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ShareScore release 0.9.0
Dataset results
7 results for “Cu(111)”
Data to "Coverage- and temperature-induced self-metalation of tetraphenyltransdibenzoporphyrin on Cu(111) "
<p>Raw data, meta data, data evaluation and final figures to the corresponding publication.</p>
Modification of electronic surface states by graphene islands on Cu(111)
<p>Data set of raw scanning tunneling microscope images and spectroscopy of monolayer graphene grown on Cu(111).</p> <p>Related papers: </p> <p><a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.91.195425">Phys. Rev. B 91, 195425 (2015) - Modification of electronic surface states by graphene islands on Cu(111) (aps.org)</a></p> <p><a href="https://iopscience.iop.org/article/10.1088/0953-8984/28/3/034003/meta">Native defects in ultra-high vacuum grown graphene islands on Cu(1 1 1) - IOPscience</a></p>
AFM-ice-Cu(111)
<p>Machine learning dataset of probe particle model CO-tip atomic force microscopy (AFM) simulation images of ice clusters on the Cu(111) surface and the corresponding atomic structure files.</p><p>The dataset consists of a total of 1837 different structures which are divided into training, validation, and test sets as 1469/110/258, and for each structure the simulations are performed 10 times with different randomized simulation parameters to yield a total of 18370 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>
Scanning tunneling microscopy of pentacene on Cu(111)
<p>STM images and spectroscopy of pentacene molecules on Cu(111). Data were taken at 5K. Data format is CreaTec ca 2007. (unpublished)</p>
NNP-ice-Cu(111)
<p>Nequip neural network potential for water molecules on Cu(111).</p><p>The compressed folder contains the training configuration file ("copper.yaml"), the weights of the pre-trained and deployed model ("best_model_cu_deployed.pth"), and the dataset used for training ("cu_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>
Cu(111) oxidation
<a href="https://spectroscopyhub.com/measurements/data-collection/a6fcccc5-f9db-4c95-b16b-de4ba4cc93db">Cu(111) oxidation</a>
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