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190 results for “Two-dimensional”
Dataset for: Adapting Explainable Machine Learning to Study Mechanical Properties of Two-Dimensional Hybrid Halide Perovskites
<p>This archive contains the in plane and out of plane Young's moduli (complete with respective VASP in and outputs) for 154 n=1 and 30 n>1 2D hybrid organic and inorganic perovskites. The data was used in the publication "Adapting Explainable Machine Learning to Study Mechanical Properties of Two-Dimensional Hybrid Halide Perovskites".</p> <p>Computational settings for the calculations were:</p> <p>Perdew-Burke-Ernzerhof (PBE) exchange-correlation with Tkatchenko-Scheffler (TS) van der Waals (vdW) corrections<br>Projector augmented-wave (PAW) method for the description of interactions between core and valence electrons.<br>A plane wave cutoff energy of 520 eV<br>A Γ-centered Monkhorst-Pack k-point mesh with a grid spacing of 2π × 0.040 Å−1 <br>Geometry optimizations were performed until energy and residual forces fell below 10−6 eV and 0.001 eV/ Å, respectively. <br><br></p>
Two-dimensional Numerical Simulations of Mixing under Ice Keels Data
<p>This deposition contains time series of relevant outputs from the ice keel simulations. These include:</p> <ol> <li>The buoyancy frequency squared (Nstar_sq)</li> <li>The spatially-average irreversible mixing rate (phi_d)</li> <li>The spatially-averaged dimensionless diapycnal diffusivity (K)</li> <li>The mixing depth (95% of mixing occurs above this depth) in meters (z_mix)</li> <li>The relative mixing depth (z_mix_rel). This differs from the previous quantity as it is relative to the keel depth. That is, if the mixing depth was the keel height then the relative mixing depth would be 0.</li> </ol> <p>The files are formatted to be imported as a dictionary into a Python file via the Json package. The keys are the simulation names. The key values are a tuple with the first value being the time series and the second being an array of times at which the respective values were recorded. All values are separated into upstream and downstream files.</p>
The Impact of Oxygen Surface Coverage and Carbidic Carbon on the Activity and Selectivity of Two-Dimensional Molybdenum Carbide (2D-Mo2C) in Fischer–Tropsch Synthesis
<p>Datasets categorized per figure and contain data in x,y format.</p> <p>for the DFT part:</p> <p>35 elementary steps were studied. Each step is marked RX_NEB_InitialState_FinalState, and corresponds to the neb calculation for the identification of the transition state. In each file, POSCAR_00 corresponds to the initial structure and POSCAR_09 to the final structure, in both cases after geometry optimization. In each state, a file with the vibration calculation for the calculation of the Gibbs Energy is included. </p> <p>The Gibbs energies of the initial, transition and final states in table format are provided in <em>Figure 4 - panel b - Gibbs Energies_Initial_Transition_Final_states_35_elementary_reactions</em></p> <p>The calculation of Gibbs energies of the gas phase molecules in the empty unit cell, used as reference states are provided in <em>Figure 4 - Reference_state_Gases_empty_unit_cell</em></p> <p>The computations for the comparison of the different sites (HMo, Hc, atop and bridge) are provided in <em>Table S6 - Comparison_adsorption_sites</em></p> <p>The computations on the model with a partial oxygen coverage (O.67 O ML) are provided in:</p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_C_CH_CCH</em></p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_CH3_H_CH4</em></p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_CO_C_O</em></p> <p><em>Figure S29 - mo2c-ctx-3x3_067OML_CO_O_CO2</em></p>
Giant vortex clusters in a two-dimensional quantum fluid: Data sets
<p>This data set collates the experimental and simulation data for the paper "Giant vortex clusters in a two-dimensional quantum fluid."</p> <p><strong>Database S1: Data_Excel_Sheet.xlsx </strong>contains the data shown in Figs. 1, 3, and 4 of the paper.</p> <p><strong>Database S2: Exp_Vortex_Location_Data.zip</strong> contains the experimental vortex positions.</p> <p><strong>Database S3: 2DGPE.zip</strong> contains the outputs of the 2D GPE simulations, along with the generating scripts.</p> <p><strong>Database S4: MonteCarlo_raw.zip</strong> contains the Monte Carlo outputs used to generate the shown in Fig. 1. </p> <p>Additional scripts to reproduce the MC data, GPE data, and reproduce the figures may be found at <a href="https://github.com/UQBEC/GiantVortices">https://github.com/UQBEC/GiantVortices</a>.</p> <p> </p>
Surrogate Modeling Benchmark - Two-dimensional heat diffusion model
<p>This dataset is related to the Two-dimensional heat diffusion model benchmark case. A detailed description of the benchmark case can be found on the public online community website UQWorld: <a href="https://uqworld.org/t/benchmark-case-two-dimensional-heat-diffusion-model/" target="_blank" rel="noopener">https://uqworld.org/t/benchmark-case-two-dimensional-heat-diffusion-model/</a>.</p> <p>The experimental designs include datasets with 400, 800, 1200, 1600, and 2000 samples, each generated using optimized maximin distance Latin Hypercube Sampling (LHS) with 1000 iterations. Each dataset is replicated 20 times. The validation set contains 100,000 samples generated by Monte Carlo simulation. Each dataset contains input samples and the corresponding computational model responses.</p> <h2>Description of the dataset file</h2> <p>The dataset file includes two variables:</p> <ul> <li><em>ExpDesigns</em>, and</li> <li><em>ValidationSet</em>.</li> </ul> <p>Both variables are Matlab structures with fields <em>X</em>, <em>Y</em>, and <em>nSamples</em>. Variable <em>ExpDesigns</em> is a non-scalar structure sized according to the number of experimental design groups. Each field of <em>X</em> for the i-th element of the struct array contains replicated datasets, forming a matrix of size [number of samples] x [dimensionality] x [number of replications]. Similarly, each field of Y for the i-th element contains replicated computational model responses that correspond to the experimental design of the same replication, sized [number of samples] x [number of model outputs] x [number of replications]. The same structure logic applies to the <em>ValidationSet</em> variable, except it contains only one dataset per benchmark case.</p> <p>The structure can be summarized as follows:</p> <ul> <li>ExpDesigns(i).X(j,k,l) <ul> <li>i: dataset group,</li> <li>j: sample index,</li> <li>k: variable index, and</li> <li>l: replication index.</li> </ul> </li> </ul> <ul> <li>ExpDesigns(i).Y(j,m,l) <ul> <li>i, j, l: same as above,</li> <li>m: computational model output index.</li> </ul> </li> </ul> <ul> <li>ValidationSet.X(j,k) <ul> <li>j, k: same as above.</li> </ul> </li> </ul> <ul> <li>ValidationSet.Y(j,m) <ul> <li>j, m: same as above.</li> </ul> </li> </ul> <h2>Description of benchmarked metamodel competitors</h2> <p>The selection of competitors was based on our experience with meta-modeling and includes various metamodel types: Polynomial Chaos Expansions (PCE), Polynomial Chaos Kriging (PCK), and Kriging. Given that each metamodel has many hyperparameters, we chose the most general settings to address different benchmark case difficulties, including dimensionality, nonlinearity, and non-monotonicity.</p> <p>For <strong>Polynomial Chaos Expansions (PCE)</strong>, we used a polynomial degree and q-norm adaptivity approach. This approach adaptively increases the maximum polynomial degree and truncation q-norm until the estimated leave-one-out error starts increasing. Maximum polynomial interaction terms were limited to 2 due to the memory requirements for large model dimensionality and large experimental designs. We tested three different solvers to calculate the PCE coefficients: Least Angle Regression (LARS), Orthogonal Matching Pursuit (OMP), and Subspace Pursuit (SP).</p> <p><strong>Polynomial Chaos Kriging (PCK)</strong> employs a sequential combination strategy of PCE and Kriging. PCE uses degree adaptivity with a fixed q-norm. The maximum number of interactions is again set to 2 with the LARS solver. Ordinary Kriging is applied using the Matérn-5/2 correlation family, ellipsoidal, and anisotropic correlation function. We used a hybrid genetic algorithm to optimize the hyperparameters.</p> <p>We benchmarked both linear and ordinary <strong>Kriging</strong>, including Matérn-5/2 and Gaussian correlation families and separable and ellipsoidal correlation, resulting in eight different Kriging competitors. The hyperparameters were calculated using a hybrid covariance matrix adaptation-evolution strategy optimization.</p> <p>For further details on the settings, please refer to the competitors.m file and UQLab user manuals:</p> <ul> <li>S. Marelli, N. Luethen, B. Sudret, <a href="https://www.uqlab.com/pce-user-manual">UQLab User Manual – Polynomial Chaos Expansions</a>, Report UQLab-V2.1-104, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>C. Lataniotis, D. Wicaksono, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/kriging-user-manual">UQLab User Manual – Kriging (Gaussian Process Modeling)</a>, Report UQLab-V2.1-105, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2024.</li> <li>R. Schoebi, S. Marelli, B. Sudret, <a href="https://www.uqlab.com/pck-user-manual">UQLab User Manual – Polynomial Chaos Kriging</a>, Report UQLab-V2.0-109, Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Switzerland, 2022.</li> </ul> <h2>Description of the results file</h2> <p>The results file contains one variable: <em>Metrics</em>. It is a Matlab structure with fields corresponding to each competitor (currently 12). Each competitor field contains data of type non-scalar struct array. The performance metrics included are RelMSE, RelRMSE, RelMAE, MAPE, Q2, and RelCVErr. Each field of Metrics.(CompetitorName) for the i-th element of the struct array contains metrics corresponding to the replicated dataset and the competitor, structured as follows:</p> <ul> <li>Metrics.(CompetitorName)(i).(MetricName)(l)<br> <ul> <li>i: dataset group,</li> <li>l: replication index.</li> </ul> </li> </ul> <p>The description of the performance measures (metrics) can be found here: <a href="https://uqworld.org/t/metamodel-performance-measures/" target="_blank" rel="noopener">https://uqworld.org/t/metamodel-performance-measures/</a>.</p> <h2>Additional files</h2> <p>We provide files in three languages (MATLAB, Python, and Julia) to showcase how to work with datasets, results, and their visualization. The files are called <em>working_with_datafiles.*</em> (the extension depends on the selected language).</p> <h2>Acknowledgment</h2> <p>This project was supported by the Open Research Data Program of the ETH Board under Grant number EPFL SCR0902285. The calculations were run on the Euler cluster of ETH Zürich using the MATLAB-based UQLab software developed at the Chair of Risk, Safety and Uncertainty Quantification of ETH Zürich.</p>
Effect of Organic Cation Size on Structural, Thermochromic, Dielectric and Photoluminescence Properties of Two-Dimensional Lead Iodide Perovskites with Extremally Reduced Dielectric Confinement
<p>Dataset for scientific publication entitled Effect of Organic Cation Size on Structural, Thermochromic, Dielectric and Photoluminescence Properties of Two-Dimensional Lead Iodide Perovskites with Extremally Reduced Dielectric Confinement. </p> <p>This research was supported by the National Science Center (Narodowe Centrum Nauki) in Poland under project No. 2020/38/A/ST3/00214. JKZ acknowledges support from Academia Iuvenum, Wroclaw University of Science and Technology.</p>
Instances for the two-dimensional heterogeneous vector bin packing problem
<p>The dataset consist of 56 instances for the two-dimensional heterogeneous vector bin packing problem.</p> <p>Six small instances with 10, 11, 12, 13,<br> 15 and 20 items, as well as 50 randomly generated<br> large instances were considered. Weights and<br> volumes of items were randomly uniformly chosen<br> integer values from [1;15] tons and [1;25]m<sup>3</sup>,<br> respectively. The set of large instances had 5 instances<br> with each of the following numbers of<br> items: 50, 70, 100, 120, 150, 200, 350, 500, 750<br> and 1000.</p> <p>File structure:</p> <p>- number of items</p> <p>- weights of items</p> <p>- volumes of items</p>
Replication Data for: Proton transport through nanoscale corrugations in two-dimensional crystals
<p>This dataset contains source data for Main Figures from "Proton transport through nanoscale corrugations in two-dimensional crystals, <i>Nature,</i> volume 620, pages 782–786" Data plotted as curves and histograms are provided in .xlsx files. AFM data are provided in both .txt and SPIP-compatible .asc file types. Filenames correspond to the figure labels and plot information as in the publication.</p>
Data sets for "On new two-dimensional UHF radar observations of equatorial spread F at the Jicamarca Radio Observatory" by Rodrigues et al.
<p>AMISR-14 data set used in the study entitled "On new two-dimensional UHF radar observations of equatorial spread F at the Jicamarca Radio Observatory" by Rodrigues et al. and published by Earth, Planets and Space, doi: 10.1186/s40623-023-01876-7.</p>
Fig. 8. A-D in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 8. A-D − Nomarski (leftmost lane), FITC-immunofluorescence images labeled with anti-α-tubulin monoclonal antibody and their magnified images (middle two lanes), and red fluorescence images (rightmost lane) stained with Acti-stain 555 phalloidin (detection for F-actin) of encysting cells of C. cucullus Nag-1. Each set of photomicrographs arranged in a horizontal row shows an identical cell except for Fig. 8C (FITC image, inset). A − Vegetative cell. B-D − Encysting cells of C. cucullus Nag-1 at 1.5 h (B), 3 h (C) and 3 days (D) after encystment induction. E − Nomarski image (left), red fluorescence images (middle) stained with Acti-stain 555 phalloidin, and a Nomarski image superimposed with a red fluorescence image obtained by Acti-stain 555 phalloidin staining (right) in encysting cells of C. cucullus Nag-1 at 3 h after encystment induction. F − Silver impregnation of a 3-day-aged cyst showing the basal structure of cilia. This photograph was reproduced from our previous work (Watoh et al. 2005, Fig. 9b). ant: anterior end, le: lepidosome, mu: mucus layer, ec/en: ectocyst layer lined with endocyst layer, m: plasma membrane. B − arrowheads: swollen tip of cilia. C − arrowhead: oral apparatus.
Fig. 6 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 6. Ca2+/overpopulation-stimulated in vivo phosphorylation of p43 (actin, identified by MS) during resting cyst formation of C. cucullus Nag-1, detected by biotinylated Phos-tag/ECL assays (A), and blots stained with CBB after the biotinylated Phos-tag/ECL detection (B). Figures above the photographs indicate time lapse after onset of encystment induction.
Fig. 5 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 5. Photomicrographs (Nomarski images) (A) and transmission electron micrographs (B) of C. cucullus Nag-1 after onset of encystment induction, showing resorption of cilia. (A) Vegetative cell at 0 h (A-1) and 2.5 h (A-2) after onset of encystment induction. (B) Encysting 3-h-aged cell (B-1) and 4-h-aged cell (B-2). ci: cilia, m: plasma membrane, ec: ectocyst layer, le: lepidosome. (B-2) a different electron micrograph of the same ultrathin section used in a previous paper (Funatani et al. 2010; Fig. 3).
Fig. 3 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 3. Immunoblotting assay using anti α-tubulin antibody showing total α-tubulin content during resting cyst formation of C. cucullus Nag-1. Figures above the photographs indicate time lapse after on- set of encystment induction.
Fig. 2 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 2. Changes of the amount of β-tubulin (p56) and its fragments (p37 and p19) contained in water-soluble fraction during resting cyst formation of C. cucullus Nag-1. Figures above the photographs indicate time lapse after onset of encystment induction.
Fig. 1. 2-D in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 1. 2-D PAGE showing an alteration of the water-soluble protein composition at 0 h–4 weeks after the onset of encystment induction of C. cucullus Nag-1. Arrowheads indicate the proteins (p56, p37, p19) whose amount uniquely and markedly changed during resting cyst formation. These proteins were identified as β-tubulin and its fragments by MS analysis (see Table 1).
Fig. 7 in Analysis of Water-Soluble Proteins by Two-Dimensional Electrophoresis in the Encystment Process of Colpoda cucullus Nag-1 and Cytoskeletal Dynamics
Fig. 7. Effects of 10 µM taxol (A) and 10 µM cytochalasin B (B) on Ca2+/overpopulation-mediated globulation of C. cucullus Nag-1 (A-1, B-1) and ciliary resorption (A-2, B-2). A-1, B-1 − The rate of encysting (rounded) cells was expressed as a percentage of the total number of tested cells (100 randomly selected cells). Open squares (negative control). The cells were suspended in 1 mM Tris-HCl (pH 7.2) solu- tion without inhibitors at low cell density (<2,000 cells/ml). Under this condition, encystment was hardly induced. Closed circles (positive control). The cells were suspended in an encystment-inducing medium [1 mM Tris-HCl (pH 7.2) and 0.1 mM CaCl2] without inhibitors at high cell density (> 30,000 cells/ml) (Ca2+/overpopulation stimulation). In this condition, the encystment was markedly induced. Open circles (experiment). The cells were suspended in an encystment-inducing medium containing taxol (Ta) or cytochalasin B (CB) at high cell density (> 30,000 cells/ml). Points and attached bars correspond to the means of 5 measurements (100 cells per measurement) obtained from different batches and standard errors, respectively. A-2, B-2 − Length of cilia at 2 h after onset of encystment induction in the presence or absence of taxol (Ta) or cytochalasin B (CB). In the negative control [Induced without 'Ta' (0 h) or Induced without 'CB' (0 h)], the cultured cells were collected, then suspended in encystment-inducing medium, and quickly fixed with 3.7% paraformaldehyde. In the positive control [Induced without 'Ta' (2 h) or Induced without 'CB' (2 h)], the cells were suspended for 2 h in an encystment-inducing medium without inhibitors at high cell density (> 30,000 cells/ml), and then fixed with 3.7% paraformaldehyde. In the experimental groups [Induced with 'Ta' (2 h) or Induced with 'CB' (2 h)], the cells were suspended for 2 h in an encystment-inducing medium containing inhibitors at high cell density (> 30,000 cells/ml), and then fixed with 3.7% paraformaldehyde. Columns and attached bars correspond to the means in 26 cells and standard errors, respectively.
Enhancing two-dimensional control via single-channel haptic feedback: A multi-dimensional encoding strategy
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Synthesis of Vinylene-Linked Two-Dimensional Conjugated Pol-ymers via the Horner-Wadsworth-Emmons Reaction
<p>DFTB+ // mio-0-1 optimised structures of 2D-PPQV1 and 2D-PPQV2. "Layer mismatch" structures were optimized with fixed unit cells.</p>
Out-of-plane ferroelectricity and robust magnetoelectricity in quasi two-dimensional materials
<p>Thin film ferroelectrics have been pursued for capacitive and nonvolatile memory devices. They rely on polarizations that are oriented in an out-of-plane direction to facilitate integration and addressability with CMOS architectures. The internal depolarization field, however, formed by surface charges can suppress the out-of-plane polarization in ultrathin ferroelectric films that could otherwise exhibit lower coercive fields and operate with lower power. Here we unveil stabilization of a polar longitudinal optical (LO) mode in the <em>n</em>=2 Ruddlesden–Popper family that produces out-of-plane ferroelectricity, persists under open-circuit boundary conditions, and is distinct from hyperferroelectricity. Our first-principles calculations show the stabilization of the LO mode is ubiquitous in chalcogenides and halides and relies on anharmonic trilinear mode coupling. We further show that the out-of-plane ferroelectricity can be predicted with a crystallographic tolerance factor, and we use these insights to design a room-temperature multiferroic with strong magnetoelectric coupling suitable for magneto-electric spin-orbit transistors. </p>
Datasets of VAMAS TWA2 Project 33: Chemical characterization of graphene related two-dimensional materials by XPS
<p>The datasets contain excel-files of the XPS raw data of the participants of the interlaboratory comparison. Additionally, the protocol is added.</p>
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OpenNeuro
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