </tbody> <tbody> <tr> <td>-133.62</td> <td>0.123112</td> <td>-0.2403</td> <td>-1.31765</td> </tr> <tr> <td>-133.721</td> <td>0.123112</td> <td>-0.28-401</td> <td>-1.37853</td> </tr> </tbody> </table> <p>Therefore, the total adsorption energy will be the sum of the last two columns.</p> <p>Energies are ordered in increasing Z for the same (x,y) point. That is, the first 12 lines correspond to 12 heights of the starting (x,y) coordinate, the next 12 lines to heights at the second (x,y) configuration and so on.</p> <p>The python script <code>plot_scan.py</code> may be used to plot the results. The <code>E_readout.dat</code> file and <code>.csv</code> must be in the same folder.</p> <blockquote> <p>python plot_scan.py mth</p> </blockquote>
Synchrotron bulk photoemission data from Au(111) and DFT calculations
<p>Extreme UV photoemission band mapping of Au(111) surface and bulk. Measurements were conducted at the SGM-3 beamline of the 3rd-generation ASTRID2 synchrotron radiation source in Aarhus, Denmark with a photon energy of 80 eV. The gold sample was purchased from MaTecK GmbH with a (111) surface. The sample temperature was maintained at 100 K during measurements.<br> <br> Experimental data are provided in ibw format (Igor Pro), HDF5, and TIFF format along with calibrated energy.</p> <p>Density functional theory calculations were carried out using GPAW 1.31 code using GLLBSC pseudopotentials. The valence electrons were described with plane-wave basis sets with a kinetic energy threshold of 600 eV and reciprocal space was sampled with a (12x12x1) mesh of k-points.</p>
Temperature-dependent structure formation in the wetting layer of the ionic liquid [C2C1Im][OTf] on Au(111)
<p>Set of STM images, DFT calculations and MD simulations of [EMIm][OTf] on Au(111) with temperature dependence.</p>
AFM-ice-Au(111)-bilayer
<p>Machine learning dataset of probe particle model CO-tip atomic force microscopy (AFM) simulation images of bilayer ice clusters on the Au(111) surface and the corresponding atomic structure files.</p><p>The dataset consists of a total of 1198 different structures which are divided into training, validation, and test sets as 958/72/168, and for each structure the simulations are performed 10 times with different randomized simulation parameters to yield a total of 11980 simulations. Each simulation consist of 15 images at different tip-sample distances at 0.1Å step.</p><p>The dataset is saved in a compressed .tar.gz archive. The decompressed archive has samples stored in the webdataset shard format. Each shard is a tar file with a number of samples. The tar files are named in the format `Ice-K-{param}_{set}_{shard}.tar`, where {param} number stands for the different sets of randomized simulation parameters, {set} stands for either train, validation, or test set, and {shard} is the shard number.</p><p>Each sample consists of a number of AFM simulation images and an xyz structure file. The image files are named in the format `{x}.{y}.png`, where {x} is the sample number, {y} is the index for the different height slices in the simulations. The height slices are numbered from 0 to 14, such that 0 is the farthest distance and 14 is the closest distance. Similarly, the corresponding xyz structure files are named `{x}.xyz`. The comment line (second line) in the xyz files has information about the parameters used for in the simulation for the sample.</p><p>The ice structures were obtained from a neural-network potential optimization, and subsequently the Hartree potentials for the structures were obtained through a density functional theory calculation using the optB86b-vdW density functional in Vienna Ab-initio Simulation Package. The AFM simulations utilize the Lennard-Jones force field for the Pauli repulsion and van der Waals interactions and tip convolution with the Hartree potential for the electrostatic interaction.</p>
AFM-ice-Au(111)-monolayer
<p>Machine learning dataset of probe particle model CO-tip atomic force microscopy (AFM) simulation images of monolayer ice clusters on the Au(111) surface and the corresponding atomic structure files.</p><p>The dataset consists of a total of 2050 different structures which are divided into training, validation, and test sets as 1640/122/288, and for each structure the simulations are performed 10 times with different randomized simulation parameters to yield a total of 20500 simulations. Each simulation consist of 15 images at different tip-sample distances at 0.1Å step.</p><p>The dataset is saved in a compressed .tar.gz archive. The decompressed archive has samples stored in the webdataset shard format. Each shard is a tar file with a number of samples. The tar files are named in the format `Ice-K-{param}_{set}_{shard}.tar`, where {param} number stands for the different sets of randomized simulation parameters, {set} stands for either train, validation, or test set, and {shard} is the shard number.</p><p>Each sample consists of a number of AFM simulation images and an xyz structure file. The image files are named in the format `{x}.{y}.png`, where {x} is the sample number, {y} is the index for the different height slices in the simulations. The height slices are numbered from 0 to 14, such that 0 is the farthest distance and 14 is the closest distance. Similarly, the corresponding xyz structure files are named `{x}.xyz`. The comment line (second line) in the xyz files has information about the parameters used for in the simulation for the sample.</p><p>The ice structures were obtained from a neural-network potential optimization, and subsequently the Hartree potentials for the structures were obtained through a density functional theory calculation using the optB86b-vdW density functional in Vienna Ab-initio Simulation Package. The AFM simulations utilize the Lennard-Jones force field for the Pauli repulsion and van der Waals interactions and tip convolution with the Hartree potential for the electrostatic interaction.</p>
AFM data of ice clusters on Cu(111) and Au(111) in paper "Structure discovery in Atomic Force Microscopy imaging of ice"
<p>Frequency shift CO-tip atomic force microscopy data of small ice clusters on Cu(111) and Au(111) surfaces as they appear in the paper "Structure discovery in Atomic Force Microscopy imaging of ice".</p><p>The data are saved in a compressed .tar.gz archive. The unpacked archive contains each experiment as a Numpy .npz file. Each file contains the measurement data as a 3D array in the key 'data' and the physical extent of the scan region in the x and y directions in Ånströms in the keys 'lengthX' and 'lengthY'.</p>
NNP-ice-Au(111)
<p>Nequip neural network potential for water molecules on Au(111).</p><p>The compressed folder contains the training configuration file ("gold.yaml"), the weights of the pre-trained and deployed model ("best_model_au_deployed.pth"), and the dataset used for training ("au_water_DFT_dataset.extxyz"). The dataset consists of positions, forces and energies from 1850 DFT single point calculations (carried out in VASP using the optB86b-vdW functional).</p><p>The model was trained on torch version 1.13.1+rocm5.1.1, and Nequip version 0.6.0</p><p> </p>
Interactions of the Ionic Liquid [C2C1Im][DCA] with Au(111) Electrodes: Interplay between Ion Adsorption, Electrode Structure and Stability
Open the record for dataset details and reuse information.
Videos of the evolution of the projected band structures of Hexagonal phosphorus monolayer on Au(111) surface during uniform adsorption
<p>Attached to this DOI are videos of the evolution of the band structure and the band structure projections during the uniform adsorption of the HexP-monolayer on the Au(111) surface.</p> <p>The HexP-monolayer starts at 5 angström above its relaxed position and ends at its final relaxed position on the Au(111) surface.</p> <p>In total, 19 interpolation structures have been created (both ends included).</p> <p>On the left is the unit-cell of the simulated structure, with phosphorus atoms in orange and gold atoms in yellow.</p> <p>Band structure calculations and projections have been done using the Quantum ESPRESSO framework.</p>
AiiDA archive of the study of electronic properties of HexP-Monolayer on Au(111) using Quantum ESPRESSO framework
Open the record for dataset details and reuse information.
Iron phthalocyanine on Au(111) is a "non-Landau" Fermi liquid
<p>Datasets (figure source, model definitions) for the manuscript "Iron phthalocyanine on Au(111) is a “non-Landau” Fermi liquid".</p>
Autonomous Scanning Probe Microscopy Investigations over WS2 and Au{111}
<p>Dataset for associated publication.</p> <p>Abstract:</p> <p>Point defect identification in two-dimensional materials enables an understanding of the local environment within a given system, where scanning probe microscopy that takes advantage of hyperspectral tunneling bias spectroscopy acquisition can both map and identify the atomic and electronic landscape. Here, dense spectroscopic volume is collected autonomously via Gaussian process regression, where convolutional neural networks are used in tandem for defect identification. Acquired data enable image segmentation across defect modes, and a workflow is provided for both machine-driven decision making during experimentation and the capability for user customization. Where scanning tunneling microscopy and spectroscopy can be nontrivial and time intensive, we provide a means towards autonomous experimentation for the benefit of both enhanced reproducibility and user-accessibility. Monolayer semiconductor is explored on tungsten disulfide sulfur vacancies to provide two-dimensional hyperspectral insight into available sulfur-substitution sites within a transition metal dichalcogenide (TMD), which is combined with spectral confirmation on the Au{111} herringbone reconstruction for both tip state verification and local fingerprinting. Overall, chalcogen vacancies, pristine TMD, Au face-centered cubic, and Au hexagonal close packed regions are examined and detected by machine learning methods.</p>
CO(v=2) scattering on Au(111) (TPD, TOF and TD*(v=1,2))
<p>TPD of CO from Au(111) and Au(332)</p> <p>TOF(v=1,2) raw data and TD*(v=1,2) scattered from Au(111)</p>
ScienceDex guides
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.