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Replication Data for: Fully guaranteed and computable error bounds on the energy for periodic Kohn-Sham equations with convex density functionals
No full textData for reproducibility of the numerical simulations of the research paper "Fully guaranteed and computable error bounds on the energy for periodic Kohn-Sham equations with convex density functionals". The .zip file contains everything needed to generate the plots shown in the paper, as well as the code to run your own simulations with the bounds derived in the paper, using the Julia software DFTK.jl.
You just have to extract the tar file from this repository to access the files and the associated README.md
Models and Prepared Datasets for Iterative Modeling of Two Heat Pumps
No full textPrepared datasets and models for iterative modeling of heat plumes in groundwater.
Models were trained with Iterative modeling.
File explanation:
1HP.zip
This zip file contains all prepared datapoints with a single heat pump. The input data fields are pressure, permeability, position of the heat pump, normalized distance to the heat pump, and temperature. Based on doi:darus-3650
2HP.zip
This zip file contains all prepared datapoints with two heat pumps. The input data fields are pressure, permeability, position of the heat pump, normalized distance to the heat pump, and temperature. Based on doi:darus-3652
unet_stand_f64_d5_k4_2500dp
This folder contains the model for the standard architecture. F stands for the number of initial features, d for the depth of the network, and k for the kernel size. The amount of datapoints the model was trained on is indicated by dp.
unet_para_f64_d5_k4_pcs_2500dp
This folder contains the model for the parallel architecture. F stands for the number of initial features, d for the depth of the network, and k for the kernel size. The amount of datapoints the model was trained on is indicated by dp.
unet_quad_f64_d5_k4_2500dp
This folder contains the model for the quadratic architecture. F stands for the number of initial features, d for the depth of the network, and k for the kernel size. The amount of datapoints the model was trained on is indicated by dp
Replication Data for: "The Role of Spacer Length in Macrocyclization Reactions Under Confinement"
No full textAll primary data files associated with this publication, experimental procedures, reaction conditions, and used analytical equipment can be found in detail in the experimental section or the paper's supporting information. Synthesized complexes (Mo1-Mo7) and macrocyclization were characterized via nuclear magnetic resonance (NMR) spectroscopy; the spectra can be found in the NMR folder. NMR Spectra are specified according to the publication number. Crystal data of novel complexes (Mo1, Mo4, and Mo6) is deposited in the Cambridge Structural Database (CSD) of the Cambridge Crystallographic Data Centre (CCDC). BET and ICP-OES data for heterogeneous catalysts are also uploaded
Replication data of Lotsch group for: "Postsynthetic Transformation of Imine- into Nitrone-Linked Covalent Organic Frameworks for Atmospheric Water Harvesting at Decreased Humidity"
No full textHerein, we report a facile postsynthetic linkage conversion method giving synthetic access to nitrone-linked covalent organic frameworks (COFs) from imine- and amine-linked COFs. The new two-dimensional (2D) nitrone-linked covalent organic frameworks, NO-PI-3-COF and NO-TTI-COF, are obtained with high crystallinity and large surface areas. Nitrone-modified pore channels induce condensation of water vapor at 20% lower humidity compared to their amine- or imine-linked precursor COFs. Thus, the topochemical transformation to nitrone linkages constitutes an attractive approach to postsynthetically fine-tune water adsorption properties in framework materials.
Open *.cif files with a visualization software (see http://ww1.iucr.org/iucr-top/cif/), *.raw files with WinXPOW, *.txt, *.csv, *.xyd and *.aif files with a text editor, *.sp files with PerkinElmer Spectrum software, *.mnova files with MestReNova, and *.pro files with TOPAS
Models and Prepared Datasets for the First Stage
No full textModels trained with Heat Plume Prediction
and datasets prepared with Heat Plume Prediction into reasonable format + normalization etc, used for training these models.
Last relevant git commit: 5d6c5eae5b00e438.
Based on raw data from doi:10.18419/darus-3649 and doi:10.18419/darus-3650
Replication Data for: "ddX: Polarizable Continuum Solvation from Small Molecules to Proteins"
No full textData for reproducibility of the numerical simulations of the research paper "ddX: Polarizable Continuum Solvation from Small Molecules to Proteins"
Replication Data for "Investigating the Long-Term Kinetics of Pd Nanoparticles Prepared from Microemulsions and the Lindlar Catalyst for Selective Hydrogenation of 3-Hexyn-1-ol"
No full textAll data files related to the publications, reaction conditions, and characterization equipment are discussed in detail in the publication. Kinetic behavior related to the Pd agglomerates, sintered Pd particles, Lindlar and BASF LF200 catalysts for the catalytic hydrogenation of 3-hexyn-1-ol can be found in the data sets. It includes the corresponding raw Excel file showing H2 pressure and temperature variation versus time in addition to the GC files, activity, and selectivity data sheets, and characterization results including elemental analysis, HAADF-STEM, SEM, XRD, and CO chemisorption. All Nomenclatures are set as referred to in the publication. This study showed that by sintering the Pd agglomerates produced via the microemulsion synthesis method, it is possible to reduce the overhydrogenation rate of the alkynols by sintering and fusion of the Pd species at elevated temperatures. It was tried at different temperatures (473, 623 and 723 K) and it was shown that at a rather similar kinetic behavior (99.99 % conversion and 85-89 % selectivity to cis-hexenol), the sintered Pd aggregates could stay selective despite a catalytic surface area about 7 times larger than that of the Pd agglomerates. This emphasizes the role of low-coordinated edge and corner sites on the final selectivity of the cis-alkenol product and demonstrates that thermal sintering reduces the number of non-selective sites without any need for toxic or organic doping agents or modifiers
GALÆXI Validation: Taylor-Green Vortex
No full text
This Dataset contains the test case definition and reference data for the Taylor-Green Vortex (TGV) test case which builds the validation test case in the GALÆXI Paper (Section 5).
Incompressible TGV (Ma=0.1) according to (Link):
J. DeBonis, Solutions of the Taylor–Green vortex problem using high-
resolution explicit finite difference methods, in: 51st AIAA Aerospace
Sciences Meeting, 2013, p. 382
Compressible TGV (Ma=1.25) according to (Link):
J.-B. Chapelier, D. J. Lusher, W. Van Noordt, C. Wenzel, T. Gibis,
P. Mossier, A. D. Beck, G. Lodato, C. Brehm, M. Ruggeri, C. Scalo,
N. Sandham, Comparison of high-order numerical methodologies for
the simulation of the supersonic taylor-green vortex flow, Physics of Fluids 2024; 36 (5): 055146.
Executable of FLEXI/GALÆXI can be built using the
build.py script:
python3 build.py ./build-folder ./userblock.txt
Note: Please ensure that all necessary dependencies of GALÆXI/FLEXI are available (including CUDA) and a Python3 environment is installed on the system
dolfinx_eqlb v1.0.0
No full textdolfinx_eqlb is an open source library, extending FEniCSx by local flux equilibration strategies. The resulting H(div) conforming fluxes can be used for the construction of adaptive finite element solvers for the Poisson problem [1], elasticity [2][3][4] or poro-elasticity [5][6].
The flux equilibration relies on so called patches, groups of all cells, connected with one node of the mesh. On each patch a constrained minimisation problem is solved [7]. In order to improve computational efficiency, a so called semi-explicit strategy [8][9] is also implemented. The solution procedure is thereby split into two steps: An explicit determination of an H(div) function, fulfilling the minimisation constraints, followed by an unconstrained minimisation on a reduced, patch-wise ansatz space. If equilibration is applied to elasticity - stress tensors have distinct symmetry properties - an additional constrained minimisation step, after the row wise reconstruction of the tensor [3][4] is implemented.
[1] Braess, D. and Schöberl, J.: Equilibrated Residual Error Estimator for Edge Elements (2008).
[2] Prager, W. and Synge, J. L.: Approximations in elasticity based on the concept of function space (1947).
[3] Bertrand et al.: Weakly symmetric stress equilibration and a posteriori error estimation for linear elasticity (2021).
[4] Bertrand et al.: Weakly symmetric stress equilibration for hyperelastic material models (2020).
[5] Riedlbeck et al.: Stress and flux reconstruction in Biot’s poro-elasticity problem with application to a posteriori error analysis (2017).
[6] Bertrand, F. and Starke, G.: A posteriori error estimates by weakly symmetric stress reconstruction for the Biot problem (2021).
[7] Ern, A. and Vohralik, M.: Polynomial-Degree-Robust A Posteriori Estimates in a Unified Setting for Conforming, Nonconforming, Discontinuous Galerkin, and Mixed Discretizations (2015).
[8] Cai, Z. and Zhang, S.: Robust equilibrated residual error estimator for diffusion problems: conforming elements (2012).<br
Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM)
No full textThe Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements (SAEM) dataset provides a comprehensive solution for addressing gaps in river discharge measurements by leveraging satellite altimetry. This dataset offers enhanced coverage for river discharge estimations by utilizing data from multiple satellite missions and integrating it with existing river gauge networks. It supports sustainable development and helps address complex water-related challenges exacerbated by climate change.
The first version of SAEM includes (1) height-based discharge estimates for 8,730 river gauges, covering approximately 88% of the total gauged discharge volume globally. These estimates demonstrate a median Kling-Gupta Efficiency (KGE) of 0.48, surpassing the performance of current global datasets. (2) Catalog of Virtual Stations (VSs): a catalog of VSs defined by specific criteria, including each station’s coordinates, associated satellite altimetry missions, distance to discharge gauges, and quality flags. (3) Altimetric Water Level Time Series: time series data of water levels from VSs that provide high-quality discharge estimates. The water level data are sourced from both existing Level-3 datasets and newly generated data within this study, including contributions from Hydroweb.Next, DAHITI, GRRATS, and HydroSat. Non-parametric quantile mapping functions: for VSs, which model the transformation of water level time series into discharge data using a Nonparametric Stochastic Quantile Mapping Function approach