en: The binding and release of ligands from their protein targets is central to fundamental biological processes as well as to drug discovery. Photopharmacology introduces chemical triggers that allow the changing of ligand affinities and thus biological activity by light. Insight into the molecular mechanisms of photopharmacology is largely missing because the relevant transitions during the light-triggered reaction cannot be resolved by conventional structural biology. Using time-resolved serial crystallography at a synchrotron and X-ray free-electron laser, we have captured the release of azo-combretastatin A4 and the resulting conformational changes in tubulin. Nine structural snapshots from 1 ns to 100 ms complemented by simulations show how cis-to-trans isomerization of the azobenzene bond leads to a switch in ligand affinity, opening of an exit channel, and collapse of the binding pocket upon ligand release. The resulting global backbone rearrangements are related to the action mechanism of tubulin-binding drugs against gout, cancer, and COVID-19.

en: Watching the release of a photopharmacological drug from tubulin using time-resolved serial crystallography: 10 microseconds after photoactivation.


$oid: OBSOLETE

en: Raw data from dataset C319_EPL1_cSAXS_273_S01394-S02309, obtained at the cSAXS beamline at the Swiss Light Source during beamtime 20190654 (e17964).

en: Non-destructive X-ray tomography of brain tissue ultrastructure. Supporting raw data. Dataset C319_EPL1_cSAXS_273_S01394-S02309.

approval_status: approved

en: The PSI Data Catalog is the central repository directory for scientific metadata generated at the Paul Scherrer Institut (PSI). Powered by SciCat—a community-driven, open-source metadata management framework co-developed by PSI—the catalog provides discoverability and standardized access pathways to petabyte-scale datasets produced across PSI’s large-scale research facilities, including the Swiss Light Source (SLS), the Swiss Spallation Neutron Source (SINQ), and the SwissFEL X-ray free-electron laser.

en: PSI Data Catalog

de: PSI Data Catalog

fr: PSI Data Catalog

it: PSI Data Catalog

political_level: other

state: active

type: organization

psi

capacity: public

de: PSI Open Data Provider

type: dataset

key: guid

key: uri

value: DCAT Harvester SciCat

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente


en: The associated article to this dataset describes a new genus and species, Renbernia zhoui E.M.Friis, P.R.Crane & K.R.Pedersen from the Early Cretaceous (early-middle Albian) mesofossil flora collected in Potomac Group sediments at the Puddledock locality, Virginia, U.S.A. The new taxon is based on microsporangiate structures with Decussosporites-type pollen in situ in densely arranged microsporangiophores. The microsporangia are elongate with extrorse and valvate dehiscence. Synchrotron radiation X-ray tomographic microscopy shows that they are bisporangiate. The in situ Decussosporites-type pollen is elliptical with an unusual arrangement of apertures. On the presumed distal face there is a long median colpus flanked laterally by two equally long lateral colpi. On the presumed proximal side there is a short transverse colpus perpendicular to the longitudinal colpi. Renbernia zhoui is compared to Brenneria potomacensis, another microsporangiate structure from the Potomac Group sediments with Decussosporites-type pollen in situ. B. potomacensis is slightly older than Renbernia zhoui and is distinguished by the more rounded microsporangia and the pollen is more distinctly saccate. A comparison with the extinct pollen genus Eucommiidites and a possible link of Renbernia zhoui to the Bennettitales-Erdtmanithecales-Gnetales group, which is diverse and common in the Puddledock mesofossil flora, is discussed.

en: Extinct seed plant diversity in the Early Cretaceous: An enigmatic new microsporangiate fossil with Decussosporites pollen in situ


en:
Circular dichroism observed by resonant x-ray diffraction at the Cu L3 edge was investigated in a chiral antiferromagnet, Pb(TiO)Cu4(PO4)4, which shows magnetic quadrupole order (TN = 7K). At temperatures
above TN, space-group-forbidden reflection 100 is observed due to the anisotropic tensor of susceptibility (ATS) scattering. With decreasing temperature, the reflection intensity shows substantial circular dichroism below
TN. We found one-to-one correspondence between the sign of the circular dichroism and that of the sample’s crystallographic chirality. In addition, the reflection intensity depends on the product of poling magnetic and
electric fields. The circular dichroism observed in this study is interpreted as the interference between the ATS and the magnetic scatterings. This finding shows that the interference scattering probes both the chirality and the
order parameter of the magnetic quadrupole order in this chiral antiferromagnetic.

en: Chirality and magnetic quadrupole order in Pb(TiO)Cu4(PO4)4 probed by interference scattering in resonant x-ray diffraction


en: Data to reproduce the experiments described in the paper: "Image2Reg: Linking Chromatin Images to Gene Regulation using Genetic perturbations screens" by Daniel Paysan, Adityanarayanan Radhakrishnan, G.V. Shivashankar and Caroline Uhler. The raw imaging data from Rohban et al. (2017) and from the Joint Undertaking in Morphological Profiling Cell Painting Consortium (Chandrasekaran et al., 2023) used in the study was obtained from IDR0033 available at "http://idr-demo.openmicroscopy.org/webclient/?show=screen-1751" , respectively from the Cell Painting Gallery on the Registry of Open Data on AWS "https://registry.opendata.aws/cellpainting-gallery/". Please note that these data resources need to be appropriately referenced if this dataset is used. Please refer to the above mentioned paper for a complete list of the used data sets.

en: Data for Image2Reg


en: Multiple genomic and proteomic studies have suggested that peripheral blood mononuclear cells (PBMCs) respond to tumor secretomes and thus could provide possible avenues for tumor prognosis and treatment evaluation. We hypothesized that the chromatin organization of PBMCs obtained from liquid biopsies, which integrates secretome signals with gene expression programs, provides efficient biomarkers to characterize tumor signals and the efficacy of proton therapy in tumor patients. In the paper associated to the enclosed data set, we show that chromatin imaging of PBMCs combined with machine learning methods provides such robust and predictive chromatin biomarkers. Our work shows that such chromatin biomarker enable the accurate diagnoses of different tumor types and monitoring of treatment effects of patients undergoing proton therapy and thus highlights their potential for accurate cancer diagnostics and therapy evaluation.

en: Imaging data related to the paper: "Imaging and AI based Chromatin Biomarkers for Diagnosis and Therapy Evaluation from Liquid Biopsies" by Kiran Challa* and Daniel Paysan*, Dominic Leiser, Nadia Sauder, Damien C. Weber and G.V. Shivashankar.


en: The controlled rotation of acoustically levitated samples is beneficial for analyzing sample properties, e.g., in a recently reported room temperature x-ray diffraction experiment, wherein thin film sample holders comprising thin film disks with short blades attached around their circumference were utilized. However, the mechanism of producing the torque and the determinant factor of the rotation direction for these planar ultrasonic rotors have been elusive. We, therefore, study the impact of the size and shape on the rotation characteristics of these ultrasonic rotors in air and further study the influence of the viscosity of fluid. Theory and experiment demonstrate the essential role of the short blades in producing the acoustic torque both in air and water. In the airborne case, the shape and arrangement of the blades are found to determine the rotation direction. In water, with a dynamic viscosity 55 times higher than that of air, we demonstrate that ultrasonic rotors down to 25-μm-disk-diameter function in an optimized experimental geometry with approximately the same actuation efficiency as in air. Our results will be beneficial to further improve the applicability of the ultrasonic rotors as sample holders for airborne experiments and to explore the micrometer-scale ultrasonic rotors in liquid.

en: Data for Size and shape dependent rotation characteristics of thin film ultrasonic rotors


en: In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original experimental sources.This dataset is intended for researchers in materials science and mechanical engineering, offering a robust foundation for training and testing deep learning algorithms in fatigue analysis. By making this dataset publicly available, we aim to foster collaboration and further advancements in the field of metal fatigue prediction. Researchers are encouraged to utilize and contribute to the dataset, thereby enhancing its scope and applicability.

en: A deep learning dataset for metal multiaxial fatigue life prediction

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

value: DCAT Harvester Materials Cloud

deep-learning

display_name: deep-learning

fatigue-life-prediction

display_name: fatigue-life-prediction

metal-fatigue

display_name: metal-fatigue

multiaxial-fatigue

display_name: multiaxial-fatigue


en: A transient state of the excess electron in liquid water preceding the development of the solvation shell, the so-called wet electron, has been invoked to explain spectroscopic observations, but its binding energy and atomic structure have remained highly elusive. Here, we carry out hybrid functional molecular dynamics to unveil the ultrafast solvation mechanism leading to the hydrated electron. In the pre-hydrated regime, the electron is found to repeatedly switch between a quasi-free electron state in the conduction band and a localized state with a binding energy of 0.26 eV, which we assign to the wet electron. This transient state self-traps in a region of the liquid which extends up to ∼4.5 Å and involves a severe disruption of the hydrogen-bond network. Our picture provides an unprecedented view on the nature of the wet electron, which is instrumental to understand the properties of this fundamental species in liquid water.

en: Picture of Wet Electron: A Localized Transient State in Liquid Water

first-principles

display_name: first-principles

hydrated-electron

display_name: hydrated-electron

liquid-water

display_name: liquid-water

molecular-dynamics

display_name: molecular-dynamics

wet-electron

display_name: wet-electron


en:
Project abstract: In this work, a theoretical framework is developed to explain and predict changes in the product distribution of the propene dimerization reaction, which yields a mixture of C6 olefin isomers, resulting from the use of different porous materials as catalysts. The MOF-74 class of materials has shown promise in catalyzing the dimerization of propene with high selectivity for valuable linear olefin products. We show that experimentally observed changes in the product distribution can be explained in terms of the contribution of the pores to the free energy of formation, which are directly computed using molecular simulation. Our model is used to screen a library of 118 existing and hypothetical MOF and zeolite structures to study how product distribution can be tuned by changing pore size, shape, and composition of porous materials. Using these molecular descriptors, catalyst properties are identified that increase the selective reaction of linear olefin isomers, which are valued as industrial feedstocks. A pore size commensurate with the size of the desired linear products enhances linear conversion by sterically hindering the branched isomers. Another promising feature is the presence of open metal sites, which interact with the olefin π-bond to provide favorable binding sites for the linear isomers.

About this entry: We provide the geometrical framework descriptors, linear conversions, and thermodynamic data for the 118 frameworks screened in this study, as well as thermodynamics of adsorption properties computed for 12 C6 olefin isomers in each framework. For details about the methods used to obtain these data, please see the corresponding paper (DOI: 10.1021/acscatal.7b00712). The data provided here was used to create the scatter plots in the original paper and can be used to find the product distribution and adsorption properties for all of the materials considered in the study.

en: Predicting Product Distribution of Propene Dimerization in Nanoporous Materials (Data Download)

envi

en: Environment

metalorganic-frameworks

display_name: metalorganic-frameworks

molecular-simulation

display_name: molecular-simulation

monte-carlo

display_name: monte-carlo

product-distribution

display_name: product-distribution

propene-dimerization

display_name: propene-dimerization

zeolites

display_name: zeolites


en: The leucine-lysine amphiphilic peptide LKα14 has been used to study fundamental driving forces in processes such as peptide-surface binding and biomineralization. Here, we employ molecular dynamics (MD) simulations in tandem with replica exchange metadynamics to probe the binding mechanism and thermodynamics of LKα14 on silica. We also investigate the effect that the nature of the silica surface – crystalline vs. amorphous, has on the binding properties and peptide-surface conformations. We find that water adsorbs differently on both surfaces; it forms a denser interfacial layer on the crystalline surface, compared to the amorphous surface. This causes the peptide to bind more strongly on the amorphous surface than the crystalline surface. Cluster analysis shows that the peptide adopts a helical conformation at both surfaces, with a greater distribution of states on the crystalline surface. Peptide binding is primarily through lysine interactions, in line with prior experimental results.

en: Amphiphilic Peptide Binding on Crystalline vs. Amorphous Silica from Molecular Dynamics Simulations

enhanced-sampling

display_name: enhanced-sampling

molecular-dynamics-simulations

display_name: molecular-dynamics-simulations

peptide-surface-interactions

display_name: peptide-surface-interactions


en: Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by covalent, hydrogen or van der Waals interactions - self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides an entirely new view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications.

en: Polygonal tessellations as predictive models of molecular monolayers

molecular-self-assembly

display_name: molecular-self-assembly

tesselation

display_name: tesselation


en: Weyl semimetals in three-dimensional crystals provide the paradigm example of topologically protected band nodes. It is usually taken for granted that a pair of colliding Weyl points annihilate whenever they carry opposite chiral charges. In stark contrast, here we report that Weyl points in systems symmetric under the composition of time-reversal with a π-rotation are characterized by a non-Abelian topological invariant. The topological charges of the Weyl points are transformed via braid phase factors which arise upon exchange inside symmetric planes of the reciprocal momentum space. We elucidate this process with an elementary two-dimensional tight-binding model implementable in cold-atoms setups and in photonic systems. In three dimensions, interplay of the non-Abelian topology with point-group symmetry is shown to enable topological phase transitions in which pairs of Weyl points may scatter or convert into nodal-line rings. By combining our theoretical arguments with first-principles calculations, we predict that Weyl points occurring near the Fermi level of zirconium telluride (ZrTe) carry non-trivial values of the non-Abelian charge, and that uniaxial compression strain drives a non-trivial conversion of the Weyl points into nodal lines.

en: Non-Abelian reciprocal braiding of Weyl points and its manifestation in ZrTe

epfl

display_name: epfl

non-abelian-braiding

display_name: non-abelian-braiding

snsf

display_name: snsf

taas

display_name: taas

weyl-semimetal

display_name: weyl-semimetal

zrte

display_name: zrte


en: The solar water splitting process assisted by semiconductor photocatalysts attracts growing research interests worldwide for the production of hydrogen as a clean and sustainable energy carrier. Due to their optical and electrical properties several oxynitride materials show great promise for the fabrication of efficient photocatalysts for solar water splitting. This study reports a comparative investigation of particle- and thin films-based photocatalysts using three different oxynitride materials. The absolute comparison of the photoelectrochemical activities favors the particle-based electrodes due to the better absorption properties and larger electrochemical surface area. However, thin films surpass the particle-based photoelectrodes due to their more suitable morphological features that improve the separation and mobility of the photo-generated charge carriers. Our analysis identifies what specific insights into the properties of materials can be achieved with the two complementary approaches.

en: Oxynitride thin films versus particle-based photoanodes: a comparative study for photoelectrochemical solar water splitting

marvel

display_name: marvel

oxynitride-photoanode

display_name: oxynitride-photoanode

pulsed-laser-deposition

display_name: pulsed-laser-deposition

solar-fuel

display_name: solar-fuel

solar-water-splitting

display_name: solar-water-splitting


en: Skutterudites are crystals with a cagelike structure that can be augmented with filler atoms ("rattlers"), usually leading to a reduction in thermal conductivity that can be exploited for thermoelectric applications. Here, we leverage the recently introduced Wigner formulation of thermal transport to elucidate the microscopic physics underlying heat conduction in skutterudites, showing that filler atoms can drive a crossover from the Boltzmann to the Wigner regimes of thermal transport, i.e., from particlelike conduction to wavelike tunneling. At temperatures where the thermoelectric efficiency of skutterudites is largest, wavelike tunneling can become comparable to particlelike propagation. We define a Boltzmann deviation descriptor able to differentiate the two regimes and relate the competition between the two mechanisms to the materials' chemistry, providing a design strategy to select rattlers and identify optimal compositions for thermoelectric applications.

en: Crossover from Boltzmann to Wigner thermal transport in thermoelectric skutterudites

phonons

display_name: phonons

thermal-conductivity

display_name: thermal-conductivity

thermoelectrics

display_name: thermoelectrics


en: We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on the known material Cr2Ge2Te6, using density functional theory (DFT) calculations and determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability.

en: Data-driven studies of magnetic two-dimensional materials

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

machine-learning

display_name: machine-learning

magnetic-materials

display_name: magnetic-materials

two-dimensional-materials

display_name: two-dimensional-materials


en: High Entropy Alloys (HEAs) are a new broad class of near-random solid solution alloys that can possess some impressive mechanical and physical properties including high stability against grain growth (i.e. low grain boundary (GB) mobility). Here, it is shown that an initially flat GB in an HEA can become spontaneously rough, driven by natural local compositional fluctuations. Roughening lowers the total GB energy and thus can inhibit migration. A parameter-free theoretical framework is developed to demonstrate the energetics and size scales of the roughening in terms of solute/GB interaction energies and GB disconnection energies. Above a critical level of solute/GB interactions, a planar GB is predicted to roughen down to the scale of the GB periodic unit. A similar theory for 1D GBs (minimum periodic length in one direction) is also developed since such geometries are common in atomistic simulations. Specific predictions are made for the symmetric tilt boundaries Σ17 [100] (530) and Σ5 [100] (310) in a model CoCuFeNi alloy and atomistic simulations demonstrate roughening consistent with the theory. Analysis of the stresses needed to drive migration shows how migration can be inhibited or enhanced, rationalizing variations in mobility of GBs in HEAs.

en: Theory of spontaneous grain boundary roughening in high entropy alloys

materialscloud

en: Materials Cloud

grain-boundaries

display_name: grain-boundaries

high-entropy-alloys

display_name: high-entropy-alloys

pinning

display_name: pinning

spontaneous-roughening

display_name: spontaneous-roughening


en:
The uploaded archive provides a ML-ready data set extracted from the juHemd database (see references) augmented with supplemental data for atomic descriptors. Descriptors provided in this data set include structural, magnetic, atomic quantities as well as derived (summed) quantities. In total, 118 possible descriptors are included of which 12 are DFT generated. For each simulation type (LDA/GGA) there is also a data set cleaned from DFT data available.

After data cleaning and preprocessing we extracted 387 LDA calculated magnetic Heusler structures as well as 408 GGA structures which have a full structural and magnetic data set. As we only aim at magnetic compounds, we chose to filter out compounds from the original JuHemd which have at least 0.1 Bohr magneton as total absolute magnetic moment. For each data file there is an existing descriptor file naming all the descriptors included in the data set.

en: ML-ready Curie temperatures and descriptors extracted from the JuHemd database

envi

en: Environment

critical-temperature

display_name: critical-temperature

curie-temperature

display_name: curie-temperature

density-functional-theory

display_name: density-functional-theory

electronic-structure

display_name: electronic-structure

heusler-alloy

display_name: heusler-alloy

judft

display_name: judft

kkr

display_name: kkr

magnetic

display_name: magnetic

magnetic-phase-transition

display_name: magnetic-phase-transition

magnetic-structure

display_name: magnetic-structure

max

display_name: max

monte-carlo

display_name: monte-carlo


en: We present evidence of a strong circular photon drag effect (PDE) in topological insulators (TIs) through the observation of threefold rotationally symmetric helicity-dependent topological photocurrents using THz spectroscopy in epitaxially-grown Bi₂Se₃ with reduced crystallographic twinning. We establish how twinned domains introduce competing nonlinear optical (NLO) responses inherent to the crystal structure that obscure geometry-sensitive optical processes through the introduction of a spurious mirror symmetry. Minimizing the twinning defect reveals strong NLO response currents whose magnitude and direction depend on the alignment of the excitation to the crystal axes and follow the threefold rotational symmetry of the crystal. Notably, photocurrents arising from helical light reverse direction for left/right circular polarizations and maintain a strong azimuthal dependence—a result uniquely attributable to the circular PDE, where the photon momentum acts as an applied in-plane field stationary in the laboratory frame. Our results demonstrate new levels of control over the magnitude and direction of photocurrents in TIs and that the study of single-domain films is crucial to reveal hidden phenomena that couple topological order and crystal symmetries.

en: Emergence of threefold symmetric helical photocurrents in epitaxial low twinned Bi₂Se₃

experimental

display_name: experimental

photon-drag-effect

display_name: photon-drag-effect

spectroscopy

display_name: spectroscopy

terahertz

display_name: terahertz

topological-materials

display_name: topological-materials


en: The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is computationally demanding, while simulations of small cells are often plagued with non-physical effects that are a consequence of the system's finite size. Therefore, one is often forced to use empirical models that describe the physics of the material in terms of effective interaction terms, that are fitted using the results from DFT, to perform simulations that do not suffer from finite size effects. In this study we use a machine-learning (ML) potential trained on DFT, in combination with accelerated sampling techniques, to converge the thermodynamic properties of Barium Titanate (BTO) with first-principles accuracy and a full atomistic description. Our results indicate that the predicted Curie temperature depends strongly on the choice of DFT functional and system size, due to the presence of emergent long-range directional correlations in the local dipole fluctuations. Our findings demonstrate how the combination of ML models and traditional bottom-up modeling allow one to investigate emergent phenomena with the accuracy of first-principles calculations and the large size and time scales afforded by empirical models.

en: Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

dielectric-correlations

display_name: dielectric-correlations

ferroelectrics

display_name: ferroelectrics

finite-size-effects

display_name: finite-size-effects

hybrid-dft-ml

display_name: hybrid-dft-ml

machine-learning-potentials

display_name: machine-learning-potentials

marvel

display_name: marvel

metadynamics

display_name: metadynamics

phase-transitions

display_name: phase-transitions

sinergia

display_name: sinergia

snsf

display_name: snsf


en:
Prediction of microstructure evolution during material processing is essential to control the material properties. Simulation tools for microstructure evolution prediction based on physical concepts are computationally expensive and time-consuming. Therefore, they are not practical when either there is an urgent need for microstructure morphology during the process, or there is a need to generate big microstructure datasets. Essentially, microstructure evolution prediction is a spatiotemporal sequence prediction problem, where the prediction of material microstructure is difficult due to different process histories and chemistry. We propose a Predictive Recurrent Neural Network (PredRNN) model for the microstructure prediction, which extends the inner-layer transition function of memory states in LSTMs to spatiotemporal memory flow. As a case study, we used a dataset from spinodal decomposition simulation of FeCrCo alloy created by the phase-field method for training and predicting future microstructures by previous observations. The results show that the trained network predicts quantitatively accurate microstructure morphologies while it is several orders of magnitude faster than the phase field method.
The trained model aims to generate future material microstructures by learning from the historical microstructures, where the visual dynamics are believed to have modular structures that can be learned with compositional subsystems. A PredRNN (https://github.com/thuml/predrnn-pytorch) model was trained to predict Fe-Cr-Co microstructures evolution during the spinodal decomposition.

en: Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network

phase-field-spinodal-decomposition

display_name: phase-field-spinodal-decomposition


en: In contrast to inorganic quantum wells, hybrid quantum wells (HQWs) based on metal-organic semiconductors are characterized by relatively soft lattices, in which excitonic states can strongly couple to lattice phonons. Therefore, understanding the lattice's impact on exciton dynamics is essential for harnessing the optoelectronic potential of HQWs. Beyond 2D metal halide perovskites, layered metal-organic chalcogenides (MOCs) are an air-stable, underexplored material class hosting room-temperature excitons that could be exploited as photodetectors, light emitting devices, and ultrafast photoswitches. Here, we elucidate the role of phonons in the optical transitions of the prototypical MOC [AgSePh]∞. Impulsive stimulated Raman scattering (ISRS) allows us to detect coherent exciton oscillations driven by Fröhlich interaction with low-energy optical phonons. Steady state absorption and Raman spectroscopies reveal a strong exciton-phonon coupling (Huang-Rhys parameter ~1.7) and its anharmonicity, manifested as non-trivial temperature-dependent Stokes shift. Our ab initio calculations support these observations, hinting at an anharmonic behavior of the low-energy phonons < 200 cm⁻¹. Our results untangle complex exciton-phonon interactions in MOCs, establishing an ideal testbed for room-temperature many-body phenomena.

en: Anharmonic exciton-phonon coupling in metal-organic chalcogenides hybrid quantum wells

exciton-phonon-coupling

display_name: exciton-phonon-coupling

metal-organic-chalcogenides

display_name: metal-organic-chalcogenides

raman-spectroscopy

display_name: raman-spectroscopy


en: Augmented plane wave methods enable an efficient description of atom-centered or localized features of the electronic density, circumventing high energy cutoffs and thus prohibitive computational costs of pure plane wave formulations. To complement existing implementations for ground-state properties and excitation energies, we present the extension of the Gaussian and augmented plane wave method to excited-state nuclear gradients within the Tamm–Dancoff approximation of time-dependent density functional theory and its implementation in the CP2K program package. Benchmarks for a test set of 35 small molecules demonstrate that maximum errors in the nuclear forces for excited states of singlet and triplet spin multiplicity are smaller than 0.1 eV/Å. The method is furthermore applied to the calculation of the zero-phonon line of defective hexagonal boron nitride. This spectral feature is reproduced with an error of 0.6 eV in comparison to GW–Bethe–Salpeter reference computations and 0.4 eV in comparison to experimental measurements. Accuracy assessments and applications thus demonstrate the potential use of the outlined developments for large-scale applications on excited-state properties of extended systems. The data in this record are supplementary data to the published article with DOI 10.1021/acs.jctc.4c00614.

en: Excited-state forces with the Gaussian and augmented plane wave method for the Tamm–Dancoff approximation of time-dependent density functional theory

augmented-plane-wave-methods

display_name: augmented-plane-wave-methods

gaussian-basis-sets

display_name: gaussian-basis-sets


en: In situ interfacial rheology and numerical simulations are used to investigate microgel monolayers in a wide range of packing fractions, ζ2D. The heterogeneous particle compressibility determines two flow regimes characterized by distinct master curves. To mimic the microgel architecture and reproduce experiments, an interaction potential combining a soft shoulder with the Hertzian model is introduced. In contrast to bulk conditions, the elastic moduli vary nonmonotonically with ζ2D at the interface, confirming long-sought predictions of reentrant behavior for Hertzian-like systems.

en: Interfacial fluid rheology of soft particles

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

elastic-properties

display_name: elastic-properties

hertzian-model

display_name: hertzian-model

liquid-liquid-interfaces

display_name: liquid-liquid-interfaces

microgels

display_name: microgels

softness-and-elasticity

display_name: softness-and-elasticity


en:
The discovery of chiral helical magnetism (CHM) in Cr1/3NbS2 and the stabilization of a chiral soliton lattice (CSL) has attracted considerable interest in view of their potential technological applications. However, there is an ongoing debate regarding whether the sister compound, Mn1/3NbS2, which shares the same crystal structure, exhibits similar nontrivial properties, which rely on the stabilization of the lack of inversion symmetry at the magnetic ion. In this study, we conduct a comprehensive investigation of the magnetically ordered states of both compounds, using 53Cr, 55Mn and 93Nb nuclear magnetic resonance.  Our results, supported by density functional  calculations, detect in a high quality single crystal of Cr1/3NbS2 all the signatures of the monoaxial CHM in  a magnetic field, identifying it as a textbook NMR case. The detailed understanding of this prototypic  behavior provides a reference for Mn1/3NbS2.
Despite the much larger density of specific defects in this second single crystal, we confirm the presence of a CHM phase in the Mn compound, characterized by a very large critical field for the forced ferromagnetic phase (~ 10 T for H || c).

en: Disorder-resilient transition of Helical to Conical ground states in M1/3NbS2, M=Cr,Mn

materialscloud

en: Materials Cloud

chiral-helical-magnetism

display_name: chiral-helical-magnetism

chiral-soliton-lattice

display_name: chiral-soliton-lattice

dft

display_name: dft

nuclear-magnetic-resonance

display_name: nuclear-magnetic-resonance


en: Kagome metals have emerged as pivotal materials in condensed matter physics due to their unique geometric arrangement and intriguing electronic properties. Understanding the origin of magnetism in these materials, particularly in iron rich Fe-Sn binary compounds like Fe₃Sn, holds a significant importance, as they represent potential candidates for permanent magnets with a high Curie temperature and a strong magnetic anisotropy. In the present study, we employ density-functional theory and dynamical mean-field theory to analyze the electronic structure and magnetic properties of Fe₃Sn. Our investigation reveals the presence of several nearly-flat bands and Weyl nodes at low excitation energies. The inclusion of local correlation effects is shown to push these features even closer to the Fermi energy, which may be important for their manipulation via external stimuli. Regarding magnetism, the Hubbard-like interaction leads to an increase of orbital polarization at the expenses of a minor reduction of the spin moment. The magnetic anisotropy energy exhibits a strong dependence on the particular choice of the Coulomb interaction parameters. Additionally, our detailed analysis of the interatomic exchange interactions indicates a significant contribution from the antisymmetric exchange, i.e. the Dzyaloshinskii-Moriya interaction, which showcases the existence of magnetic chirality in the system. Overall, our investigation highlights a strong interplay between the flat bands near the Fermi level, the local Coulomb interaction and the triangular geometry of the lattice, which plays a crucial role in driving the magnetic properties of this material.

en: Exploring strong electronic correlations in the breathing kagome metal Fe₃Sn

envi

en: Environment

dftdmft

display_name: dftdmft

dzyaloshinskii-moriya-interaction-dmi

display_name: dzyaloshinskii-moriya-interaction-dmi

electronic-structure

display_name: electronic-structure

flat-band

display_name: flat-band

kagome-metal

display_name: kagome-metal

magnetism

display_name: magnetism


en: Comprehending water dynamics is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab-initio molecular dynamics (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond timescales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamical properties of liquid water with ab-initio accuracy. Our findings reveal diversity in the calculated diffusion coefficient (D) and viscosity of water (η) across different methodologies. Specifically, while the GGA, meta-GGA, and hybrid functional methods struggle to predict dynamic properties under ambient conditions, whereas methods on the higher level of Jacob's ladder of DFT approximation perform significantly better. Intriguingly, we discovered that all D and η adhere to the established Stokes-Einstein (SE) relation for all the ab-initio water. The diversity observed among different methods can be attributed to distinct structural entropy, affirming the applicability of excess entropy scaling relations across all functionals. The correlation between D and η provides valuable insights for identifying the ideal temperature to accurately replicate liquid water's dynamic properties. Furthermore, our findings can validate the rationale behind employing artificially high temperatures in the simulation of water via AIMD. These outcomes not only pave the path toward designing better functionals for water but also underscore the significance of water's many-body characteristics.

en: The dynamic diversity and invariance of ab-initio water

aimd

display_name: aimd

density-functional-theory

display_name: density-functional-theory

diffusion

display_name: diffusion

dynamical-properties

display_name: dynamical-properties

machine-learning-potential

display_name: machine-learning-potential

water

display_name: water


en: The use of perovskite oxides as bifunctional catalysts for oxygen electrocatalysis is a promising strategy for developing high-performance unified regenerative fuel cells (URFCs), thanks to their highly tunable structure. This work explores the exsolution of well-dispersed nanoparticles containing reduced Pt species from La0.6Sr0.4Fe0.95Pt0.05O3-δ (LSFPt) as an effective approach to design bifunctional catalysts for oxygen electrocatalysis. A 10 h reduction treatment at 500 °C in 5% H2/Ar promotes the exsolution of ~1.2 nm Pt-containing nanoparticles on the surface, increasing oxygen deficiency without compromising the perovskite structure. The resulting catalyst exhibits significantly improved activity for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) in alkaline media, with over 2.5-fold performance improvement compared to the as-prepared LSFPt. Electrochemical impedance spectroscopy (EIS), combined with distribution of relaxation times (DRT) deconvolution analysis, revealed a notable decrease in medium-frequency resistance, indicating enhanced charge transfer at the electrode surface for OER and ORR. Post-mortem O K-edge spectra obtained by soft X-ray absorption spectroscopy in total electron yield mode further suggest an increased surface restructuring in the reduced LSFPt, attributed to the synergistic presence of surface defects and exsolved nanoparticles containing reduced Pt species. This one-step reduction protocol offers a promising strategy for designing efficient bifunctional electrocatalysts.

en: Harnessing bifunctional oxygen electrocatalysis: Exsolved Pt nanoparticles from La0.6Sr0.4Fe0.95Pt0.05O3-δ

bifunctional-electrocatalyst

display_name: bifunctional-electrocatalyst

distribution-of-relaxation-times-drt

display_name: distribution-of-relaxation-times-drt

oxygen-evolution-reaction-oer

display_name: oxygen-evolution-reaction-oer

oxygen-reduction-reaction-orr

display_name: oxygen-reduction-reaction-orr

perovskite

display_name: perovskite

pt-exsolution

display_name: pt-exsolution

unified-regenerative-fuel-cells

display_name: unified-regenerative-fuel-cells


en: Materials that efficiently promote the thermodynamically uphill water-splitting reaction under solar illumination are essential for generating carbon-free ("green") hydrogen. Mapping out the combinatorial space of potential photocatalysts for this reaction can be expedited using data-intensive materials exploration. The calculated band gaps and band alignments can serve as key indicators and metrics to computationally screen photoactive materials. Ternary main-group metal sulfides containing p- and s-block elements represent a promising, albeit underexplored, class of photocatalysts. Here, we computationally screen 86 candidate ternary main-group metal sulfides containing p- and s-block elements. By validating electronic structure predictions against experimental band gaps and band edges for synthetically accessible materials, we propose eight potential photocatalysts. Using computed Pourbaix diagrams, we further narrowed the candidate pool to four materials based on the predicted aqueous stability. We then synthesized and characterized these four materials and experimentally screened them for photoresponsiveness under photocatalytically relevant conditions. We also characterized their experimental band gaps and band edge positions and compared them with computational predictions. Based on the experimental screening protocols, we identify MgIn₂S₄ and BaSn₂S₅ as photoresponsive materials with sufficient aqueous stability to be considered in greater depth as potential photocatalysts for overall water-splitting. This record contains the computational predictions for the four candidates discussed in our manuscript.

en: Data-intensive exploration of the photoelectrochemical responses of main-group metal sulfides

photocatalysts

display_name: photocatalysts

sulfides

display_name: sulfides

water-splitting

display_name: water-splitting


en:
Phonon interactions from lattice anharmonicity govern thermal properties and heat transport in materials.These interactions are described by n-th order interatomic force constants (n-IFCs), which can be viewed as high-dimensional tensors correlating the motion of n atoms, or equivalently encoding n-phonon scattering processes in momentum space. Here, we introduce a tensor decomposition to efficiently compress n-IFCs for arbitrary order n. Using tensor learning, we find optimal low-rank approximations of n-IFCs by solving the resulting optimization problem. Our approach reveals the inherent low dimensionality of phonon-phonon interactions and allows compression of the 3 and 4-IFC tensors by factors of up to 10^3-10^4 while retaining high accuracy in calculations of phonon scattering rates and thermal conductivity. Calculations of thermal conductivity using the compressed n-IFCs achieve a speed-up by nearly three orders of magnitude with >98% accuracy relative to the reference uncompressed solution. These calculations include both 3- and 4-phonon scattering and are shown for a diverse range of materials (Si, HgTe, MgO, TiNiSn and monoclinic ZrO2).  In addition to accelerating state-of-the-art thermal transport calculations, the method shown here paves the way for modeling strongly anharmonic materials and higher-order phonon interactions.

This data contains the phonon scattering rates data with full and compressed phonon-phonon interactions. The data corresponds to Figure 3 in the paper and includes information on the phonon scattering rates for two materials: silicon and HgTe.

en: Phonon scattering rates data for silicon and HgTe

phonon-phonon-interactions

display_name: phonon-phonon-interactions

phonon-scattering-rate

display_name: phonon-scattering-rate

thermal-transport

display_name: thermal-transport


en: Charge carrier mobilities are critical parameters in halide perovskite solar cells, governing their average carrier velocity under an applied electric field and overall efficiency. Recent advances in first-principles calculations of electron-phonon interactions and carrier mobilities have enabled predictive computations for perovskite solar cells. However, the flexible octahedral frameworks and cationic displacements in these materials challenge the harmonic approximation, leading to significant difficulties in accurately calculating transport properties. To address these issues, we combine temperature-dependent effective potentials with the ab initio Boltzmann transport equations to compute carrier mobilities in a representative lead-free perovskite, CsSnBr₃. At room temperature, the electron/hole Hall mobilities in CsSnBr₃ are 106/256 cm²/Vs when neglecting anharmonic effects and 59/145 cm²/Vs when included. This overestimation of the harmonic approximation arises from the neglect of scattering coming from soft modes. We provide a workflow for performing first-principles carrier mobility calculations in anharmonic systems, advancing the predictive modeling of perovskite solar cells.

en: Impact of anharmonicity on the carrier mobility of the Pb-free CsSnBr₃ perovskite

ab-initio

display_name: ab-initio

anharmonicity

display_name: anharmonicity

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

mobility

display_name: mobility


en:
GapExtractor v2.0© is a free software designed to determine the band gap energy of non-translucent samples by analyzing their diffuse reflectance spectra. The analysis is based on the Tauc-method and the Kubelka and Munk formalism. The algorithm is coded in the Python 3.11.5 programming language. By coping, running or distributing this software, you indicate your acceptance of the license terms.

Steps to reproduce: Unzip GapExtractor v2.0 file; run gapextractor_v2.0.py; check the required phyton libraries; install if necessary.

en: GapExtractor v2.0

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

band-gap-energy

display_name: band-gap-energy

diffuse-reflectance-spectroscopy

display_name: diffuse-reflectance-spectroscopy

polyphasic-samples

display_name: polyphasic-samples

tauc-method

display_name: tauc-method


en: Gallium nitride (GaN) is a wide-bandgap semiconductor of significant interest for applications in solid-state lighting, power electronics, and radio-frequency amplifiers. An important limitation of this semiconductor is its low intrinsic hole mobility, which hinders the development of p-channel devices and the large-scale integration of GaN CMOS in next-generation electronics. Prior research has explored the use of strain to improve the hole mobility of GaN, but a systematic analysis of all possible strain conditions and their impact on the mobility is lacking. In this study, we introduce a piezomobility tensor notation to characterize the relationship between applied strain and hole mobility in GaN. To map the strain-dependence of the hole mobility, we solve the ab initio Boltzmann transport equation, accounting for electron-phonon scattering and GW quasiparticle energy corrections.

en: Design of high-mobility p-type GaN via the piezomobility tensor

materialscloud

en: Materials Cloud

hole-mobility

display_name: hole-mobility

nitrides

display_name: nitrides


en: ZnO nanoparticle powders are an important and common starting material for functional devices that require the highest purity for electronic and optical applications. In this combined ab initio and experimental study, we use electron paramagnetic resonance (EPR) spectroscopy to detect shallow donor states in the ZnO lattice via their characteristic EPR resonance at g = 1.96. Using DFT calculations that account for the chemical environment and the high temperatures during the gas-phase synthesis in conjunction with control experiments with atomic hydrogen, we show that these paramagnetic defects correspond to neutral hydrogenated oxygen sites (OHO0). As a key implication of these findings, ZnO particle powders, although produced under pure gas-phase synthesis conditions and subsequently annealed in vacuum to 873 K, were found to be doped with hydrogen at a base concentration of 10-5 at% and higher. This, in turn, highlights the often-overlooked role of unwanted hydrogen in nanoscale ZnO as components for applications in optics, electronics, and sensing.

en: Ubiquitous shallow trap states and lattice hydrogenation of ZnO particles

envi

en: Environment

defect

display_name: defect

hydrogen

display_name: hydrogen

zno

display_name: zno


en:
A mathematical parameter is proposed to describe the p-p-s electronic coupling strength in transition metal dichalcogenides (TMDCs). Two key descriptors, Iband(p−s)Iband(p−s) and Cband(p−p′)Cband(p−p′), are defined as follows:

Iband(p−s)=ϵp−ϵsϵp,Cband(p−p′)=ϵp′−ϵpϵp′Iband(p−s)=ϵpϵp−ϵs,Cband(p−p′)=ϵp′ϵp′−ϵp

where ϵϵ represents the p-band or s-band center positions of the atoms. Here, pp, p′p′, and ss refer to the p-band of the doped anion atom, the p-band of the first neighboring six S/Se atoms around the doped anion atom, and the s-band of Li atoms, respectively. Iband(p−s)Iband(p−s) characterizes the energy gap between the doped TMDCs and lithium polysulfides (LiPS), where a smaller value indicates a reduced band center energy gap, leading to enhanced interfacial electron transfer dynamics and a stronger interaction between the doped TMDCs and LiPS. On the other hand, Cband(p−p′)Cband(p−p′) quantifies the coupling strength between the p-band of the doped anion atoms and the neighboring S/Se atoms, where a smaller value indicates stronger coupling.

The relationship between the Gibbs energy barrier (ΔGΔG) and the coupling descriptors Iband(p−s)Iband(p−s) and Cband(p−p′)Cband(p−p′) is established through a machine learning approach, represented as:

ΔGi=f(Iband(i)(p−s),Cband(i)(p−p′))ΔGi=f(Iband(i)(p−s),Cband(i)(p−p′))

The model demonstrates that Iband(p−s)Iband(p−s) and Cband(p−p′)Cband(p−p′) effectively capture the relationship between the p-p-s coupling strength and the Gibbs free energy barrier, with a fitting result achieving an average Root Mean Square Error (RMSE) of approximately 0.07 eV.

en: A machine-learning (ML) assisted approach revealing p-p-s electronic coupling descriptor

catalytic-activity

display_name: catalytic-activity

electronic-coupling

display_name: electronic-coupling

li-s-batteries

display_name: li-s-batteries

machine-learning-prediction

display_name: machine-learning-prediction


en: The inner structure of polymeric microgels critically influences their responsiveness and potential applications, yet remaining challenging to resolve at molecular resolution. In this work, a structural characterization of thermoresponsive copolymer microgels is provided by integrating small-angle neutron scattering (SANS), dynamic light scattering (DLS), and nuclear magnetic resonance (NMR) measurements with multi-scale simulations. Specifically, Poly(N-isopropylacrylamide-co-N-isopropylmethacrylamide), P(NIPAM-co-NIPMAM), copolymer microgels, in which a random monomer distribution is conventionally assumed, are considered. By synthesizing different samples, including isotopically labeled microgels via selective deuteration, the microgels swelling behavior is probed and distinct polymer-specific signatures are revealed. To elucidate their internal distribution, monomer-resolved microgel simulations are performed across different copolymer models. A direct comparison between experimental and numerical form factors provides evidence of preferential organization into block structures, challenging the prevailing view of random distribution. 13C-NMR experiments confirm NIPAM-rich blocks and atomistic simulations link this unexpected block-like architecture to distinct local hydrogen-bonding patterns. This integrated approach provides the first direct evidence of preferential block formation in P(NIPAM-co-NIPMAM) microgels. Beyond this system, these results establish a generalizable strategy for unveiling hidden structural order in copolymer microgels, offering new strategies to tailor their design and to enhance control of material responsivity.

en: Thermoresponsive copolymer microgels synthesized via single-step precipitation polymerization: random or block structure?

copolymerization

display_name: copolymerization

microgels

display_name: microgels

molecular-modeling

display_name: molecular-modeling

nmr-spectroscopy

display_name: nmr-spectroscopy

selective-neutron-scattering

display_name: selective-neutron-scattering


en: Core-shell (CS) microgels–comprising a solid core chemically linked to a crosslinked polymer shell–are an important class of stimuli-responsive colloids widely employed for both fundamental and applied studies. Despite extensive experimental investigations, their numerical modeling remains underdeveloped; in particular, a detailed, temperature-resolved insight of their effective interactions is missing. While pairwise descriptions known for standard microgels are expected to be partially extensible to CS-microgels, the effect of the solid core and in turn, the extent and character of many-body contributions remain elusive. To clarify these open issues, this work introduces a computational method to generate realistic CS-microgels at different crosslinker concentrations, accurately reproducing the structure of experimental silica core–poly-N-isopropylacrylamide (pNIPAM) shell microgels at different temperatures across the Volume Phase Transition (VPT) and for different shell-to-core ratios. After validating the model, effective interactions between CS-microgels are calculated, extracting not only two-body interactions but also providing the first quantitative investigation of three-body effects in any microgel system. The present findings show that, albeit small, the latter contributions are non-negligible and exhibit an intriguing sign reversal: while being predominantly attractive at low temperatures, they become repulsive at high ones, nearly canceling out at the VPT. These results reveal a subtle interplay between microgel architecture, temperature and many-body correlations, with direct implications on the design of responsive soft materials and their mutual interactions.

en: Numerical investigation of realistic core-shell microgels: insights on two- and three-body effective interactions

coarse-grained-models

display_name: coarse-grained-models

core-shell-microgels

display_name: core-shell-microgels

effective-interaction

display_name: effective-interaction

many-body-effects

display_name: many-body-effects

molecular-simulation

display_name: molecular-simulation


en: The intrinsic stress in nanomultilayers (NMLs) is typically dominated by interface stress, which is particularly high in immiscible Cu/W NMLs. Here, atomistic simulations with a chemically-accurate neural network potential reveal the role of interfacial intermixing and metastable phase formation on the interface stress levels.  These results rationalize an experimentally-reported compressive-to-tensile transition as a function of NML deposition conditions and the extremely high interface stresses under some conditions.

en: Uncovering the origin of interface stress enhancement and compressive-to-tensile stress transition in immiscible nanomultilayers

cuw-nanomultilayers

display_name: cuw-nanomultilayers

interface-stress

display_name: interface-stress

intermixing

display_name: intermixing

metastable-phases

display_name: metastable-phases

molecular-statics

display_name: molecular-statics


en:
Molecular dynamics (MD) provides insights into atomic-scale processes by integrating over time the equations that describe the motion of atoms under the action of interatomic forces. Machine learning models have substantially accelerated MD by providing inexpensive predictions of the forces, but they remain constrained to minuscule time integration steps, which are required by the fast time scale of atomic motion. In this work, we propose FlashMD, a method to predict the evolution of positions and momenta over strides that are between one and two orders of magnitude longer than typical MD time steps. We incorporate considerations on the mathematical and physical properties of Hamiltonian dynamics in the architecture, generalize the approach to allow the simulation of any thermodynamic ensemble, and carefully assess the possible failure modes of such a long-stride MD approach. We validate FlashMD's accuracy in reproducing equilibrium and time-dependent properties, using both system-specific and general-purpose models, extending the ability of MD simulation to reach the long time scales needed to model microscopic processes of high scientific and technological relevance.

This repository contains the scripts used for the training and analysis of the FlashMD models presented in the associated paper/preprint.

en: FlashMD: long-stride, universal prediction of molecular dynamics

atomistic-machine-learning

display_name: atomistic-machine-learning

long-stride-simulations

display_name: long-stride-simulations

ml-accerelated-simulations

display_name: ml-accerelated-simulations

molecular-dynamics

display_name: molecular-dynamics


en:
Dual function materials (DFMs) enable reactive carbon capture (RCC), an intensified approach to carbon dioxide capture and utilization for cost and energy input reductions. Yet, there is a fundamental lack of understanding of mechanisms around CO2 adsorption and subsequent conversion on these materials, hindering further development. Herein, we investigated several supported alkaline metal oxides for their CO2 adsorption characteristics to find that Na/Al2O3 had the highest CO2 adsorption capacity, accompanied by a variety of CO2 adsorption geometries as identified by in situ DRIFTS and computational modeling. The addition of catalytic metals (Ru, Pd) increased the adsorption capacity of Na/Al2O3 without altering binding modes. In the subsequent reactive desorption step, acetate and formate intermediates were observed. Notably, this mechanistic investigation identified that the formation of acetate species was unique to RCC on a DFM, as these species were not observed in co-fed hydrogenation over the DFM or RCC over a Na-free catalyst.

Herein, we share the structural CONTCARs obtained using periodic PBE calculations in VASP for hydrated (110) and (100) surfaces of gamma-alumina, Na2O-modified surface models for Na/Al2O3, subsequent structures of various surface carbonate (CO3*) and bicarbonate (HCO3*) configurations formed on each of these surfaces upon CO2 adsorption and adsorbed acetate and formate radicals on the surfaces.

en: Identifying mechanistic differences between co-fed CO2 hydrogenation and reactive CO2 capture using Ru and Pd dual function materials

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

alumina

display_name: alumina

density-functional-theory

display_name: density-functional-theory

dfm

display_name: dfm

dft

display_name: dft

dual-function-material

display_name: dual-function-material

rcc

display_name: rcc

reactive-carbon-capture

display_name: reactive-carbon-capture


en: Hollow microgels are elastic polymer shells easily realizable in experiments. Recent works have shown the emergence of buckling events in dilute hollow microgels under the effect of an added osmotic pressure. Here, we perform large-scale simulations to show that these microgels at high enough packing fractions undergo spontaneous symmetry-breaking deformations ranging from single large dents to multiple indentations, even in the absence of any externally applied stress. This self-induced buckling phenomenon is thus solely driven by interparticle crowding. We construct a phase diagram inspired by vesicle shape theories, mapping local curvature metrics as a function of the reduced volume, to quantify these findings, and we also propose ways to observe the occurrence of buckling in experiments. The present results thus rationalize the deformations occurring in suspensions of micro- and nanoscale elastic shells, offering a synthetic analogue to biological ones and allowing direct control on buckling instabilities for potential applications. Beyond materials design, these insights may also help to describe shape regulation in natural systems such as cells and vesicles, where similar deformations are observed.

en: Self-induced buckling in hollow microgels

materialscloud

en: Materials Cloud

buckling-instability

display_name: buckling-instability

microgels

display_name: microgels

monomer-resolved-simulations

display_name: monomer-resolved-simulations


en:
The past few years have seen the development of ``universal'' machine-learning interatomic potentials (uMLIPs) capable of approximating the ground-state potential energy surface across a wide range of chemical structures and compositions with reasonable accuracy. While these models differ in the architecture and the dataset used, they share the ability to compress a staggering amount of chemical information into descriptive latent features. Herein, we systematically analyze what the different uMLIPs have learned by quantitatively assessing the relative information content of their latent features with feature reconstruction errors as metrics, and observing how the trends are affected by the choice of training set and training protocol. We find that the uMLIPs encode chemical space in significantly distinct ways, with substantial cross-model feature reconstruction errors. When variants of the same model architecture are considered, trends become dependent on the dataset, target, and training protocol of choice. We also observe that fine-tuning of a uMLIP retains a strong pre-training bias in the latent features. Finally, we discuss how atom-level features, which are directly output by MLIPs, can be compressed into global structure-level features via concatenation of progressive cumulants, each adding significantly new information about the variability across the atomic environments within a given system.

This repository contains the scripts to reproduce results presented in the associated paper/preprint.

en: Comparing the latent features of universal machine-learning interatomic potentials

envi

en: Environment

feature-comparison

display_name: feature-comparison

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning


en: Topological effects arising from the Berry curvature lead to intriguing transport signatures in quantum materials. Two such phenomena are the chiral anomaly and nonlinear Hall effect (NLHE). A unified description of these transport regimes requires a quantitative treatment of both band topology and electron scattering. Here, we show accurate predictions of the magnetoresistance in topological semimetals and NLHE in noncentrosymmetric materials by solving the Boltzmann transport equation (BTE) with electron-phonon (e-ph) scattering and Berry curvature computed from first principles. We apply our method to study magnetotransport in a prototypical Weyl semimetal, TaAs, and the NLHE in strained monolayer WSe2, bilayer WTe2 and bulk BaMnSb2. In TaAs, we find a chiral contribution to the magnetoconductance which is positive and increases with magnetic field, consistent with experiments. We show that e-ph interactions can significantly modify the Berry curvature dipole and its dependence on temperature and Fermi level, highlighting the interplay of band topology and electronic interactions in nonlinear transport. The computed nonlinear Hall response in BaMnSb2 is in agreement with experiments. By adding the Berry curvature to first-principles transport calculations, our work advances the quantitative analysis of a wide range of linear and nonlinear transport phenomena in quantum materials.

en: Magnetotransport in topological materials and nonlinear Hall effect via first-principles electronic interactions and band topology

berry-curvature

display_name: berry-curvature

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

magnetotransport

display_name: magnetotransport

nonlinear-hall-effect

display_name: nonlinear-hall-effect

topological-materials

display_name: topological-materials


en:
We use the self-consistent harmonic approximation (SSCHA) with machine learning interatomic potentials to calculate the effect of 18O substitution on the properties of quantum paraelectric SrTiO3 (STO). We find that calculations including both quantum and anharmonic effects are able to reproduce the experimentally observed isotope effect, in which the replacement of 16O by 18O induces the ferroelectric state, and we demonstrate that the ferroelectric phase transition in ST18O can be reproduced in a purely displacive description. We calculate the ferroelectric soft mode frequency as a function of volume, lattice parameters, and temperature for ST16O and ST18O and find that the phase space in which ST16O shows quantumparaelectric behavior, while ST18O becomes ferroelectric, is narrow. Our Letter shows that machine learning interatomic potentials enable temperature-dependent simulations that include quantum and anharmonic phonon effects and that quantitative prediction of the phase diagram in the case of STO is limited by the accuracy of the underlying electronic structure method.

This dataset contains the inputs/and outputs from the SSCHA calculations.

en: Machine-learning-enabled ab initio study of quantum phase transitions in SrTiO3

ferroelectric

display_name: ferroelectric

isotope-effect

display_name: isotope-effect

quantum-paraelectric

display_name: quantum-paraelectric


en:
The nudged elastic band (NEB) method is the standard approach for finding minimum energy paths and transition states on potential energy surfaces. Practical NEB calculations require several pre-processing steps: endpoint minimization, structural alignment, and initial path generation. These steps are typically handled by ad-hoc scripts or manual intervention, introducing errors and hindering reproducibility. We present a fully automated, open-source Snakemake workflow for small gas phase molecules that couples modern machine learning potentials (PET-MAD) to the eOn saddle point search software. Each step of the calculation lifecycle is encoded as an explicit dependency graph, from model retrieval and endpoint preparation through path initialization and band optimization. The workflow resolves all software dependencies from conda-forge, ensuring identical execution across platforms. Validation on the HCN to HNC isomerization demonstrates that the automated pipeline recovers the known single-barrier energy profile and product energy without manual intervention.

This record contains the data needed to "rehydrate" the Github repository to the point of submission including the models and results.

en: Reproducible orchestration of best practices for reaction path optimization with the nudged elastic band

computational-chemistry

display_name: computational-chemistry

nudged-elastic-band

display_name: nudged-elastic-band

transition-state

display_name: transition-state


en: This record contains the computational data associated with the manuscript “Distinct O2 activation regimes and an activation–removal trade-off at 3d-metal-doped MoS2 sulfur vacancies” by Park and Kim. The dataset includes optimized atomic structures, representative VASP input templates, and curated tabulated data supporting the reported adsorption energies, structural parameters, electronic descriptors, and kinetic benchmarks for Fe-, Co-, Ni-, and Cu-doped MoS2 sulfur vacancies. The data are intended to support reproducibility of the figures and tables in the manuscript and the Supporting Information. Detailed descriptions of the individual files and folders are provided in the accompanying README.txt file.

en: Computational dataset for distinct O2 activation regimes and an activation–removal trade-off at 3d-metal-doped MoS2 sulfur vacancies

cohp

display_name: cohp

density-functional-theory

display_name: density-functional-theory

electronic-structure-descriptors

display_name: electronic-structure-descriptors

oxygen-activation

display_name: oxygen-activation

single-atom-catalyst

display_name: single-atom-catalyst

strain-engineering

display_name: strain-engineering

sulfur-vacancy

display_name: sulfur-vacancy

transition-metal-doping

display_name: transition-metal-doping


en: These data characterize the activation processes of CoSn(OH)6-based electrocatalysts for the oxygen evolution reaction in alkaline conditions. Crystalline (CoSn25), crystalline defective (CoSn150) and amorphous (CoSn300) catalysts with identical metal compositions from the same precursor batch were considered. The operando X-ray diffraciton and X-ray abosorption spectroscopy indicate that crystalline and amorphous catalysts take different reconstruction pathways under the reaction conditions. Co L-edge total electron yield data indicate that a metal-ligand charge transfer surface state is formed during electrochemical activation that correlates well with the observed electrochemical activity.

en: Characteristic data of the CoSn(OH)6 alkaline electrochemical activation processes.

electrocatalysis

display_name: electrocatalysis

oer

display_name: oer

operando

display_name: operando

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

synchrotron

display_name: synchrotron

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy

x-ray-diffraction

display_name: x-ray-diffraction


en: This dataset contains structural minima, saddle-point structures, and transformation pathways of magic-size gold nanoclusters. Gold nanoclusters exhibit a complex energy landscape characterized by competing structural motifs and narrow energy gaps, which facilitate structural coexistence and rapid interconversion. In this study, we employ a high-accuracy machine-learned potential, trained on 20,000 density functional theory reference points, to investigate the structural transformation pathways of Au55, Au147, Au309, and Au561 nanoclusters. Our analysis encompasses transitions between cuboctahedral, Ino's decahedral, and icosahedral geometries. Saddle point searches reveal that high-symmetry transformations are governed by "jitterbug"-type and slip-dislocation motions. Furthermore, we identify lower-barrier, asymmetric pathways that facilitate the transition into disordered, Jahn–Teller-stabilized distorted icosahedra. Using Minima Hopping sampling, we uncover a variety of low-symmetry minima, including new global minima for Au309 and Au561 that are up to 2.8 eV lower in energy than previously reported structures. In contrast to prior computational results, our predicted transformation pathways are in qualitative agreement with experimental observations, suggesting that these newly identified disordered motifs are central to the structural evolution of gold nanoclusters.

en: The transformation mechanisms among cuboctahedra, Ino's decahedra and icosahedra structures of magic-size gold nanoclusters

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

dft

display_name: dft

machine-learning

display_name: machine-learning

nanoclusters

display_name: nanoclusters


en: This dataset provides the computational data supporting the manuscript entitled "Molybdenum Oxidation State Governs Brønsted–Lewis Acid Synergy in Dimethyl Oxalate Transesterification" by Li et al., providing all computational data underlying the reported adsorption behaviors on MoO3(010) and MoO2(011) surfaces. It includes optimized bulk and surface structures, representative CP2K input/output files, and post-processed electron density data, enabling reproducibility of the adsorption energies and electron density difference maps for dimethyl oxalate (DMO) and ethanol. The calculations reveal that DMO adsorbs more strongly on MoO3(010) (−0.9 eV) than on MoO2(011) (−0.7 eV), reflecting more facile Lewis acid activation by the Mo6+-dominated surface. Electron density accumulates between the surface Mo and the carbonyl O atom, while the carbonyl C loses density, indicating polarization of the molecule upon adsorption. Ethanol adsorption shows even more pronounced differences: on MoO3(010), it interacts either with a surface Lewis acidic Mo atom or a Brønsted acidic Mo–OH site, both configurations exhibiting similar adsorption energies of approximately −1.1 eV. Adsorption on the Lewis acid site induces electron accumulation at the Mo–O bond and activation of the hydroxyl H, whereas adsorption on the Brønsted acid site increases the overall electronegativity of ethanol, enhancing its nucleophilicity. In contrast, ethanol binds weakly (−0.1 eV) to MoO2(011) via a hydrogen bond with a surface O atom, with negligible interaction with Mo sites. All data are organized in a comprehensive folder structure covering bulk crystal models, isolated molecules, surface optimizations, and adsorption energy evaluations, with hydroxylated and non-hydroxylated MoO3 surfaces included. Computational settings employ CP2K (GPW) with PBEsol for structural optimizations and BEEF-vdW for energy calculations, along with specified basis sets, k-point sampling, and slab models. The dataset enables verification of all figures and tables in the manuscript and facilitates detailed analysis of Brønsted–Lewis acid synergy in MoOx-catalyzed transesterification.

en: Data for the adsorption properties of dimethyl oxalate and ethanol on the MoO3(010) and MoO2(011) surfaces

materialscloud

en: Materials Cloud

adsorption

display_name: adsorption

dimethyl-oxalate

display_name: dimethyl-oxalate

electron-density-difference

display_name: electron-density-difference

moox

display_name: moox


en: Van der Waals (vdW) interactions play a crucial role in the formation of various systems. Two-dimensional (2D) metals have attracted considerable attention due to their unique properties, particularly in applications where vdW interactions play a significant role.  In particular, a graphene template facilitates the self-assembly of 2D metal nanosheets. A study of vdW interactions between copperene and graphene has been carried out. The behavior of the binding energy of copperene/graphene is studied within the framework of DFT-D3.Collection of data, input and output files. Calculations were performed using Quantum ESPRESSO.

en: Long-range van der Waals forces between graphene and copperene: first principles calculations

envi

en: Environment

input-files

display_name: input-files

output-files

display_name: output-files

quantum-espresso

display_name: quantum-espresso


en: Rutile Sn1−x Gex O2 alloys are promising materials for high-power electronic applications due to their dopability and tunable ultra-wide band gaps. We use first-principles density functional theory and statistical mechanics to investigate the crystallographic, electronic, and thermodynamic properties of rutile Sn1−x Gex O2 alloys. We predict that the lattice parameters follow Vegard’s law, while band gaps calculated with the hybrid HSE06 functional exhibit strong bowing, consistent with experiment. We also predict that the disordered phase has a large positive mixing enthalpy and a slight tendency for Ge–Sn clustering, indicated by weakly negative short-range order parameters. This large positive mixing enthalpy produces a miscibility gap with a critical temperature above 2300 K, implying that the high Ge and Sn solubilities observed in thin-film synthesis cannot be explained by the incoherent phase diagram alone. We demonstrate that coherency strain substantially alters phase stability. Calculations of the coherent spinodal show significant suppression of the miscibility gap, reducing the critical temperature to ≈ 900 K. These coherent phase boundaries could account for the experimentally observed high solubilities at typical growth temperatures. Our results indicate that coherency strain stabilizes these metastable alloys and enables bandgap engineering in this ultrawide-bandgap material system.

en: Thermodynamic and electronic properties of rutile Sn1−x Gex O2 alloys from first principles

band-gap

display_name: band-gap

classical-statistical-mechanics

display_name: classical-statistical-mechanics

thermodynamics-of-mixing

display_name: thermodynamics-of-mixing


name: Stadt Winterthur, Stadtentwicklung der Stadt Winterthur

de:
Dieser Datensatz vergleicht die Angebotsmieten mit den Bestandesmieten in Winterthur. Die Statistiken wurden berechnet auf Stufe Stadtkreis und dem gesamten Stadtgebiet. Enthalten sind durchschnittliche, monatliche Mietpreise. Dabei wird zwischen der Anzahl der Zimmer unterschieden.

Die Daten stammen aus dem Wohnmonitor und geben einen Überblick über die Wohnsituation in Winterthur auf verschiedenen räumlichen Ebenen.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Angebots- und Bestandesmieten in Winterthur, pro Stadtkreis und Zimmerzahl

de: genossenschaftswohnungen wohnungsmieten mietpreise mietwohnungen zimmerzahl

stadt-winterthur

de: Die Stadt Winterthur als intelligente, vernetzte und sichere Stadt nutzt städtische Daten als Basis für effiziente und nutzerfreundliche Lösungen und stellt sie der Allgemeinheit unter Gewährleistung des Datenschutzes als 'Open Data' zur freien Wiederverwendung zur Verfügung. Die Publikation der OGD-Datensätze ist in Erarbeitung, das Angebot wird laufend ausgebaut.

de: Stadt Winterthur

political_level: commune

value: Stadt Winterthur - DCAT Harvester

soci

de: Bevölkerung und Gesellschaft

en: Population and society

fr: Population et société

it: Popolazione e società

regi

de: Regionen und Städte

en: Regions and cities

fr: Régions et villes

it: Regioni e città

de: Die Daten sind mit Quelle und Definition in der Ressource «Dokumentation Daten» vollständig beschrieben.

format: CSV

de: Dokumentation Daten

format: TXT

genossenschaftswohnungen

display_name: genossenschaftswohnungen

mietpreise

display_name: mietpreise

mietwohnungen

display_name: mietwohnungen

wohnungsmieten

display_name: wohnungsmieten

zimmerzahl

display_name: zimmerzahl


de:
Der Datensatz zeigt, wie sich der Wohnungsbestand in Winterthur zusammensetzt.
Er enthält Informationen zu Bauperioden, Wohnungsgrössen und zur Anzahl Zimmer und weist diese für Quartiere, Stadtkreise und für die gesamte Stadt aus.
Zudem wird unterschieden, ob sich die Wohnungen in Einfamilienhäusern oder in Mehrfamilienhäusern befinden.
Die Daten stammen aus dem Wohnmonitor und geben einen Überblick über die Wohnsituation in Winterthur auf verschiedenen räumlichen Ebenen.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Wohnungsbestand in Winterthur nach Quartieren und Stadtkreisen

de: wohnflaeche wohnung wohnungen einfamilienhaus wohnungsbestand quartieranalyse zimmerzahl

einfamilienhaus

display_name: einfamilienhaus

quartieranalyse

display_name: quartieranalyse

wohnflaeche

display_name: wohnflaeche

wohnung

display_name: wohnung

wohnungen

display_name: wohnungen

wohnungsbestand

display_name: wohnungsbestand


de:
Der Datensatz zeigt die Preise für Wohnungen in Winterthur, wie sie auf Immobilienporalen ausgeschrieben wurden. Enthalten sind Wohnungen zur Miete als auch zum Kauf, aggregiert bis auf Stufe Stadtkreis. Zudem wird unterschieden zwischen neuwertigen Wohnungen und dem Altbestand an Wohnungen.

Die Daten stammen aus dem Wohnmonitor und geben einen Überblick über die Wohnsituation in Winterthur auf verschiedenen räumlichen Ebenen.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Mieten und Kaufpreise in Wohnungsinseraten in Winterthur nach Stadtkreisen

de: quartieranalyse immobilienpreise mietpreise mietwohnungen preise preisentwicklung wohnbevoelkerung wohneigentum

immobilienpreise

display_name: immobilienpreise

preise

display_name: preise

preisentwicklung

display_name: preisentwicklung

wohnbevoelkerung

display_name: wohnbevoelkerung

wohneigentum

display_name: wohneigentum


de:
Der Datensatz zeigt die Anzahl der Haushalte von Winterthur, neuzugezogen und alteingesessen.
Es wird zudem unterschieden nach Haushaltstyp und der Lebensphase, in der sich der Haushalt befindet.
Die Informationen sind verfügbar für Quartiere, Stadtkreise und der gesamten Stadt Winterthur.
Die Daten stammen aus dem Wohnmonitor und geben einen Überblick über die Wohnsituation in Winterthur auf verschiedenen räumlichen Ebenen.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Haushaltsstruktur in Winterthur nach Quartieren und Stadtkreisen

de: quartieranalyse haushalte haushaltsgroesse haushaltsstruktur haushaltstypen bevoelkerung

de: Die Datenreihe des Wohnmonitors gibt einen Überblick zum Wohnen in Winterthur auf Stufe Quartier, Stadtkreis und der gesamten Gemeinde. Die Daten sind mit Quelle und Definition in der Ressource «Dokumentation Daten» vollständig beschrieben.

bevoelkerung

display_name: bevoelkerung

haushalte

display_name: haushalte

haushaltsgroesse

display_name: haushaltsgroesse

haushaltsstruktur

display_name: haushaltsstruktur

haushaltstypen

display_name: haushaltstypen


de:
Der Datensatz zeigt die Wohnsituation der Haushalte von Winterthur.
Es wird unterschieden nach Haushaltstyp und der Lebensphase, in der sich der Haushalt befindet.
Zudem werden die Werte gruppiert nach Bauperiode, Gebäudetyp und der Belegungsdichte der Haushalte.
Die Informationen sind verfügbar für Quartiere, Stadtkreise und der gesamten Stadt Winterthur.
Die Daten stammen aus dem Wohnmonitor und geben einen Überblick über die Wohnsituation in Winterthur auf verschiedenen räumlichen Ebenen.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Wohnsituation der Haushalte in Winterthur nach Quartieren und Stadtkreisen

de: quartieranalyse haushalte haushaltsgroesse haushaltsstruktur haushaltstypen


de:
Der Datensatz zeigt das steuerbare Einkommen und Vermögen der Wohnbevölkerung von Winterthur.
Einkommen und Vermögen werden zudem nach Haushaltstyp unterschieden.
Dabei werden die Werte in Perzentile angegeben, sowohl pro Haushalt als auch pro Äquivalenzperson.
Die Informationen sind verfügbar für Quartiere, Stadtkreise und der gesamten Stadt Winterthur.
Die Daten stammen aus dem Wohnmonitor und geben einen Überblick über die Wohnsituation in Winterthur auf verschiedenen räumlichen Ebenen.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Steuerbares Einkommen und Vermögen in Winterthur nach Quartieren und Stadtkreisen

de: gemeindesteuern vermoegen einkommen haushaltsgroesse haushaltsstruktur haushaltstypen steuern steuerpflichtige

stadt-winterthur

de: Die Stadt Winterthur als intelligente, vernetzte und sichere Stadt nutzt städtische Daten als Basis für effiziente und nutzerfreundliche Lösungen und stellt sie der Allgemeinheit unter Gewährleistung des Datenschutzes als 'Open Data' zur freien Wiederverwendung zur Verfügung. Die Publikation der OGD-Datensätze ist in Erarbeitung, das Angebot wird laufend ausgebaut.

de: Stadt Winterthur

soci

de: Bevölkerung und Gesellschaft

econ

de: Wirtschaft und Finanzen

en: Economy and finance

fr: Économie et finances

it: Economia e finanze

einkommen

display_name: einkommen

gemeindesteuern

display_name: gemeindesteuern

steuern

display_name: steuern

steuerpflichtige

display_name: steuerpflichtige

vermoegen

display_name: vermoegen


de: Anzahl der Besuche pro Bibliothek, Museum und Theater in Winterthur. Winterthur bietet eine Vielzahl an kulturellen Einrichtungen und Veranstaltungen. Die hier aufgeführten Besuche sind eine enge Auswahl und sollen in Zukunft um weitere Einrichtungen und Veranstaltungen ergänzt werden.

de: Besuche kultureller Einrichtungen in Winterthur

de: theater museen bibliotheken

educ

de: Bildung, Kultur und Sport

en: Education, culture and sport

fr: Éducation, culture et sport

it: Istruzione, cultura e sport

de: Die Indikatoren sind mit Quelle und Definition in der Ressource «Dokumentation Daten» vollständig beschrieben.

media_type: text/csv

de: Dokumentation Daten

media_type: text/plain

bibliotheken

display_name: bibliotheken

museen

display_name: museen

theater

display_name: theater


feuerwehr-einsatze-in-winterthur

de: Schutz & Intervention Winterthur veröffentlicht seit 2019 sämtliche Feuerwehr-Einsätze auf der städtischen Website. Die Mehrheit der Einsätze finden auf städtischem Gebiet statt. Wird die Feuerwehr von Schutz & Intervention Winterthur zur Unterstützung in umliegende Gemeinden gerufen, sind diese Einsätze ebenfalls im API erfasst. Die Rohdaten sind als API verfügbar und können sowohl als JSON als auch als CSV bezogen werden. In den Rohdaten enthalten sind Angaben wie das Einsatzdatum, der Zeitpunkt des Alarms, die Dauer des Einsatzes, ein Titel, die verwendeten Fahrzeuge sowie die Anzahl der involvierten Personen der Feuerwehr und des Zivilschutzes. Die Einsätze werden regelmässig (meist täglich) nachgeführt.

de: Feuerwehr-Einsätze in Winterthur

de: feuerwehr rettung sicherheit

de: API-Dokumentation Feuerwehr-Einsätze

en: API-Dokumentation Feuerwehr-Einsätze

fr: API-Dokumentation Feuerwehr-Einsätze

it: API-Dokumentation Feuerwehr-Einsätze

title_for_slug: feuerwehr-einsatze-in-winterthur

just

de: Justiz, Rechtssystem und öffentliche Sicherheit

en: Justice, legal system and public safety

fr: Justice, système juridique et sécurité publique

it: Giustizia, sistema giuridico e sicurezza pubblica

de: Dieser Endpoint liefert die Einsätze der Feuerwehr Winterthur im JSON-Format. Die Dokumentation zum API ist im Datensatz verlinkt unter "API-Dokumentation Feuerwehr-Einsätze".

de: Feuerwehr-Einsätze Winterthur (API JSON)

format: JSON

media_type: application/json

de: Dieser Endpoint liefert die Einsätze der Feuerwehr Winterthur im CSV-Format. Die Dokumentation zum API ist im Datensatz verlinkt unter "API-Dokumentation Feuerwehr-Einsätze".

de: Feuerwehr-Einsätze Winterthur (API CSV)

feuerwehr

display_name: feuerwehr

rettung

display_name: rettung

sicherheit

display_name: sicherheit


de:
Der Datensatz zeigt die wirtschaftliche Wohnbevölkerung der Stadt Winterthur ab 2014. Enthalten sind Angaben zur Anzahl Personen nach Nationalität, Geschlecht und Altersklasse. Die Nationalität unterscheidet zwischen Schweizer Staatsangehörigen und ausländischen Staatsangehörigen. Die Altersklassen sind in 5-Jahres-Gruppen eingeteilt. Die Daten zeigen, wie sich die Zusammensetzung der Bevölkerung in Winterthur entwickelt.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Bevölkerung der Stadt Winterthur, nach Heimat, Geschlecht und Altersklasse

de: amtlichesgeschlecht bevoelkerung altersgruppen gemeinden nationalitaet

stadt-winterthur

de: Die Stadt Winterthur als intelligente, vernetzte und sichere Stadt nutzt städtische Daten als Basis für effiziente und nutzerfreundliche Lösungen und stellt sie der Allgemeinheit unter Gewährleistung des Datenschutzes als 'Open Data' zur freien Wiederverwendung zur Verfügung. Die Publikation der OGD-Datensätze ist in Erarbeitung, das Angebot wird laufend ausgebaut.

de: Stadt Winterthur

soci

de: Bevölkerung und Gesellschaft

de: Eine vollständige Dokumentation ist in der Ressource "Dokumentation Daten" zu finden.

de: Bevölkerung, nach Heimat, Geschlecht und Alter

altersgruppen

display_name: altersgruppen

amtlichesgeschlecht

display_name: amtlichesgeschlecht

gemeinden

display_name: gemeinden

nationalitaet

display_name: nationalitaet


de:
Der Datensatz zeigt die wirtschaftliche Wohnbevölkerung der Stadt Winterthur ab 2014. Enthalten sind Angaben zur Anzahl Personen nach Jahr, Quartier, Stadtkreis, Ländergruppe und Region. Die Ländergruppen fassen Staatsangehörigkeiten zusammen, zum Beispiel Italien, Nordeuropa oder Südosteuropa. Die Daten zeigen, wie sich die Bevölkerung in den Quartieren nach Staatsangehörigkeit und Herkunftsregion zusammensetzt. Kleine Gruppen werden aus Datenschutzgründen nicht immer mit einer Anzahl ausgewiesen.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Bevölkerung der Stadt Winterthur, nach Staatenkleingruppe und Quartier

de: bevoelkerungswachstum gemeinden bevoelkerung nationalitaet

de: Bevölkerung, nach Staatenkleingruppe und Quartier

de: Dokumentation Daten

bevoelkerung

display_name: bevoelkerung

bevoelkerungswachstum

display_name: bevoelkerungswachstum


de:
Der Datensatz zeigt die wirtschaftliche Wohnbevölkerung der Stadt Winterthur per Ende Jahr. Enthalten sind Angaben zur Anzahl Personen nach Jahr und Ländergruppe. Die Ländergruppen fassen Nationalitäten zusammen, zum Beispiel Schweiz, Italien oder Nordeuropa. Die Daten zeigen, wie sich die Bevölkerung in Winterthur nach Nationalität zusammensetzt.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Bevölkerung der Stadt Winterthur, nach Staatenkleingruppe

de: Bevölkerung, nach Staatenkleingruppe


de:
Der Datensatz zeigt die wirtschaftliche Wohnbevölkerung der Stadt Winterthur per Ende Jahr. Enthalten sind Angaben zur Anzahl Personen nach Nationalität und Geburtsstaat. Die Nationalität unterscheidet zwischen Schweizer Staatsangehörigen und ausländischen Staatsangehörigen. Der Geburtsstaat unterscheidet zwischen in der Schweiz geborenen Personen und im Ausland geborenen Personen. Die Daten zeigen, wie sich die Bevölkerung in Winterthur nach Nationalität und Geburtsstaat zusammensetzt.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Bevölkerung der Stadt Winterthur, nach Heimat und Herkunft

de: bevoelkerung herkunft bevoelkerungswachstum gemeinden nationalitaet

de: Bevölkerung, nach Heimat und Herkunft

herkunft

display_name: herkunft


beschaftigte-der-stadt-winterthur

de:
Der Datensatz zeigt die Anzahl Beschäftigte in der Stadt Winterthur ab 2018. Die Daten liegen pro Quartal vor und beziehen sich jeweils auf den Stand am Ende des Quartals. Enthalten sind Angaben zur Gesamtzahl der Beschäftigten, zu Beschäftigten nach Geschlecht sowie zu Beschäftigten im zweiten und dritten Wirtschaftssektor. Zusätzlich enthält der Datensatz Vollzeitäquivalente. Die Zahlen sind Schätzungen und auf 100 gerundet. Die Daten stammen aus der Beschäftigungsstatistik BESTA des Bundesamts für Statistik.
Weitere Informationen zur Datenquelle sowie zu den verwendeten Definitionen sind in der Ressource «Dokumentation Daten» beschrieben.

de: Beschäftigte der Stadt Winterthur

de: arbeitskraefte gemeinden beschaeftigte bevoelkerung

stadt-winterthur

de: Die Stadt Winterthur als intelligente, vernetzte und sichere Stadt nutzt städtische Daten als Basis für effiziente und nutzerfreundliche Lösungen und stellt sie der Allgemeinheit unter Gewährleistung des Datenschutzes als 'Open Data' zur freien Wiederverwendung zur Verfügung. Die Publikation der OGD-Datensätze ist in Erarbeitung, das Angebot wird laufend ausgebaut.

de: Vorbereiteter Code in R oder Python für jedes Dataset mit csv-Datei(en)

title_for_slug: beschaftigte-der-stadt-winterthur

econ

de: Wirtschaft und Finanzen

de:
Die Daten beinhalten aggregierte Beschäftigtenzahlen in der Stadt Winterthur. Sie sind nach Sektor und Geschlecht aufgeschlüsselt. Neben den Totalen werden auch die Vollzeitäquivalente ("VZAE") abgebildet. Die hier ausgewiesenen Daten zu den Beschäftigten basieren auf der Beschäftigungsstatistik (BESTA) des Bundesamt für Statistik BFS.
Die Daten sind mit Quelle und Definition in der Ressource «Dokumentation Daten» vollständig beschrieben.

de: Beschäftigte im Quartal nach Sektor und Geschlecht in Total und Vollzeitäquivalente ab 2018

arbeitskraefte

display_name: arbeitskraefte

beschaeftigte

display_name: beschaeftigte