en:
Deorbitalization of a conventional meta-generalized-gradient exchange-correlation approximation replaces its dependence upon the Kohn-Sham kinetic energy density with a dependence on the density gradient and Laplacian. In principle, that simplification should provide improved computational performance relative to the original meta-GGA form because of the shift from an orbital-dependent generalized Kohn-Sham potential to a true KS local potential. Often that prospective gain is lost because of problematic roughness in the density caused by the density Laplacian and consequent roughness in the exchange-correlation potential from the resulting higher-order spatial derivatives of the density in it. We address the problem by constructing a deorbitalizer based on the RPP deorbitalizer [Phys. Rev. Mater. 6, 083803 (2022)] with comparative smoothness of the potential along with retention of constraint satisfaction as design goals. Applied to the r2SCAN exchange-correlation functional [J. Phys. Chem. Lett. 11, 8208 (2020)], we find substantial timing improvements for solid-state calculations over both r2SCAN and its earlier deorbitalization for high precision calculations of structural properties, while improving upon the accuracy of RPP deorbitalization for both solids and molecules.

Data archive includes data needed to reproduce the figures and tables of this work, and input scripts for test set calculations performed using VASP and NWChem electronic structure codes.

en: Performance improvement of deorbitalized exchange-correlation functionals

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).

computation

display_name: computation

density-functional-theory

display_name: density-functional-theory

deorbitalization

display_name: deorbitalization

metagga

display_name: metagga


$oid: OBSOLETE

en:
Advanced fission and fusion technologies require materials that withstand extreme conditions, relying on irradiation-tolerant structures to suppress defect evolution and mechanical degradation. This study introduces a novel approach to immobilize irradiation-induced defects by tuning local lattice distortion in concentrated solid solution alloys. Results demonstrate that higher distortion levels significantly reduce microstructural changes. The single-phase Ni80Mo20 alloy, with the highest recorded distortion (4.82% atomic size mismatch), exhibits frozen defect motion and negligible irradiation effects. This suggests engineering lattice distortion as a straightforward method for designing irradiation-resistant metallic alloys.

This dataset provides the initial and final atomic structures from molecular dynamics simulations of single vacancy and single interstitial diffusion in Ni, NiFe, NiCoV, and Ni80Mo20 alloys, each exhibiting distinct levels of lattice distortion.

en: Achieving enhanced irradiation-resistant metallic alloys by immobilizing induced defects

materialscloud

approval_status: approved

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

irradiation-damage

display_name: irradiation-damage

lattice-distortion

display_name: lattice-distortion

metallic-alloys

display_name: metallic-alloys

molecular-dynamics

display_name: molecular-dynamics


en: We establish a quantitative relation between the altermagnetic spin-splitting and different higher order multipoles of the charge and magnetization density around the magnetic atoms. Magnetic multipoles such as octupoles or triakontadipoles have been suggested as potential ferroic order parameters for d- and g-wave altermagnetism, respectively, based mainly on qualitative symmetry arguments. We use first-principles-based electronic structure calculations to establish a clear quantitative relation between the strength of the altermagnetic spin splitting and the magnitude of certain local multipoles. We vary the magnitude of these multipoles either by applying an appropriate constraint on the charge density or by varying a corresponding structural distortion mode, using two simple perovskite materials, SrCrO₃ and LaVO₃, as model systems. Our analysis indicates that in general the altermagnetic spin splitting is not exclusively determined by the lowest order nonzero magnetic multipole, but results from a superposition of contributions from different multipoles with comparable strength, suggesting the need for a multi-component order parameter to describe altermagnetism. We also discuss different measures to quantify the overall spin-splitting of a material, without relying on features that might be specific to only individual bands.

en: Multipoles as quantitative order parameters for altermagnetic spin splitting

altermagnetism

display_name: altermagnetism

dft

display_name: dft

multipoles

display_name: multipoles


en: This repository accompanies the manuscript “Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation” and contains vasprun.xml files of all calculations. The manuscript presents DFT calculations made with VASP of the 2H, 4H and 6H polytypes of Ge and SiGe and discusses their electronic and optical properties. The first part presents transition matrix elements and the radiative lifetimes. The second part presents the dielectric functions and absorptions of the structures from model BSE calculations.

en: Data availability for "Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation"

condensed-matter-physics

display_name: condensed-matter-physics

hexagonal-ge

display_name: hexagonal-ge

model-bse

display_name: model-bse

optoelectronic-materials

display_name: optoelectronic-materials

polytypes

display_name: polytypes


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

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: Interatomic potentials remain a critical bottleneck for accurate large-scale atomistic simulations. Machine-learning potentials provide a promising route to overcoming this limitation; however, the construction of property-relevant training datasets remains a major challenge. Here, we propose an active-learning strategy based on property balancing, in which the fraction of atomic environments associated with pre-identified key material properties is dynamically optimized during training. We demonstrate this strategy for pure magnesium, whose macroscopic plastic deformation is governed by several subtle dislocation transformations that place stringent demands on potential accuracy. Using the proposed strategy, we develop a Behler–Parrinello-type neural-network potential that captures these dislocation-mediated mechanisms with a level of accuracy that has been difficult to achieve in previous studies of magnesium. The successful application to magnesium highlights the versatility and effectiveness of property balancing as a general strategy for developing machine-learning potentials for metallic systems with multiple competing property requirements. This approach provides a practical pathway toward predictive mechanistic simulations of structural metals.

en: Property-balancing active learning strategy for machine learning interatomic potentials: case study on dislocation mechanics in magnesium

dislocation

display_name: dislocation

magnesium

display_name: magnesium

neural-network-potential

display_name: neural-network-potential


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

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 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).

adsorption

display_name: adsorption

dimethyl-oxalate

display_name: dimethyl-oxalate

electron-density-difference

display_name: electron-density-difference

moox

display_name: moox


en:
This dataset supports a computational study of finite-concentration pair breaking in Mn- and Co-doped 2H-NbSe2 using the coherent potential approximation combined with Bogoliubov-de Gennes calculations within the KKR Green-function framework. The work investigates how magnetic impurities located on the van der Waals empty-sphere site modify the superconducting anomalous density of 2H-NbSe2 from the dilute impurity limit up to the critical concentration where superconductivity collapses.

The dataset contains the full AiiDA provenance needed to reproduce the calculations reported in the companion manuscript. It includes the pristine superconducting 2H-NbSe2 reference calculation, normal-state CPA KKR calculations for Mn and Co impurities, and self-consistent CPA-BdG concentration series for both dopants. These data reproduce the concentration-dependent normalized anomalous density, the impurity-dependent critical concentrations, and the comparison between Mn- and Co-induced changes in the superconducting response.

All calculations were performed with the JuKKR Green-function code through the AiiDA-KKR workflow infrastructure on the JURECA-DC supercomputer at Forschungszentrum Jülich, with additional computational resources from CLAIX at RWTH Aachen University. The record is intended to provide transparent access to the computational provenance, input and output data, and analysis workflow underlying the reported CPA-BdG results.

en: CPA-BdG calculations for Mn- and Co-doped 2H-NbSe2: finite-concentration pair breaking and anomalous density

materialscloud

en: Materials Cloud

aiida

display_name: aiida

anomalous-density

display_name: anomalous-density

bogoliubov-de-gennes-bdg

display_name: bogoliubov-de-gennes-bdg

co

display_name: co

coherent-potential-approximation-cpa

display_name: coherent-potential-approximation-cpa

kkr-green-function-method

display_name: kkr-green-function-method

magnetic-impurities

display_name: magnetic-impurities

mn

display_name: mn

pair-breaking

display_name: pair-breaking

spin-orbit-coupling

display_name: spin-orbit-coupling

superconductivity

display_name: superconductivity

transition-metal-dichalcogenide

display_name: transition-metal-dichalcogenide

yu-shiba-rusinov-states

display_name: yu-shiba-rusinov-states


en: Topological constraints such as the Bernal-Fowler ice rules govern atomic arrangements in proton-disordered crystals. Machine learning force fields (MLFFs) provide a computationally efficient route to exploring such disordered energy landscapes using on-the-fly sampling without explicit topological supervision. Whether such a data-driven approach can autonomously recover ice-rule constraints remains an open question. Here, we show that a data-efficient GAP-SOAP force field trained on 212 DFT calculations achieves a energy RMSE of 19 meV/H2O on Ice VII configurations and reproduces phonon dispersions and infrared spectra in good agreement with DFT. A representative trained model relaxes 8 × 8 × 8 supercells (3072 atoms) with randomized proton orientations into structures consistent with ice-rule constraints, recovering a body-centred cubic oxygen lattice at 30 GPa. Under compression, the model captures the qualitative features of the Ice VII → Ice X transformation and remains stable over the 30–90 GPa range for the tested configurations. While extended training does not guarantee monotonic improvement in all properties, the model enables efficient large-scale molecular dynamics, reaching approximately one frame per second on a single CPU core. These results demonstrate that local environment descriptors can encode key aspects of hydrogen-bond topology, providing a data-efficient route to structural relaxation in disordered molecular crystals.

en: Emergence of Bernal-Fowler ice rules in crystal structure prediction via data-efficient machine learning force fields: the case of Ice VII

envi

en: Environment

active-learning

display_name: active-learning

crystal-structure-prediction

display_name: crystal-structure-prediction

high-pressure-ice

display_name: high-pressure-ice

ice-rules

display_name: ice-rules

ice-vii

display_name: ice-vii

machine-learning-force-fields

display_name: machine-learning-force-fields


en: Generative artificial-intelligence (AI) models, such as score-based diffusion models, have recently advanced the field of computational materials science by enabling the generation of new materials with desired properties. In addition, these models could also be leveraged to reconstruct crystal structures for which partial information is available. One relevant example is the reliable determination of atomic positions occupied by hydrogen atoms in hydrogen-containing crystalline materials. While crucial to the analysis and prediction of many materials properties, the identification of hydrogen positions via X-ray scattering experiments has been historically challenging, and often requires more expensive neutron scattering measurements. As a consequence, and despite experimental advances which enable us nowadays to accurately determine hydrogen positions based on X-ray scattering experiments, inorganic crystallographic databases still report many lattice structures where hydrogen atoms have been either omitted or inserted with heuristics or by chemical intuition. Here, we combine diffusion models from the field of materials science with techniques originally developed in computer vision for image inpainting. We present how this knowledge transfer across domains enables a much faster and more accurate completion of host structures, compared to unconditioned diffusion models or previous approaches solely based on density-functional theory (DFT). Overall, when applied to a test dataset of hydrogen-containing materials from the MC3D database, our approach exceeds a success rate of 97% in terms of finding a structural match or predicting a more stable configuration (according to DFT) than the initial reference from the experimental source database (and with a success rate exceeding 99% when excluding structures flagged as theoretical in MC3D), both when starting from structures that were already relaxed with DFT, or when starting directly from the experimentally determined host structures.

en: Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions

aiida

display_name: aiida

dft

display_name: dft

generative-ai

display_name: generative-ai

machine-learning

display_name: machine-learning

mlip

display_name: mlip


en: Hydrogen carriers that enable efficient transport and on-demand release of molecular hydrogen (H2) are crucial for practical hydrogen-based energy systems. Hydrogen boride (HB) nanosheets, composed of boron and hydrogen in a 1:1 stoichiometric ratio, have promising potential as safe and lightweight hydrogen carriers owing to their high gravimetric hydrogen density (8.5 wt %). In particular, heating of HB nanosheets results in H2 release over a broad temperature range from 423 to 1473 K. However, the mechanism of the multimodal H₂ desorption remains unclear. In this work, we elucidate that the interlayer H···H distances (dH···H) determine the multimodal desorption of H2 especially in the lower-temperature range (< 623 K). HB nanosheets subjected to various temperatures under different H2 pressures were prepared to investigate the thermal stability of their bonding configurations. Infrared spectroscopy and temperature-programmed desorption measurements revealed the occurrence of hydrogen depletion while the bonding configuration remains unchanged. The first to third nearest dH···H, along with the numbers of the corresponding H···H pairs, were systematically calculated for four possible interlayer stacking types. The calculated H···H pairs distribution closely matched the experimental profile for the thermally induced H2 desorption, suggesting that the multimodal desorption of H2 in the lower-temperature range is governed by the distribution of hydrogen distances. This study elucidates the unique mechanisms of H2 formation in HB nanosheets and provides valuable insights into the design of two-dimensional hydrogen containing materials.

en: Interlayer hydrogen-hydrogen spacing regulates the formation of molecular hydrogen in hydrogen boride nanosheets

hydrogen-boride-nanosheet

display_name: hydrogen-boride-nanosheet

hydrogen-release

display_name: hydrogen-release

interlayer-space

display_name: interlayer-space

two-dimensional-materials

display_name: two-dimensional-materials


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

input-files

display_name: input-files

output-files

display_name: output-files

quantum-espresso

display_name: quantum-espresso


en: Predicting quasiparticle energies in materials requires expensive numerical evaluations of the electron self-energy. This limits calculations to ordered systems with small unit cells. Here, using symbolic regression, we show that the GW self-energy can be accurately approximated with compact analytical functions of physically motivated Kohn-Sham descriptors. These expressions can be learned from a single GW calculation in the ordered phase and remain accurate under symmetry breaking induced by quantum and thermal fluctuations, elastic deformations, and amorphous disorder. This development enables routine GW calculations of complex materials with thousands of atoms at a computational cost comparable to semi-local density functional theory. We demonstrate the accuracy of this approach for covalent semiconductors, ionic insulators, and two-dimensional materials. These results establish symbolic regression as a viable route to predictive, interpretable, and transferable many-body electronic structure models.

en: Electron correlation in semiconductors and insulators via symbolic regression

electronic-structure

display_name: electronic-structure

equation-discovery

display_name: equation-discovery

gw

display_name: gw

many-body-perturbation-theory

display_name: many-body-perturbation-theory

symbolic-regression

display_name: symbolic-regression


en: In the field of orthopedic biomechanics, synthetic bone surrogates are essential for evaluating surgical techniques, implant stability, and fracture risks due to their reproducible geometry and consistent mechanical behavior compared to human cadaveric tissue. Fourth-generation (4th Gen) Sawbones(R) composite models, which utilize a short fiber-filled epoxy (SFFE) cortical shell and a polyurethane foam (PUF) cancellous core, represent the industry standard. However, a significant gap remains in the literature regarding the full anisotropic characterization of these individual constituents, which is critical for high-fidelity numerical simulations. This study presents a comprehensive experimental dataset covering uniaxial tension (ASTM D638), compression (ASTM D695), and fracture toughness (ASTM E399) tests, alongside specialized shear characterization using a modified Single Cube Apparatus (SCA). The dataset includes 93 successful mechanical tests: 56 for the SFFE cortical surrogate and 37 for the PUF cancellous surrogate. The results validate the transverse isotropy of the SFFE and the isotropic behavior of the PUF. These findings provide the required mechanical properties for accurate finite element studies, facilitating the rigorous validation of synthetic bone surrogates as representations of biological human tissue.

en: Dataset for characterization of fracture and elastic properties of commercially available polyurethane foam and short fiber filled epoxy for bone models

anisotropy

display_name: anisotropy

bone-substitutes

display_name: bone-substitutes

characterization

display_name: characterization

experimental

display_name: experimental

fracture-toughness

display_name: fracture-toughness

sawbones

display_name: sawbones


en: The favourable thermodynamic properties of magnesium make it a promising negative electrode for rechargeable Mg-metal batteries. However, parasitic side reactions and uncontrolled deposition at the unstable electrode/electrolyte interface severely hinder reversible electrodeposition. Here we demonstrate that a high density of twin defects via cyclic-multidirectional deformation markedly increases the diffusion rate and simultaneously suppresses interfacial reactions. Comprehensive morphological characterisation combined with theoretical calculations reveals that the twinned-magnesium negative electrode exhibits significantly enhanced kinetics, leading to a reduced dimensionless electrochemical Damköhler number and indicating that magnesium deposition becomes less diffusion-limited.Furthermore, reorganisation energy analysis based on Marcus–Hush–Chidsey kinetics, in conjunction with the battery overpotential, indicates that the twin structure effectively mitigates parasitic reactions between the negative electrode and the electrolyte, thereby significantly reducing the passivation effect and enabling long-term reversible magnesium electrodeposition. Consequently, the designed twinned-magnesium electrodes in an asymmetric coin cell paradigm achieve a Coulombic efficiency of 99.53% over 2000 cycles at 30 mA cm-2 with a capacity of 2 mAh cm-2, competitive with reported magnesium metal negative electrodes. This simple strategy has been successfully extended to the zinc metal system as a proof of concept, demonstrating the universal potential of twin engineering for achieving reversible metal electrodeposition.

en: Twinned-magnesium metal negative electrodes enable reversible magnesium electrodeposition

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).

interface-passivation

display_name: interface-passivation

magnesium-electrodeposition

display_name: magnesium-electrodeposition

magnesium-metal-negative-electrodes

display_name: magnesium-metal-negative-electrodes

twinning

display_name: twinning


en: The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many widely used electronic-structure databases are assembled having materials screening as primary goal rather than robust force-field learning, are limited in their scope to a specific class of chemical compounds, and/or employ inconsistent DFT functionals and settings. Here we introduce MAD-1.5, a highly curated dataset designed explicitly for training broadly applicable atomistic models across the periodic table at high levels of theory. MAD-1.5 extends the MAD dataset with targeted enrichment strategies that improve the coverage of chemical space to 102 elements while keeping the total number of configurations compact. All structures are computed with a single, standardized all-electron DFT workflow using the r2SCAN meta-GGA functional and consistent convergence settings, ensuring uniformity across chemically heterogeneous systems. The dataset encompasses molecules, clusters, bulk crystals, surfaces, and low-dimensional structures, and its quality and consistency are further enhanced by outlier removal using uncertainty quantification. We demonstrate the high accuracy that can be achieved with the proposed dataset by training PET-MAD-1.5, a generally applicable r2SCAN interatomic potential that covers 102 elements in the periodic table and achieves exceptional levels of benchmark accuracy and stability in challenging simulation protocols.

en: High-quality, high-information datasets for universal atomistic machine learning

materialscloud

en: Materials Cloud

atomistic-simulations

display_name: atomistic-simulations

dataset

display_name: dataset

density-functional-theory

display_name: density-functional-theory

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning

universal-models

display_name: universal-models


en: Dynamical control of the optical nonlinear properties in solids – with light itself – will be essential for the development of ultrafast photonic technologies, including high-speed terahertz-rate data communication and advanced signal processing. Previously, methods for optically modulating nonlinear optical processes, such as second harmonic generation (SHG), have relied predominantly on non-resonant light-matter interaction or photo-generation of hot electrons in nanoscale materials. However, these approaches are typically constrained by limited interaction lengths and the initial frequency conversion is relatively weak under equilibrium conditions, making them impractical for devices that require high-contrast switching of strong signals. Here, modulation of a strong SHG signal in a bulk β-BaB2O4 (BBO) nonlinear optical crystal is achieved through resonant transient lattice deformation induced by intense terahertz (THz) pulses tuned to a specific infrared-active phonon mode. Selective lattice excitation results in an unprecedented ~30% modulation of the SHG in BBO. In contrast to past efforts employing non-resonant THz interaction, this effect cannot be explained by modulation of the nonlinear susceptibility – a 𝜒(3)-effect. But rather – through polarization-resolved measurements, ab initio calculations, and analytical modelling –the SHG modulation is found to be driven by THz-induced phonon dynamics that affect the nonlinear optical phase-matching conditions through a 𝜒(2) process. This resonant all-optical control mechanism points toward new optical control schemes, based on selected lattice degrees of freedom, to dynamically tune the overall nonlinear optical response in bulk materials. Such schemes may find practical applications in ultrafast optoelectronics and integrated photonics.

en: All-optical control of second-harmonic generation in β-BaB2O4 via coherent, terahertz-driven acentric lattice displacement

envi

en: Environment

all-optical-control-in-bulk-crystals

display_name: all-optical-control-in-bulk-crystals

nonlinear-optical-phase-matching

display_name: nonlinear-optical-phase-matching

nonlinear-susceptibility

display_name: nonlinear-susceptibility

resonant-lattice-dynamics

display_name: resonant-lattice-dynamics

second-harmonic-generation

display_name: second-harmonic-generation

terahertz-phonon-excitation

display_name: terahertz-phonon-excitation


en: The Shastry-Sutherland compound SrCu2(BO3)2 (SCBO) is a paradigmatic low-dimensional quantum spin system, featuring geometrically frustrated interacting spin dimers and a rich landscape of emergent quantum phenomena. While its magnetic properties have been extensively explored, the nature of its electronic excitations - particularly the Cu2+ d–d transitions - remains poorly understood. A detailed characterization of these excitations is essential to determine the crystal field splitting and the degree of Cu 3d–O 2p hybridization, both of which underpin superexchange interactions and magnetic behavior in SCBO. Here, the electronic properties of SCBO are investigated by combining spectroscopic techniques with theoretical approaches for first-principles band structure calculations.Experimentally, we employ L3-edge resonant inelastic x‑ray scattering (RIXS) to directly probe intra-d excitations, supplemented by broadband infrared reflectivity and ellipsometry measurements to resolve charge-transfer (CT) features. Theoretically, multireference quantum chemistry calculations accurately reproduce the energies (1.8–2.4 eV) and symmetries of the observed d–d excitations, while band-structure calculations based on density functional theory elucidate the CT excitation spectrum. These combined approaches define the energy scales for both d–d and CT transitions, validate the computational framework, and provide quantitative microscopic parameters essential for refining superexchange-based magnetic models in this prototypical frustrated quantum antiferromagnet.

en: Probing high-energy electronic excitations in the Shastry-Sutherland compound SrCu2(BO3)2 with resonant inelastic x-ray scattering and optical spectroscopy

d-d-excitations

display_name: d-d-excitations

dftu

display_name: dftu

resonant-inelastic-x-ray-scattering

display_name: resonant-inelastic-x-ray-scattering

shastry-sutherland-model

display_name: shastry-sutherland-model


en: We developed a machine learning interatomic potential (MLIP) for Ge-rich GeSbTe alloys of interest for applications in phase change memories embedded in microcontrollers. The MLIP was generated by fitting with a neural network method a large database of energies and forces computed within density functional theory of elemental, binary, stoichiometric and non-stoichiometric ternary alloys in the Ge-Sb-Te phase diagram. The MLIP is demonstrated to be highly transferable to large regions of the phase diagram around the compositions included in the dataset. The MLIP is then exploited to simulate the crystallization with phase separation of three Ge-rich alloys on the Ge-SbTe and Ge- GeSbTe tie-lines that correspond to the set process of the memory cell. The transformation on the ns time scale and at 600 K, comparable to the operation conditions of the memory, yields crystalline cubic GeTe slightly Sb-doped and amorphous GeSb and Ge. These metastable phases differ from the thermodynamically stable products and form due to kinetics effects on the short time span of the set operation in phase change memories.

en: On phase separation and crystallization of Ge-rich GeSbTe alloys from atomistic simulations with a machine learning interatomic potential

database

display_name: database

interatomic-potential

display_name: interatomic-potential


en: Contact variability remains a major obstacle for scaled two-dimensional transistors, but the extent to which contact geometry can control defect sensitivity remains unclear. This dataset supports a first-principles comparison of top and recessed Ti-MoS2 contacts under a common ordered sulfur-vacancy sequence. The deposited data include VASP POSCAR-format atomic structures generated from NanoDCAL device geometries, the corresponding NanoDCAL SCF input files used as structure sources, and processed metadata tables documenting vacancy coordinates, contact geometries, convergence status and Schottky-barrier-height extraction. These data support the manuscript conclusion that recessed Ti contacts strongly reduce vacancy-position-dependent carrier-injection-barrier variation in MoS2 relative to conventional top contacts.

en: Defect-tolerant carrier injection in MoS2 enabled by recessed Ti contacts

recessed-contact

display_name: recessed-contact

schottky-barrier

display_name: schottky-barrier


en: We develop data-efficient machine learning interatomic potentials (MLIPs) for fast molecular dynamics simulations combining DeePMD and MACE models within an active learning and knowledge distillation framework. Using liquid water as a case study, we first independently train DeePMD and MACE models from scratch through active learning. We find that MACE requires around 3 times less training data than DeePMD, but its inference speed is 10 times lower. We also show that starting from a pretrained foundation model based on the MACE architecture further reduces the training data by a factor of 7, resulting in a fine-tuned foundation model with a 25 times data reduction compared to DeePMD. To overcome the limitation associated with the lower inference speed of MACE potentials, we next develop a knowledge distillation scheme to train a DeePMD potential from the fine-tuned foundation model through an inexpensive active learning workflow. The distilled model is generated with ~10 times less computer time than the DeePMD model trained from scratch, while showing the same fast inference speed.Comparison with ab initio calculations shows that all the models reach the same level of accuracy in reproducing structural, vibrational, and diffusive properties of liquid water. Our approach enables practical, data-efficient training of customized MLIPs with high speed and accuracy.

en: Data-efficient and fast machine learning molecular dynamics through integrated active learning and knowledge distillation

active-learning

display_name: active-learning

deepmd

display_name: deepmd

knowledge-distillation

display_name: knowledge-distillation

mace

display_name: mace

machine-learning-interatomic-potential

display_name: machine-learning-interatomic-potential


en: The dataset provides crystallographic information (CIF) files for 27 dynamically stable two-dimensional silicon–metal borophene monolayers (Manuscript ID: BP14856, submitted to Physical Review B), which are made available for reviewers to download and inspect. All structures were relaxed using density functional theory (DFT) with the projector augmented wave–generalized gradient approximation (PAW‑PBE) method, based on a periodic slab model with a vacuum gap of 30 Å. With these CIF files, the lattice parameters and chemical compositions can be conveniently cross‑checked against the present paper. The accompanying README file documents in detail all computational parameters, including the VASP code, the PAW‑PBE functional, a plane‑wave cutoff energy of 600 eV, and a k‑point grid spacing of 0.189 Å⁻¹.

en: Silicon-metal sublayer engineering of honeycomb borophene:   DFT-relaxed structures of 27 dynamically stable 2D X–Si–B monolayers

dft

display_name: dft

electron-phonon-coupling

display_name: electron-phonon-coupling


en: This project contains data and software used for the publication. Code is provided which was used to analyze the magnetic structure and spin excitations in the material Co3Sn2S2 using the software SpinW for Matlab. Diffuse magnetic scattering data and a small code to read it are also included.

en: Noncollinear kagome magnet driven by giant antisymmetric exchange

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).

diffuse-magnetic-scattering

display_name: diffuse-magnetic-scattering

spinw

display_name: spinw


en: This archive contains the data supporting the manuscript of the same title. The calculations are for bulk antiferromagnetic (type-II) NiO in a four-atom rhombohedral cell, and comprise the Wannierization of the Kohn-Sham manifold, the linear-response calculation of the on-site (U) and inter-site (V) Hubbard parameters, DFT+U+V calculations, and the construction of the block-diagonalizing and hybridized projectors discussed in the manuscript.

en: DFT+U+V is equivalent to DFT+U with density-dependent hybridized projectors

materialscloud

en: Materials Cloud

electronic-structure

display_name: electronic-structure


en: We investigate the Uhlenbeck--Ford (UF) model as a reference system for free-energy calculations in two-dimensional (2D) fluids. The 2D virial coefficients are computed exactly up to tenth order and combined with molecular simulation data to construct highly accurate numerical representations of the equation of state and the excess Helmholtz free energy. We then determine the phase diagram of the model in order to establish the thermodynamic stability limits of the fluid phase and thereby identify the range of applicability of the UF model as a fluid reference system. In the course of this analysis, we identify the solid, hexatic, and fluid phases, and show that the fluid remains the only thermodynamically stable phase, independent of density, for scaling parameters up to p<=70. Finally, we demonstrate the practical applicability of the 2D UF model as a reference system through thermodynamic integration calculations of the free energy of a two-dimensional Lennard--Jones fluid.

en: The Uhlenbeck–Ford model in two dimensions: Reference system for fluid-phase free-energy calculations

envi

en: Environment

free-energy

display_name: free-energy

lammps

display_name: lammps

molecular-dynamics

display_name: molecular-dynamics

statistical-mechanics

display_name: statistical-mechanics


en:
The requirement of generating predictions that exactly fulfill the fundamental symmetry of the corresponding physical quantities has profoundly shaped the development of machine-learning models for physical simulations.In many cases, models are built using constrained mathematical forms that ensure that symmetries are enforced exactly. However, unconstrained models that do not obey rotational symmetries are often found to have competitive performance, and to be able to \emph{learn} to a high level of accuracy an approximate equivariant behavior with a simple data augmentation strategy.In this paper, we introduce rigorous metrics to measure the symmetry content of the learned representations in such models, and assess the accuracy by which the outputs fulfill the equivariant condition.We apply these metrics to two unconstrained, transformer-based models operating on decorated point clouds (a graph neural network for atomistic simulations and a PointNet-style architecture for particle physics) to investigate how symmetry information is processed across architectural layers and is learned during training.Based on these insights, we establish a rigorous framework for diagnosing spectral failure modes in ML models. Enabled by this analysis, we demonstrate that one can achieve superior stability and accuracy by strategically injecting the minimum required inductive biases, preserving the high expressivity and scalability of unconstrained architectures while guaranteeing physical fidelity.

This record includes sofware and instructions on how to reproduce the main results of this manuscript.

en: How unconstrained machine-learning models learn physical symmetries

atomistic-simulations

display_name: atomistic-simulations

cscs

display_name: cscs

equivariance

display_name: equivariance

erc

display_name: erc

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning

snsf

display_name: snsf


en:
The Density-Functional Toolkit (DFTK) is a recent plane-wave density-functional theory (DFT) code, written with a flexible architecture and in a high-level language to facilitate development of new methods and support fundamental mathematical research on DFT. DFTK has been instrumental in a good dozen of recent papers, and its core DFT routines are considered stable. So far a systematic precision benchmark against other codes and approaches had not yet been performed. In this work, we apply the verification protocol established by E. Bosoni et al. [Nat. Rev. Phys. 6, 45-58 (2024)] based on the AiiDA high-throughput engine, to validate the precision of DFTK against the existing all-electron and pseudopotential data. This record contains our results, as well as the scripts needed to reproduce them. Out of the 720 systems, DFTK achieves excellent agreement with the all-electron average (ε<0.06) on 223 systems and good-but-not-excellent agreement (0.06<=ε<0.2) on 371 systems. Comparing DFTK with ABINIT (ABINIT@PW|PseudoDojo-v0.5), the agreement is excellent on 697 systems and good-but-not-excellent on 14 systems; the agreement is bad (ε>=0.2) for Ne and I systems only, likely due to the corresponding pseudopotentials.

This agreement is made possible by a custom "v0.5.1-fix" of PseudoDojo:

The pseudopotential files are truncated at rcut=5.99 bohr.

The UPF files in PseudoDojo v0.5 for Bi, Pb, Po, Rn, Te, Tl were missing the third projector and had to be re-generated with a patched ONCVPSP.

The new pseudopotentials in PseudoDojo v0.5 do not have a recommended Ecut, so we used the recommended Ecut of PseudoDojo v0.4.1 + 20 Ha.

We acknowledge and thank Yihan Wu for the initial versions of aiida-dftk and the DFTK support in aiida-common-workflows.

en: Precision verification results data for DFTK@PW|PseudoDojo-v0.5.1-fix

automation

display_name: automation

equation-of-state

display_name: equation-of-state

pseudopotentials

display_name: pseudopotentials

verification

display_name: verification


en: Chromium oxide (Cr2O3) is widely considered a p-type transparent conducting oxide despite ongoing debate regarding its intrinsic transport character. Here, we resolve this question by computing phonon-limited electron and hole mobilities using the Boltzmann transport equation. We find that electron mobility systematically exceeds hole mobility over a wide temperature range, demonstrating that Cr2O3 is intrinsically n-type. Analysis of scattering mechanisms reveals that scattering with phonons affect electrons and holes similarly, and that the mobility asymmetry originates from the electronic structure, namely the larger effective mass and multi-valley character of the valence band. These results indicate that the commonly observed p-type behavior is likely extrinsic. The intrinsic n-type character, combined with moderate hole mobility, enables ambipolar transport and revises the role of Cr2O3 in transparent electronics. Additionally, the alignment of electrical and magnetic easy axes reveals coupling between charge transport and antiferromagnetic order, opening opportunities for magnetoelectric and spintronic devices

en: Challenging the p-type paradigm: intrinsic n-type mobility in antiferromagnetic Cr2O3

carrier-mobility

display_name: carrier-mobility

magnetism

display_name: magnetism

transparent-conducting-oxides

display_name: transparent-conducting-oxides


en: We perform a first-principles high-throughput screening of the mechanical properties of phonon-mediated superconductors derived from the recent experimentally synthesized superconducting materials database PRX Energy \textbf{4}, 033012 (2025). We develop the aiida-mechanical workflow which combines first-principles calculations of elastic constants and generalized stacking fault energies, to assess the ductility of superconducting candidates. Starting from 250 materials identified with promising superconducting critical temperatures, we computed their elastic tensors to evaluate the bulk modulus, shear modulus, Pugh's ratio, and Pettifor's ratio. To further characterize their plastic deformation behavior, we calculated the stacking fault energy and surface energy for selected materials and slip directions, allowing the estimation of Rice's ratio and related ductility indicators. We find that several new materials simultaneously exhibit high Tc and ductility, including HfPd2Al, TiRuSb, and ZrNi2Ga. This dataset provides the complete computational provenance. Specifically, it contains the fully relaxed crystal structures, Quantum ESPRESSO calculations for all elastic tensor (finite-displacement and Born approximation methodologies) and stacking fault energy workflows, as well as k-point and supercell convergence tests. This work offers a quantitative mapping of mechanical performance across a wide range of superconductors and provides a valuable reference for finding new practical superconducting materials.

en: In search of novel ductile superconductors

ductility

display_name: ductility

elastic-tensor

display_name: elastic-tensor

generalized-stacking-fault-energy

display_name: generalized-stacking-fault-energy

pettifors-ratio

display_name: pettifors-ratio

pughs-ratio

display_name: pughs-ratio

rices-ratio

display_name: rices-ratio

superconductor

display_name: superconductor

surface-energy

display_name: surface-energy


en:
Semilocal density functionals such as the Perdew-Burke-Ernzerhof (PBE) functional notoriously underestimate experimental band gaps. Hybrid functionals address this “band gap problem” by admixing a fraction of Fock exchange to semilocal exchange. The optimal mixing parameter depends on the specific material and can be identified as the inverse dielectric constant (dielectric-dependent hybrid functional, DDH). Here, we show that dielectric constants obtained using the r²SCAN meta-GGA functional are significantly more accurate than dielectric constants obtained using the semilocal PBE functional. We propose the DD-r²SCANH functional, a dielectric-dependent hybrid functional based on r²SCAN. DD-r²SCANH can outperform the standard PBE-based DDH in terms of band gaps and other electronic structure properties. Particularly impressive improvements are obtained for narrow-gap semiconductors such as Ge and InAs, where PBE wrongly predicts a metallic phase, but r²SCAN opens a band gap.

Here, we provide VASP input files for DD-r²SCANH, and further we provide the crystal structures used for our band gap benchmark in POSCAR format.

en: Dielectric-dependent hybrid functional from meta-GGA

density-functional-theory

display_name: density-functional-theory

dft

display_name: dft

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure


en: Ceria-based inverse catalysts derive their functionality from Ce3+ oxygen vacancy chemistry, yet strategies to deliberately control vacancy formation and distribution at metal oxide interfaces remain limited. Here, we demonstrate that aliovalent samarium doping provides a direct and tunable route to vacancy engineering in the prototypical CeO2/Ru(0001) inverse catalyst. Using real-time, nanometer-resolved low-energy electron microscopy in combination with low-energy electron micro-diffraction, X-ray absorption spectroscopy in photoemission electron microscopy, and density functional theory, wedirectly correlate lattice structure, oxidation state, and vacancy formation under reducing conditions. Sm incorporation generates extrinsic oxygen vacancies, which are accompanied by Ce3+ formation, which induces a pronounced lattice expansion that in turn fosters the formation of additional intrinsic oxygen vacancies, substantially enhancing reducibility and stabilizing reduced ceria phases relative to undoped CeO2. The doped system exhibits accelerated reduction kinetics, ordered intrinsic vacancy formation, and modified phase-transition behavior. By establishing how aliovalent dopants govern the vacancy landscape and redox response in inverse ceria catalysts, this work provides atomic-level mechanistic insight and concrete design principles for engineering reducible oxides at metal oxide interfaces with tailored redox properties.

en: Samarium-driven vacancy engineering in CeO2/Ru(0001) inverse catalysts

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).

ceria-inverse-catalysts

display_name: ceria-inverse-catalysts

dftu

display_name: dftu

lanthanide-doping

display_name: lanthanide-doping

leem

display_name: leem

oxygen-vacancies

display_name: oxygen-vacancies

redox-dynamics

display_name: redox-dynamics


en: This dataset supports a spin-polarized density functional theory study examining whether O₂-activation-favorable active-site environments at Fe- and Co-substituted sulfur vacancies in monolayer MoS₂ can produce a comparable response for H-free N₂ adsorption. Side-on and end-on N₂ adsorption branches were evaluated together with projected density of states and crystal orbital Hamilton population analyses to distinguish coordination geometry, thermodynamic preference, and intramolecular bond weakening. A perpendicular electric field and K/Na co-adsorption were further considered as physical and chemical perturbations. The calculations show that shared side-on coordination does not produce O₂-like N₂ activation, while the thermodynamically preferred end-on branch retains a shorter and stronger N–N bond. Alkali co-adsorption substantially enhances N–N bond weakening but does not eliminate the O₂–N₂ activation gap or reverse the end-on thermodynamic preference. The archive provides the optimized structures, representative computational input and output data, and numerical source data supporting the reported analyses.

en: Alkali co-adsorption enhances bond-resolved N2 activation at O2-active MoS2 single-atom sites

materialscloud

en: Materials Cloud

alkali-promotion

display_name: alkali-promotion

cohp

display_name: cohp

nitrogen-activation

display_name: nitrogen-activation

single-atom-catalysis

display_name: single-atom-catalysis


en: The FAIR principles have transformed how computational materials data and workflows are shared, yet existing repositories can only serve pre-computed entries — their coverage is perpetually incomplete and cannot adapt to new questions on demand. We built OptiMat Alloys, a large-language-model-powered conversational agent for multi-principal element alloy exploration, on three pillars: a living database that stores every calculation with provenance, low-barrier accessibility through a web interface that requires zero programming expertise, and built-in uncertainty quantification via cross-potential and cross-configuration validation. By coupling foundational machine-learning interatomic potentials that span nearly the entire periodic table with natural-language interaction, the agent enables targeted, on-demand computation guided by the user's own domain knowledge — extending FAIR from pre-computed repositories to on-demand knowledge generation, and making computational alloy screening accessible to any materials scientist.

en: OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent

envi

en: Environment

fair-principle

display_name: fair-principle

llm-agent

display_name: llm-agent

materials-informatics

display_name: materials-informatics

multi-principal-element-alloys

display_name: multi-principal-element-alloys

universal-machine-learning-potentials

display_name: universal-machine-learning-potentials


en: In this study, we employ density functional theory calculations to investigate the reaction pathways of CO₂ conversion to CH₄ on hydrogen-deficient HB sheets, where H-vacancy sites serve as the active centers for CO₂ activation. Our results reveal that the conversion of CO₂ proceeds via a CO-mediated dissociative mechanism. We further consider the effect of O* participation as a reactant following C–O bond cleavage, and show that co-adsorbed OH* lowers the barrier for HCO* formation, making the associative pathway more kinetically favorable than the route proceeding via C*. The rate-determining step is identified as the hydrogenation of CH* to CH₂*, with a barrier of 2.02 eV, consistent with the experimentally observed negligible CH₄ yield under dry conditions. These findings provide mechanistic insight and complement existing experimental observations.

en: Carbon dioxide hydrogenation to methane on hydrogen boride sheet: a density functional theory study

dft

display_name: dft

reaction-pathway

display_name: reaction-pathway

vibrational-properties

display_name: vibrational-properties


en: When a superconductor is placed in contact with a normal material, Cooper pairs penetrate the latter and induce superconductivity via the proximity effect. Despite its central role in quantum materials, superconducting devices and topological platforms, a predictive first-principles description of the proximity effect at realistic interfaces has remained computationally prohibitive so far. Here, we fill this gap by developing a Green's-function framework based on real-space dynamical embedding that enables first-principles simulations of superconducting proximity in mesoscopic systems. We show that the proximity effect admits a transparent diagrammatic formulation in terms of normal and anomalous embedding self-energies, which disentangle and quantify the distinct renormalization mechanisms generated by coupling to a superconducting bath. By combining this formalism with recursive schemes, we compute local spectral functions and proximity lengths extending over hundreds of nanometers into the bulk without resorting to thick interface slabs. We deploy the approach on tight-binding models (Qi-Hughes-Zhang and Fu-Kane-Mele), where we analyze mixed-parity superconductivity in topological insulators proximitized by s-wave superconductors, and on first-principles simulations of NbSe2/CrBr3 heterostructures based on density-functional theory and maximally-localized Wannier functions, the latter enabling direct comparison with scanning tunneling spectroscopy experiments. Our work provides a scalable and conceptually unified framework that bridges microscopic electronic structure and mesoscale proximity physics, enabling predictive atomistic simulations of superconducting interfaces.

en: First-principles real-space embedding theory of the superconducting proximity effect

proximity-effect

display_name: proximity-effect

superconductivity

display_name: superconductivity

topological-materials

display_name: topological-materials


en: Magnesium oxide is a paradigmatic ionic oxide, yet its experimentally observed chemical activity remains difficult to reconcile with its wide band gap and non-reducible character. Here, we combine polarization-dependent infrared reflection–absorption spectroscopy with hybrid density functional theory to investigate CO adsorption on welldefined MgO singlecrystal surfaces, explicitly disentangling the roles of surface orientation, point defects, and lowcoordination step-edge sites. Using CO as a site-sensitive vibrational probe, we demonstrate that the reactivity of MgO(100) originates from fourfold-coordinated Mg4c2+ step-edge sites rather than terrace Schottky defects. Distinct vibrational signatures, adsorption geometries, and binding energies are observed in quantitative agreement between experiment and theory. Onthe more open MgO(110) surface, where Mg4c2+ sites are intrinsic to the terrace, a coordinationcontrolled trend emerges: Mg4c2+ sites bind CO more strongly and exhibit higher-frequency bands than Mg5c2+ sites. Analysis of transition dipole moments further reveals a substantial enhancement of infrared intensities at low-coordinated sites, underscoring the need to account for site-specific dipole strengths in spectral interpretation. These results establish extended lowcoordination motifs as the dominant defect sites on MgO surfaces, with direct implications for metal anchoring and the design of defect-engineered oxide catalysts

en: CO adsorption on MgO revisited: Polarization-resolved identification of step edges as the dominant defects on MgO(100) and MgO(110)

active-sites

display_name: active-sites

irras

display_name: irras

magnesium-oxide

display_name: magnesium-oxide

schottky-defects

display_name: schottky-defects

step-edges

display_name: step-edges


en: Heterogeneous catalytic pathways for clean energy conversion involve thousands of elementary steps, but most models involve only a few dozen reactions. We combine extensive density functional theory calculations, machine learning for activation barrier prediction, and human intelligence-inspired elementary reaction enumeration and identification. This enables automated kinetic modeling of CO2 hydrogenation on copper, a key process to produce fuels and chemicals. We construct one of the largest datasets consisting of 152 elementary CO2 reduction reactions and experimentally determine CO2 conversion, finding that even large networks with 100+ reactions are insufficient. In contrast, our approach reveals 9389 elementary reactions, reducing human bias in the reaction pathway. We unravel 40-fold higher CO2 conversion rates, following experimental trends of methanol and CO production. We establish the crucial role of inter-species hydrogen transfer and hydrogenation by molecular H2, a surprising data-driven discovery validated post-facto. The proposed strategy to comprehensively model complex reaction mechanisms will significantly advance catalysis research.

en: Data-driven massive reaction networks reveal mechanistic pathways underlying catalytic CO2 hydrogenation

automated-reaction-enumeration

display_name: automated-reaction-enumeration

machine-learning

display_name: machine-learning

microkinetic-modeling

display_name: microkinetic-modeling

themocatalysis

display_name: themocatalysis


en:
These are the data used in the manuscript titled "First–principles spin–lattice coupling from downfolded electron–phonon interaction" The abstract of the manuscript :

 We present a method to calculate spin-phonon coupling parameters from first-principles perturbation theory by downfolding the electron-phonon coupling (EPC). We exploit the localized nature of magnetic moments and atomic displacements by working in the Wannier representation of the electronic Hamiltonian and the EPC matrix. The spin system is mapped to a classical Heisenberg Hamiltonian, whose parameters are obtained by treating local spin rotations as a perturbation within a Green's-function formalism. The spin and phonon perturbations are connected through the EPC parameters, which enter as lattice-induced perturbations to the tight-binding Hamiltonian. By combining these lattice perturbations with local spin rotations, we obtain real-space derivatives of magnetic exchange parameters without performing displaced magnetic supercell calculations. We illustrate the method on SrMnO3 and show that it can be integrated directly into standard workflows.

en: First–principles spin–lattice coupling from downfolded electron–phonon Interaction

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

spin-phonon-coupling

display_name: spin-phonon-coupling


en: Isolated dual-atom sites constitute a promising yet largely unexplored strategy for directing reaction intermediates in CO₂ hydrogenation to methanol. In this work, we identify isolated Cu+–O–Ga3+ dual-atom motifs and provide a mechanistically consistent interpretation linking them to enhanced methanol synthesis. This finding is elucidated through the combined use of steady-state isotopic transient kinetic analysis (SSITKA) with diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and density functional theory (DFT) calculations. Isolated Cu⁺–O–Ga³⁺ pairs, anchored within Cu–Ga–Mg–Al hydrotalcite–derived catalysts, are enabled by ionic-radius compatibility and the preferential Cu+-Ga³⁺ association within the oxide lattice.  X-ray absorption spectroscopy and deep learning-assisted atomic-resolution HAADF-STEM imaging, combined with statistical pair analysis, provide complementary evidence supporting a statistically relevant population of short-range Cu+–O–Ga3+ pairs. Together with the kinetic, spectroscopic, and theoretical analyses, these findings support Cu+–O–Ga3+ proximal motifs as the most plausible structural origin of the enhanced methanol-forming functionality and provide a rational design principle for improved methanol synthesis catalysts.

en: Isolated Cu⁺–O–Ga³⁺ pairs as structural motifs that enhance CO₂-to-methanol conversion

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).

copper

display_name: copper

gallium

display_name: gallium

isolated-dual-atom-sites

display_name: isolated-dual-atom-sites

methanol

display_name: methanol


en: This record presents a first-principles oxygen K-edge X-ray absorption spectroscopy (XAS) dataset for the Ni-rich layered cathode material NMC811 (Li₁₋ₓNi₀.₈Mn₀.₁Co₀.₁O₂) across three states of charge. Nine NMC811 supercells were considered: three low-energy cation/vacancy configurations at each of three lithiation levels (pristine x = 0.0, half-delithiated x = 0.5, and fully delithiated x = 1.0), each supercell containing 60 transition-metal sites (48 Ni, 6 Mn, 6 Co) and 120 oxygen sites. For every symmetrically inequivalent oxygen site, the O K-edge spectrum was computed with the excited-electron-and-core-hole (XCH) method as implemented in VASP 6.4.2, using the R2SCAN meta-GGA functional and GW-type PAW data sets; spectra were aligned via their total-energy-difference excitation onsets. The dataset contains 923 site-resolved O K-edge spectra together with the ground-state reference structures and the VASP input files (INCAR, POSCAR, KPOINTS) for both the ground-state and the core-hole calculations. The site-resolved spectra encode the spectral signatures of the individual local oxygen environments and can be combined into per-structure spectra or exploited further, for instance for machine-learning-assisted spectral interpretation. Helper scripts and a structure index are included. POTCAR files are not distributed (VASP license); experimental reference spectra used in the associated manuscript are not included and should be obtained from the original publication.

en: Oxygen K-edge X-ray absorption spectroscopy database for NMC811 layered cathode materials

materialscloud

en: Materials Cloud

first-principles-spectra-database

display_name: first-principles-spectra-database

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


en: Lithium halides with the general formula LixMyX6}, where M indicates metal ions and X halide anions are very actively studied as solid-state electrolytes, because of relatively low cost, high stability and Li conductivity. The structure and properties of these halide-based solid electrolytes (HSE) can be tuned by alloying, e.g. using different halides and/or transition metals simultaneously. The large chemical space is difficult to sample by experiments, making simulations based on broadly applicable machine-learning interatomic potentials (MLIPs) a promising approach to elucidate structure-property relations, and facilitate the design of better-performing compositions.Here we focus on the Li3YCl6xBr6(1-x) system, for which reliable experimental data exists, and use the recently-developed PET-MAD universal MLIP to investigate the structure of the alloy, the interplay of crystalline lattice, volume and chemical composition, and its effect on Li conductivity.  We find that the distribution of Cl and Br atoms is only weakly correlated, and that the primary effect of alloying is to modulate the lattice parameter -- although it can also trigger transition between different lattice symmetries. By comparing constant-volume and constant-pressure simulations, we disentangle the effect of lattice parameter and chemical composition on the conductivity, finding that the two effects compensate each other, reducing the overall dependency of conductivity on alloy composition. We also study the effect of Y-In metal substitution finding a small increase in the conductivity for the C2/m phase at 25\% In content, and an overall higher conductivity for the P\bar{3}m1 phase.An extended study of the effect of metal substitution, enabled by the semi-quantitative accuracy of the universal model, shows that the effect of metal-site alloying is small, indicating that alloying can be used to optimize cost, or electrochemical stability, without major impact on bulk conductivity.

en: Mechanistic study of mixed lithium halides solid state electrolytes.

envi

en: Environment

halide-based-sse

display_name: halide-based-sse

machine-learning-potential

display_name: machine-learning-potential

solid-state-electrolytes

display_name: solid-state-electrolytes

universal-machine-learning-potential

display_name: universal-machine-learning-potential


en: This dataset includes the input files, modified EPW code and python code to reproduce the results and recreate the figures from the paper of Nina Girotto Erhardt and Samuel Poncé: "Higher-order nonadiabaticity governs the temperature dependence of the phonon spectrum". Density functional perturbation theory computes phonons within the adiabatic framework. While many studies addressed the failure of the adiabatic approximation, the first-order nonadiabatic phonon spectrum contains certain crucial deficiencies. The first-order approximation is inadequate to describe the temperature dependence of the spectrum, underestimates phonon linewidth in the zone-center, overestimates its nonadibatic correction, and exhibits prominent nonadiabatic splitting. We show how accounting for the electron broadening by going beyond the first-order approximation mitigates these deficiencies  and improves experimental agreement.

phonons

display_name: phonons

spectral-function

display_name: spectral-function


en:
Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and data-generation schemes designed to streamline the parameterization of MLIPs for modeling precipitation in multi-component alloys. We developed an algorithm that enumerates symmetrically distinct transformation pathways connecting chemical decorations on different parent crystal structures. Additionally, we introduce the weighted Kendall-τ coefficient and its semi-grand canonical generalization as metrics for quantifying MLIP accuracy in predicting low-temperature thermodynamics. We apply these approaches to parameterize an MLIP for a dilute Mg-Nd alloy. The resulting potential reproduces the complex early-stage precipitation behavior observed experimentally. Large-scale atomistic simulations reveal competition between order-disorder and structural transformations. Furthermore, these results suggest a continuous transition between high-symmetry hcp and bcc crystal structures during aging heat treatments.

The datasets contain the crystal structure data used to fit the Mg-Nd interatomic potential. The data include bulk Mg and Nd structures in different coordination environments and at varying densities; several point and structural defects of bulk Mg; symmetry-distinct chemical orderings of Mg and Nd on different parent lattices; and several symmetry-unique hcp-to-bcc transformation pathways of different Mg-Nd chemical decorations. A comprehensive overview of each dataset is provided in the paper's supplemental material.

en: Machine learning interatomic potentials for solid-state precipitation

machine-learning

display_name: machine-learning

marvel

display_name: marvel

metallurgy

display_name: metallurgy


en: The determination of magnetic exchange interactions is essential for the quantitative description and predictive modeling of magnetic materials. In this work, we present a neighbor-shell-based screening method for selecting magnetic configurations suitable for extracting Heisenberg exchange parameters beyond nearest-neighbor from density functional theory calculations. Using only the structure, the proposed approach identifies the linear independence of neighbor-shell contributions before any first-principles calculations instead of relying on trial-and-error generation of magnetic configurations. We apply the proposed approach to two representative classes of magnetic configurations: random spin states and spin spirals, and validate its predictions against direct DFT fitting for Fe and MnF$_2$. We show that only parameters obtained when all relevant neighbor-shell contributions are linearly independent remain transferable to other magnetic configurations. This dataset contains the first-principles calculation data and fitting results supporting the study of magnetic-configuration selection for the extraction of Heisenberg exchange interactions.

en: Magnetic-configuration design for reliable Heisenberg exchange parameters

heisenberg-exchange-interactions

display_name: heisenberg-exchange-interactions

neighbor-shell-analysis

display_name: neighbor-shell-analysis

random-spin-states

display_name: random-spin-states

spin-spirals

display_name: spin-spirals


en: Electrical conductivity is a key property for the design of new metallic conductors, but its accurate first-principles prediction is computationally expensive, which has limited its exploration in high-throughput materials screening. In the associated work, we combine machine-learning surrogate models with density-functional theory (DFT) and density-functional perturbation theory (DFPT) to screen approximately 2.8 million inorganic compounds from the Alexandria database for metallic electrical conductivity, using the electron–phonon coupling constant λ from Eliashberg theory as a proxy for scattering strength. From this pool, only the compounds predicted to exceed a high-conductivity threshold were carried forward for explicit electron–phonon coupling calculations and Boltzmann transport theory using the EPW code, solving the iterative linearized Boltzmann transport equation (BTE), narrowing the candidates down to 52 top-performing compounds. This dataset provides the DFT, DFPT, and EPW input and output files for these 52 candidates, together with 24 elemental benchmark metals and 3 spin-orbit-coupling reruns computed for validation.

en: High-throughput study of electrical conductivity in ordered metals

eletrical-conductivity

display_name: eletrical-conductivity

epw

display_name: epw

high-throughput

display_name: high-throughput

quantum-espresso

display_name: quantum-espresso


en: Resonant Raman spectroscopy probes, in a single measurement, how electrons and phonons couple in a material. While density-functional theory (DFT) typically reproduces well phonon frequencies, resonant Raman intensities hinge on electron-phonon matrix elements and electronic transitions that are far more sensitive to the underlying exchange-correlation approximation, yet accessible so far only for a handful of semilocal functionals. Here, we introduce a general finite-difference framework that computes resonant Raman tensors for any electronic-structure method able to deliver forces, eigenvalues, and wavefunctions. Applying it to graphene and monolayer MoS2 with hybrid functionals and meta-GGAs, we show that these methods systematically enhance electron-phonon couplings relative to semilocal DFT, reflecting reduced dielectric overscreening. Accurate intensities require eigenvalues and electron-phonon matrix elements to be treated consistently at the same level of theory; among the approaches tested, hybrid functionals agree best with experiment, opening the door to systematic beyond-DFT Raman characterization of 2D materials.

en: Resonant Raman spectroscopies beyond density-functional theory

beyond-dft

display_name: beyond-dft

electron-phonon-coupling

display_name: electron-phonon-coupling

resonant-raman

display_name: resonant-raman


en: The targeted design of multi-RE-principal-component RE2Si2O7 disilicates ((nRExi)2Si2O7) for environmental barrier coatings (EBCs) applications requires customizing the multi-RE compositions to achieve maximal optimization on the target properties, with a prerequisite to control their high-temperature stabilized phase as β or γ polymorphs. This is challenged by their rich polymorphic phases varied with the elemental properties on the RE cationic sites. In this study, a random forest (RF) model is developed with high accuracy to classify the four types of high-temperature stabilized phase composition − single-β, single-γ, single-δ/mixed δ+γ, and phase separation – identifying the average RE3+ cationic radius ( ) and the deviation of RE3+ cationic radius ( ) as the most influential factors. The well-trained model is validated on the prediction of (Gdx1Hox2Ybx3Lux4)2Si2O7 and (Ndx1Hox2Ybx3Lux4)2Si2O7 systems, supported by experimental characterization of six representative compositions. High-throughput DFT calculations reveal that the formation of their high-temperature stabilized phases correlates with the the low energy costs to accommodate configurational randomness into the multicomponent system, characterized by rapid convergence of the configurational entropy of mixing with increased excitation energy. The design criteria for single-phase β-(nRExi)2Si2O7 and γ-(nRExi)2Si2O7 disilicates are established. This work provides an investigation paradigm enabling the targeted design of (nRExi)2Si2O7 EBCs materials.

en: Machine learning empowered compositional design of multiple rare-earth principal component disilicates for environmental barrier coatings applications

environmental-barrier-coatings

display_name: environmental-barrier-coatings

high-entropy-rare-earth-disilicates

display_name: high-entropy-rare-earth-disilicates

phase-composition

display_name: phase-composition


en: We present an efficient first-principles approach for simulating the nonequilibrium electron dynamic in extended systems beyond the linear regime. The method combines Koopmans-compliant functionals, which provide an accurate description of quasiparticle band structures, with the real-time evolution of the electronic density matrix in a Wannier basis within the Hartree plus screened exchange (HSEX) approximation. The locality of the orbital basis enables physically justified approximations that significantly reduce the computational and memory scaling of the method while preserving accuracy. We benchmark the approach in the linear regime against experimental spectra and reference GW-BSE calculations for systems featuring both weakly and strongly bound excitons. Moving to the nonlinear regime, we investigate high-harmonic generation (HHG) in silicon and lithium fluoride. While in silicon the HHG spectrum is largely governed by the quasi-particle band structure, we find that in materials featuring strong excitonic effect such as LiF the harmonic emission is selectively enhanced at excitonic resonances, suggesting that HHG probes correlated electron-hole excitations rather than solely the quasiparticle band structure. The present framework enables fully ab initio simulations of excitonic effects in nonlinear optical spectra at a significantly reduced computational cost compared to real-time many-body approaches, providing an efficient route to the study of ultrafast and strong-field phenomena in solids.

en: Efficient nonequilibrium electron dynamics from first-principles: leveraging Koopmans spectral functionals and Wannier localization

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).

ab-initio

display_name: ab-initio

electron-dynamics

display_name: electron-dynamics

high-harmonic-generation

display_name: high-harmonic-generation

koopmans-functionals

display_name: koopmans-functionals

nonequilibirum

display_name: nonequilibirum

optical-absorption

display_name: optical-absorption

real-time-dynamics

display_name: real-time-dynamics


en: Polaron formation localizes charge carriers and drives a crossover from band-like to hopping transport in materials. Hopping dynamics can be obtained from DFT supercell calculations of transition states, but these suffer from polaron self-interaction, spurious electrostatics, and poor scaling with polaron size. We introduce a supercell-free framework for ab initio polaron hopping transport based on variational polaron equations and the string method. The approach optimizes transition states between self-trapped polaron states directly in reciprocal space and provides the polaron configurations along the path, enabling evaluation of adiabatic hopping rates and mobilities. We apply the method to LiF and rutile TiO2, revealing multi-step and anisotropic hopping mechanisms. In rutile TiO2, the computed electron-polaron mobility agrees with experiment, whereas band-like Boltzmann transport substantially overestimates the mobility. Our results establish a scalable route to first-principles polaron-hopping dynamics in materials in which charge motion is governed by self-trapping.This archive contains the data necessary to reproduce the results of the "Optimal transition states for polaron hopping transport without supercells" article by Vasilii Vasilchenko, Matteo Giantomassi, Samuel Poncé, and Xavier Gonze.

materialscloud

en: Materials Cloud

first-principles-calculations

display_name: first-principles-calculations

hopping-transport

display_name: hopping-transport

minimum-energy-paths

display_name: minimum-energy-paths

polarons

display_name: polarons


name: Amt für Statistik und Daten, Wahlen & Abstimmungen

de: Parteiparolen von kantonalzürcher Parteien mit Einsitz im Zürcher Kantonsrat zu eidgenössischen und kantonalen Abstimmungen. Abweichende Parolen der jeweiligen nationalen Parteien sind ausgewiesen. Es besteht kein Anspruch auf Vollständigkeit. Bitte senden Sie Ergänzungen zum Datensatz an die unter Kontakt angegebene E-Mail-Adresse.

de: Abstimmungsparolen von kantonalzürcher und nationalen Parteien

de: exekutive abstimmungen kantonalevolksabstimmungen kantonsrat regierungsrat politik eidgenoessischevolksabstimmungen

statistisches-amt-kanton-zuerich

de: Das Amt für Statistik und Daten umfasst das Kompetenzzentrum Daten des Kantons Zürich, organisiert, koordiniert und steuert die politische Teilhabe inkl. der Wahlen und Abstimmungen im Kanton und beobachtet die Entwicklung des Kantons mit statistischen Methoden. Wir versorgen Bevölkerung, Verwaltung, Politik, Gemeinden, Medien, Firmen und sonstige Organisationen mit qualitativ hochstehenden Daten, Information und Fakten zum Kanton Zürich. Inhaltlich arbeiten wir unabhängig und objektiv.

de: Amt für Statistik und Daten Kanton Zürich

political_level: canton

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

value: ZH-Kt Statistisches Amt - DCAT Harvester

gove

de: Regierung und öffentlicher Sektor

en: Government and public sector

fr: Gouvernement et secteur public

it: Governo e settore pubblico

format: CSV

media_type: text/csv

abstimmungen

display_name: abstimmungen

eidgenoessischevolksabstimmungen

display_name: eidgenoessischevolksabstimmungen

exekutive

display_name: exekutive

kantonalevolksabstimmungen

display_name: kantonalevolksabstimmungen

kantonsrat

display_name: kantonsrat

politik

display_name: politik

regierungsrat

display_name: regierungsrat


de: zeigt die nutzbaren Grundwasservorkommen, die oberirdischen Gewässer und Uferbereiche, die mittleren Grundwasserspiegel und die Mächtigkeit des Grundwassers.

de: Grundwasservorkommen

de: gewasserschutz grundwasser hydrologie

buwd-uwe

de:
Zum Leistungsauftrag der Dienststelle Umwelt und Energie gehören die Umweltbeobachtung, Information und Beratung, die Prüfung von umweltrelevanten Vorhaben, der Schutz von Umweltgütern (z.B. Grundwasser), die Kontrolle u.a. von Abfallanlagen und Feuerungen sowie die Förderung von erneuerbaren Energien und Energieeffizienz.

Die Dienststelle umfasst die Abteilungen Gewässer und Boden, Entsorgung und Risiko, Energie, Luft, Lärm & Strahlen sowie die Zentralen Dienste.

en:
The mandate of the Department of Environment and Energy includes environmental monitoring, providing information and advice, reviewing environmentally relevant projects, protecting environmental resources (e.g., groundwater), inspecting waste facilities and combustion plants, among other things, and promoting renewable energy and energy efficiency.

The department comprises the divisions of Water and Soil, Waste Management and Risk, Energy, Air, Noise & Radiation, and Central Services.

fr:
Le mandat de prestations de l'Office de l'environnement et de l'énergie comprend la surveillance de l'environnement, l'information et le conseil, l'examen des projets ayant une incidence sur l'environnement, la protection des ressources environnementales (par exemple les eaux souterraines), le contrôle, entre autres, des installations de traitement des déchets et des installations de combustion, ainsi que la promotion des énergies renouvelables et de l'efficacité énergétique.

L'Office comprend les divisions Eaux et sols, Gestion des déchets et risques, Énergie,  Air, bruit et rayonnements, ainsi que les services centraux.

en: Department of Environment and Energy

de: Dienststelle Umwelt und Energie

de: Kanton Luzern, Abteilung Geoinformation

de: geocat.ch Permalink

fr: geocat.ch permalien

it: geocat.ch link permanente

en: geocat.ch permalink

spatial: Kanton Luzern

envi

en: Environment

de: Produktansicht Geodatenshop

de: Datenshop

value: Kt. LU UWE - geocat harvester


name: Centre de compétences et de coordination OGD / Kompetenz- und Koordinationszentrum für OGD

fr: Ce jeu de données présente le nombre de décès dans le canton de Fribourg depuis 2000, par semaine, classe d'âge (0-64, 65+) et genre.Les données proviennent de l'Office fédéral de la statistique (OFS) et sont mises à jour chaque semaine, en incluant uniquement les décès survenus au moins 15 jours auparavant. Des modifications peuvent survenir à tout moment en raison de décès signalés ultérieurement.Pour plus d'informations sur la Statistique du mouvement naturel de la population (BEVNAT) : [https://www.bfs.admin.ch/bfs/fr/home/statistiques/population/enquetes/bevnat.assetdetail.35996254.html](https://www.bfs.admin.ch/bfs/fr/home/statistiques/population/enquetes/bevnat.assetdetail.35996254.html)

fr: age deces hebdomadaire genre

ssd

fr: Le Service de la statistique et de la donnée est le centre de compétence cantonal pour la statistique publique. Il collecte, analyse et diffuse des données statistiques sur la situation et l’évolution du canton, soutient les services de l’État dans la gestion des données et coordonne la diffusion des données administratives en libre accès.

en: The Statistics and Data Service is the cantonal competence center for public statistics. It collects, analyzes, and disseminates statistical data on the situation and development of the canton, supports state services in data management, and coordinates the dissemination of administrative data in open access.

de: Das Amt für Statistik und Daten ist das kantonale Kompetenzzentrum für die öffentliche Statistik. Es erhebt, analysiert und verbreitet statistische Daten über die Lage und die Entwicklung des Kantons, unterstützt die Dienststellen des Staats bei der Verwaltung ihrer Daten und koordiniert die Veröffentlichung der offenen Verwaltungsdaten.

it: Il Servizio Statistiche e Dati è il centro di competenza cantonale per la statistica pubblica. Raccoglie, analizza e diffonde dati statistici sulla situazione e l'evoluzione del cantone, supporta i servizi statali nella gestione dei dati e coordina la diffusione dei dati amministrativi ad accesso aperto.

de: Amt für Statistik und Daten Freiburg

en: Statistics and Data Service Fribourg

fr: Service de la statistique et de la donnée Fribourg

it: Servizio Statistiche e Dati Friburgo

de: ssd

value: DCAT Harvester Kt. FR

soci

de: Bevölkerung und Gesellschaft

en: Population and society

fr: Population et société

it: Popolazione e società

heal

de: Gesundheit

en: Health

fr: Santé

it: Salute

fr: Décès par semaine selon le genre, dès 2000 (csv)

fr: csv

fr: Décès par semaine selon le genre, dès 2000 (json)

fr: json

format: JSON

media_type: application/json

fr: Décès par semaine selon le genre, dès 2000 (jsonl)

fr: jsonl

fr: Décès par semaine selon le genre, dès 2000 (parquet)

fr: parquet

format: PARQUET

fr: Décès par semaine selon le genre, dès 2000 (rdfxml)

fr: rdfxml

format: RDF XML

media_type: application/rdf+xml

fr: Décès par semaine selon le genre, dès 2000 (jsonld)

fr: jsonld

format: JSON-LD

fr: Décès par semaine selon le genre, dès 2000 (turtle)

fr: turtle

format: RDF Turtle

media_type: text/turtle

fr: Décès par semaine selon le genre, dès 2000 (n3)

fr: Décès par semaine selon le genre, dès 2000 (xlsx)

fr: xlsx

format: XLS

age

display_name: age

deces

display_name: deces

genre

display_name: genre

hebdomadaire

display_name: hebdomadaire


de: Offizieller Datensatz für das «Verzeichnis der Gebäudeadressen». Das Verzeichnis der Gebäudeadressen beinhaltet sämtliche offiziellen und damit behördenverbindlichen Adressen von Liechtenstein. Alle Gebäude müssen eine oder mehrere eindeutige Adressen aufweisen. Gebäude mit Wohnnutzung, Arbeitsstätten sowie Gebäude von allgemein öffentlichem Interesse müssen zwingend im offiziellen Verzeichnis der Gebäudeadressen erscheinen. Grundlage für das offiziellen Verzeichnis der Gebäudeadressen bildet die amtliche Vermessung.

en: Official data set for the "Directory of building addresses". The directory of building addresses contains all official and thus officially binding addresses in Liechtenstein. All buildings must have one or more unique addresses. Buildings with residential use, workplaces and buildings of general public interest must appear in the official directory of building addresses. The official list of building addresses is based on the official cadastral survey.

de: Offizielles Verzeichnis der Gebäudeadressen Liechtenstein

en: Official directory of building addresses Liechtenstein

en: addresses national road opendata official-geodata building-address high-value-dataset-hvd cadastral-surveying building nomenclature

de: adressen national strasse opendata geobasisdaten gebaudeadresse high-value-dataset-hvd amtliche-vermessung-av gebaude nomenklatur

fr: adresses nationales route opendata geodonnees-de-base adresse-de-batiment high-value-dataset-hvd mensuration-officielle-mo batiment nomenclature

it: indirizzi nazionali strada opendata geodati-di-base indirizzo-di-edificio high-value-dataset-hvd misurazione-ufficiale-mu edificio nomenclatura

amt-fuer-tiefbau-und-geoinformation-fl

en: The Civil Engineering and Geoinformation Office is responsible for the construction,  operation and maintenance of main roads, the coordination of geodata infrastructure, official surveying and the catastre of Public Law Restrictions on Landownership (PLR-cadastre).

de: Das Amt für Tiefbau und Geoinformation ist zuständig für den Bau,  Betrieb und Unterhalt der Landstrassen, die Koordination der Geodateninfrastrukur, die Amtliche Vermessung und den ÖREB-Kataster.

de: Amt für Tiefbau und Geoinformation

fr: Amt für Tiefbau und Geoinformation

it: Amt für Tiefbau und Geoinformation

fr: geocat.ch permalien

spatial: Liechtenstein

regi

de: Regionen und Städte

en: Regions and cities

fr: Régions et villes

it: Regioni e città

envi

de: Umwelt

fr: Environnement

de: Datenausgabestelle der Liechtensteinischen Landesverwaltung

en: Data Distribution Office of the Liechtenstein Administration

de: Homepage ATG, Datenausgabe

format: HTML

media_type: text/html

de: Geodatenportal der Liechtensteinischen Landesverwaltung

en: Geoportal of the Liechtenstein National Administration

de: Download Bereich des Geodatenportals

de: OGC WMS Dienst der Landesverwaltung

fr: OGC WMS Dienst

it: OGC WMS Dienst

en: OGC WMS Service

format: WMS

de: Dateidownload über das Geoportal Liechtenstein

fr: Dateidownload Geoportal Liechtenstein

en: Download on the Geoportal Liechtenstein

de: Download über das Geodatenportals

value: Liechtenstein - Geocat Harvester