en:
We establish a set of exact sum rules that relate the interatomic force constants to the frequency-dependent electromagnetic susceptibility of a solid or molecule, thereby generalizing the long-established principles of rototranslational symmetry to the nonadiabatic regime. Crucially, we show that in practical numerical implementations these sum rules are violated, unless special precautions are taken in the treatment of the atomic pseudopotentials. We solve these issues once and for all by correctly adapting the pseudopotential to the motion of the corresponding nucleus, with a velocity dependence of the nonlocal operator. This prescription restores the correct Galilean covariance of the Schr"odinger equation, and the expected identity between mechanical rototranslations and electromagnetic perturbations. These results conclusively fix a number of worrisome inconsistencies that were pointed out over the years in the context of linear-response theory restoring, e.g., the validity of the Larmor theorem, and the equivalence between the inertial and electrical definitions of the Drude weight in metals.

This dataset contains the input and output files used to produce the results presented in the manuscript.

en: Rototranslational sum rules for nuclear dynamics via traveling pseudopotentials

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).

density-functional-theory

display_name: density-functional-theory

nonadiabatic-regime

display_name: nonadiabatic-regime

pseudopotentials

display_name: pseudopotentials


$oid: OBSOLETE

en: We present a density-functional theory study of the effects of oxygen vacancies on the structural and electronic properties of vanadium dioxide (VO2). Our motivation is the reported suppression of the metal-insulator transition by oxygen vacancies and the lack of a clear consensus on its origin. We use the DFT+V method with a static intersite vanadium-vanadium interaction term, V, to calculate the properties of the oxygen-deficient metallic rutile and insulating monoclinic M1 phases of VO2 on the same footing. We find that oxygen vacancies induce local distortions in the M1 phase, but do not destroy the dimerization usually associated with the insulating behavior. In spite of this, we find that the M1 phase becomes metallic as a result of the partial filling of the conduction band due to a rigid-band-like doping effect.

en: Oxygen vacancies in vanadium dioxide: A DFT+V study

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

dft

display_name: dft

dftuv

display_name: dftuv

vacancy

display_name: vacancy


en: We present a high-throughput computational screening for fast lithium-ion conductors aimed at identifying candidate materials for application in all-solid-state electrolytes. Beginning with more than 30,000 experimentally reported Li-containing structures drawn from the Inorganic Crystal Structure Database, the Materials Platform for Data Science (Pauling file), and the Crystallography Open Database, we apply a series of automated structural and compositional filters to obtain 1500 unique crystal structures suitable for electronic-structure calculations which yields nearly 1,000 electronic insulators. We then estimate Li-ion diffusivities for these insulating candidates using molecular dynamics simulations at multiple temperatures. To make simulations computationally feasible at this scale while preserving near first-principles fidelity, we employ a foundational machine-learned interatomic potential which is carefully fine-tuned on relevant Li-chemistries. We discuss the details of the fine-tuning strategies and data-consistency considerations required to obtain a very accurate and robust model. From the MD results, we identify three particularly promising novel oxide candidates for room-temperature solid-state electrolytes, including LiSn2(AsO4)3, LiIn(IO3)4, and LiB6S4(Cl3O4)2, which all exhibit ionic conductivity greater than 1 mS/cm at room temperature with diffusion barrier between 0.20 and 0.25 eV. We provide the full screening protocol as well as a prioritised list of materials for experimental follow-up, demonstrating the value of provenance-aware, ML-driven simulations in accelerating solid-state electrolyte discovery.

en: High-throughput screening for solid-state Li-ion conductors combining machine learning and first-principles calculations

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

li-ion-conductors

display_name: li-ion-conductors

molecular-dynamics

display_name: molecular-dynamics

solid-state-electrolytes

display_name: solid-state-electrolytes


en: Materials exhibiting coexisting and coupled ferroelectric and magnetic orders are rare. Here we show, using density functional theory calculations, that introducing FeLa antisite defects into pristine, non-ferroelectric, G-type antiferromagnetic (AFM) LaFeO3 renders the material simultaneously ferroelectric and a defect-induced local ferrimagnetic phase. We assess the thermodynamic stability of FeLa antisites and identify synthesis conditions under which they are likely to form. Nudged elastic band calculations indicate an accessible barrier for ferroelectric polarization switching. Interestingly, we observe an inverse magnetoelectric coupling in which the ferroelectric polarization controls the magnetic order. We further propose two defect-mediated magnetization-control mechanisms enabled by ferroelectric switching in LaFeO3, both involving a 180° reversal: (i) switching the FeLa antisite moments while the pristine G-type AFM order remains unchanged, and (ii) of the pristine G-type AFM order while the FeLa antisite moments are pinned by a static magnetic field. In both cases, exchange coupling between the antisite and pristine Fe sublattices enables magnetization switching, highlighting a defect-engineering route to electrically controlled magnetic states for data storage.

en: Multiferroicity and 180° magnetization switching in LaFeO3 via antisite defects

antisite-defect

display_name: antisite-defect

multiferroic

display_name: multiferroic


en: Sodium (Na) metal batteries are considered promising solutions for next-generation electrochemical energy storage because of their low costs and high energy densities. However, the slow Na dynamics result in unfavourable Na deposition and dendrite growth, which compromise cycling performance. Here we propose a nanotwinned alloy strategy prepared by high-pressure solid solution followed by Joule-heating treatment to address sluggish Na dynamics, achieving homogeneous Na deposition. By employing cost-effective Al-Si alloys for validation, Si solubility of 10 wt.% is extended through a high-pressure solid solution, and nanotwinned-Si particles, with a volume fraction of 82.7%, are subsequently formed through Joule-heating treatment. The sodiophilic nanotwinned-Si sites exhibit a high diffusion rate, which reduces the nondimensional electrochemical Damköhler number to far below 1, shifting the diffusion-controlled deposition behavior to reaction-controlled deposition. This transition facilitates spherical Na deposition and dendrite-free growth, allowing a symmetric cell to achieve stable Na plating/stripping over 5300 hours at 5 mA cm⁻² with a cumulative capacity of 13.25 Ah cm⁻². This strategy is also demonstrated in another CuAg system with nanotwinned Ag structures.The record contains data that support the findings described in the publication.

en: A nanotwinned-alloy strategy enables fast sodium deposition dynamics

dynamics

display_name: dynamics

nanotwinned-alloy

display_name: nanotwinned-alloy

sodium-metal-battery

display_name: sodium-metal-battery


en:
This dataset contains the minimal set of AiiDA calculations supporting the results presented in the paper "Multiband Anisotropic Superconductivity in 2H-NbSe₂ Revealed by Ab-Initio Kohn--Sham Bogoliubov--de Gennes Calculations".

It includes the fully relativistic Kohn--Sham Bogoliubov--de Gennes (KS-BdG) calculations for pristine bulk 2H-NbSe₂ performed with the JuKKR code and the AiiDA-KKR plugin. The calculations use a single, physically transparent pairing strength λ_Nb applied only to the Nb sites and quantitatively reproduce the experimentally observed multigap density of states with coherence peaks at 1.85, 1.55, and 1.4 meV together with the ~30% momentum-dependent superconducting gap anisotropy. The archive provides the normal-state electronic structure and the self-consistent superconducting state, including all input parameters required for full reproducibility.

en: Multiband anisotropic superconductivity in 2H-NbSe2 revealed by ab-initio Kohn--Sham Bogoliubov--de Gennes calculations

aiida

display_name: aiida

aiida-kkr-ksbdg

display_name: aiida-kkr-ksbdg

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

superconductivity

display_name: superconductivity


en: We develop an extensive dataset of Allen-Cahn simulations of crystal growth in 2D, including kinetic anisotropy to produce hexagonally faceted profiles, and a variable supersaturation parameter to control the growth rates and local faceting. A phase-field numerical approach based on a finite-difference explicit time integration scheme is used to generate such evolution sequences. Two Convolutional Recurrent Neural Network architectures are then exploited to train quantitative surrogate models reproducing such evolution sequences conditioned to the supersaturation value. The first infers the supersaturation parameter implicitly from an input mini-sequence of a few evolution frames while the second takes it as an explicit input along with a single initial frame. The predictive performances of the two models are evaluated by extensive testing on dedicated testsets. In particular, the analysis includes both the testing on a large dataset of the same kind of the training one, and on dedicated datasets with assigned values of the supersaturation parameter. While the architecture using the parameter as explicit input yields the best performance, with quantitative reproduction of the true phase-field solutions, the mini-sequence model produce satisfactory results only by taking comparatively larger dataset sizes. The generalization capabilities of the trained models are finally inspected by considering larger domains, longer evolution times and different initial coverages.

en: Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioning

allen-cahn-equation

display_name: allen-cahn-equation

convolutional-recurrent-neural-network

display_name: convolutional-recurrent-neural-network

crystal-growth

display_name: crystal-growth

surrogate-model

display_name: surrogate-model


en: In rarefied atmospheric environments, where continuum fluid dynamics breaks down, aerodynamic drag is governed by gas–surface momentum exchange, making surface structure and chemistry key design knobs. Using molecular dynamics simulations, we show that coating the α-Al2O3(0001) surface with graphene markedly reduces the tangential momentum accommodation coefficient (TMAC) of N2, shifting scattering toward more specular reflection and thereby lowering drag; we further benchmark this response against graphite. The reduction is robust and strengthens up to 900 K. While structural defects can increase TMAC – and hence drag – via defect-induced corrugation and local atomic and electronic rearrangements, graphene retains near-ideal low-TMAC performance at experimentally relevant defect densities. These results identify graphene as a thermally resilient, low-drag coating for aerospace-relevant surfaces in rarefied flows.

en: Defects, corrugation and temperature govern rarefied-air drag on graphene coatings

accommodation-coefficients

display_name: accommodation-coefficients

air

display_name: air

alumina

display_name: alumina

coating

display_name: coating

defects

display_name: defects

drag

display_name: drag

graphene

display_name: graphene

graphite

display_name: graphite

rarefied-gas

display_name: rarefied-gas


en: The dataset provides records of creep properties for structural materials used in nuclear applications, focusing on the influence of specimen dimensions on time-dependent deformation behavior. It was developed through an extensive literature search of peer-reviewed articles, technical reports, and institutional databases, including contributions from 50 publications and databases. The search targeted studies presenting quantitative creep data for both standard and sub-sized specimens, with detailed information on testing conditions, specimen geometries, and material compositions. The compiled database contains 1,413 creep testing records covering parameters that include alloy type and grade, chemical composition, thermal and mechanical treatment history, irradiation conditions, specimen geometry and dimensions, and measured creep properties. The dataset encompasses material classes such as stainless steels (A709, SS304, SS316), reactor pressure vessel steels (SA533, SA508), nickel-based alloys (IN600, IN617), and zirconium alloys (Zr-2, Zr-3, Zr-4). Experts in materials science reviewed and validated all records, verifying material specifications, treatment conditions, and testing parameters for consistency and accuracy.

en: Creep dataset from peer-reviewed literature of nuclear structural materials

materialscloud

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

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

creep-test

display_name: creep-test

nuclear-structural-materials

display_name: nuclear-structural-materials

size-effect

display_name: size-effect

sub-sized-specimens

display_name: sub-sized-specimens


en: Cs2KInI6 is a promising lead-free halide double perovskite with a calculated direct band gap of 1.94 eV, ideal for solar cell applications. Our first-principles calculations reveal that its cubic phase (Fm3̅m) is dynamically unstable. Using an accelerated machine learning approach, we identify 42 dynamically stable structures and further validate these findings using first-principles calculations on 11 of these. The most stable phase has Cmc21 symmetry with 20 atoms/unit cell. It lies 13 meV/atom above the convex hull but lacks octahedral cation coordination. The most stable perovskite-like structure has P3̅ symmetry with 10 atoms/unit cell and low octahedral connectivity. Structure-property trade-offs are highlighted, with calculated distortions generally widening the band gap, shifting it from direct to indirect, and flattening the band edges. This work showcases the synergy of genetic algorithms, machine-learned potentials, and first principles validation for discovering stable, complex materials. This archive contains all our input, output files, the data and scripts.

en: From symmetry to stability: structural and electronic transformation in Cs₂KInI₆

materialscloud

en: Materials Cloud

first-principles

display_name: first-principles

halide-double-perovskite

display_name: halide-double-perovskite

machine-learned-interatomic-potentials

display_name: machine-learned-interatomic-potentials

phonons

display_name: phonons

polymorphs

display_name: polymorphs


en: Materials underpin modern technologies, from energy harvesting, storage, and conversion to information and communication technologies. Their functionality is often governed by the interplay between competing phases, as thermodynamic behavior shapes microscopic properties and ultimately determines technological performance; for instance, the light absorption of inorganic metal-halide perovskites in solar cells. Accurately predicting crystal thermodynamics, however, remains a major challenge for computational approaches because strong anharmonic effects require extensive sampling of the potential energy surface. Here, we present an on-the-fly Bayesian framework, combined with the stochastic self-consistent harmonic approximation, for learning first-principles interatomic potentials. This approach enables the prediction of thermodynamic properties over a broad temperature range with first-principles accuracy while requiring training on only a few tens to a few hundreds of atomic configurations. To demonstrate its power, we investigate the thermodynamic and dynamical properties of Li2O, α-CsPbI3, and δ-CsPbI3, requiring only 44, 256, and 50 total-energy calculations, respectively. Notably, we show that this framework accurately captures the phase diagram of CsPbI3, which explains its spontaneous degradation into the non-absorbing yellow phase, predicting the transition temperature with remarkable accuracy and efficiency. More broadly, the method presented opens a novel route toward accelerated materials engineering under realistic conditions for a wide range of technologically relevant applications, including solid-state batteries, optoelectronic devices, and memristors.

en: Predicting challenging phase transitions with Bayesian active learning

envi

en: Environment

active-learning

display_name: active-learning

anharmonicity

display_name: anharmonicity

machine-learning

display_name: machine-learning

phase-transitions

display_name: phase-transitions

thermodynamics

display_name: thermodynamics


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

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: The localized states in the band gap of amorphous phase change alloys like Ge2Sb2Te5 control the electrical conduction via the Poole-Frenkel mechanism. Understanding the origin of in-gap states and their evolution in time during aging of the glass is therefore important for the control of the resistance drift in phase change memory devices. Here, we use a machine learning interatomic potential to generate several models of Ge2Sb2Te5 whose electronic structure is then analyzed within density functional theory with a hybrid functional. A detailed statistical analysis of the structural motifs on which the in-gap states are localized, reveals that the vast majority of in-gap states involve wrong bonds (homopolar or Ge-Sb bonds) often accompanied by Ge in tetrahedral configurations or overcoordinated Ge and Sb atoms. Metadynamics simulations mimicking glass aging support the picture that structural relaxations lead to the depletion of in-gap states and then to an increase of resistance. The simulations thus provide important insights for the mitigation of the resistance drift in phase change memory devices.

en: On the origin of in-gap states in amorphous Ge2Sb2Te5

electronic-memories

display_name: electronic-memories

electronic-structure

display_name: electronic-structure

molecular-dynamics

display_name: molecular-dynamics

phase-change-materials

display_name: phase-change-materials


en: AMaRaNTA stands for Automating Magnetic paRAmeters iN a Tensorial ApproachThe aim of the AMaRaNTA package is to implement and automatize the widely used energy-mapping method, namely the 4-state method using density functional theory by removing the laborious, multi-step manual procedures. Based on the AiiDA framework, it extracts full spin-orbit coupling included exchange and anisotropy parameters for 2D magnets. In its current implementation (full workchain publicly available at https://github.com/AMaRaNTA-code), AMaRaNTA returns the nearest-neighbour exchange tensor, complemented by scalar parameters for second- and third-nearest-neighbour exchange interactions as well as single-ion anisotropy.Present repository is to make available the results obtained using this workchain run on a set of 29 materials, a representative subset of the Materials Cloud 2D Structure database.

en: AMaRaNTA: automated first-principles exchange parameters in 2D magnets [Results]

aiida

display_name: aiida

dft

display_name: dft

magnetic-exchanges

display_name: magnetic-exchanges


en: The precise atomic-scale structure around Eu2+ activators in the β-Si6-zAlzOzN8-z:Eu2+ commercial green phosphor remains elusive. We use the first-principles ΔSCF excited-state method, embedding of the interatomic force constants for supercells up to 3501 atoms, and Huang-Rhys theory to clarify this issue. Monte Carlo exploration is used to identify representative low-energy structural models spanning different levels of Al/O concentration z.  For the lowest-energy structure at low z, our computed photoluminescence spectrum reproduces the experimental vibronic peaks at 6K with excellent agreement in peak positions and intensities, validating the Eu-N9 coordination model with Al, O, and Eu confined to the same crystallographic plane. Analysis of the low-energy structures reveals that the electron-phonon coupling is weak (S ≈ 2.15) with a robust characteristic phonon signature across different Al/O arrangements, explaining the surprising persistence of resolved phonon replicas with increasing z. We explain the experimentally observed red-shift of emission with increasing z through systematic trends in zero-phonon line energies, modest increases in Huang-Rhys factors, and larger configurational diversity at higher compositions.

en: Micro-environment of the Eu interstitial in the β-Si6-zAlzOzN8-z:Eu2+ green phosphor

electron-phonon

display_name: electron-phonon

phosphors

display_name: phosphors

vibronic

display_name: vibronic


en: This repository accompanies the manuscript “ChemFit: A framework for automated high-dimensional model parameter optimization” and contains the source code, input structures, and simulation outputs required to reproduce the examples presented in the study. ChemFit is a Python framework for the definition and massively concurrent evaluation of simulation-based objective functions for use with gradient-free and black-box optimization algorithms.The archive includes snapshots of the source code used in the study together with the corresponding simulation metadata, logfiles, reference structures, and initial configuration files necessary to reproduce the reported optimization workflows and results.

en: Data availability for "ChemFit: a framework for automated high-dimensional model parameter optimization"

biocondensates

display_name: biocondensates

chemfit

display_name: chemfit

coarse-grained-model

display_name: coarse-grained-model

computational-physics

display_name: computational-physics

lennard-jones

display_name: lennard-jones

parameter-optimization

display_name: parameter-optimization

water-model

display_name: water-model


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


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

en: Materials Cloud

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:
This dataset contains the data (as well as the code required to generate the data) for the numerical experiments of our manuscript "Quantum Finite Temperature Lanczos Method".

Abstract:

The computation of thermal properties of quantum many-body systems is a central challenge in our understanding of quantum mechanics. We introduce the Quantum Finite Temperature Lanczos Method (QFTLM), which extends the finite-temperature Lanczos method to quantum computers by combining real-time quantum Krylov methods with efficient preparation of typical states for trace estimation. This approach enables the computation of thermal expectation values while avoiding the exponential scaling inherent to classical exact simulation techniques. Numerical experiments on the transverse-field Ising model show that QFTLM can reproduce thermal observables over a wide temperature range. We further analyze the influence of Krylov dimension, number of trace-estimator states, and Trotter error, and show that suitable regularization is essential for robustness in noisy settings. These results establish QFTLM as a promising framework for finite-temperature quantum simulation.

en: Data for manuscript on the quantum finite temperature Lanczos method

envi

en: Environment

finite-temperature

display_name: finite-temperature

krylov

display_name: krylov

lanczos

display_name: lanczos

quantum

display_name: quantum

simulation

display_name: simulation


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

dft

display_name: dft

hexagonal-ge

display_name: hexagonal-ge

model-bse

display_name: model-bse

optoelectronic-materials

display_name: optoelectronic-materials

polytypes

display_name: polytypes


en: Maximally-localized Wannier functions are quantum wavefunctions resembling atomic orbitals that are used to describe electrons in condensed matter. Since their introduction in 1997, these functions have become ubiquitous in ab initio materials simulations, including applications in linear-scaling methods, strongly correlated electron systems, quantum transport, electron-phonon interactions, and topological materials. Despite their widespread adoption in a vast software ecosystem, Wannier functions have not yet attained their fullest potential in the presence of entangled bands, as their optimization remains challenging and labor-intensive. Here, we introduce a universal meta-optimization method that leverages workflow abstraction and machine-learning techniques like differential evolution and Bayesian optimization to generate globally optimized Wannier functions without human intervention. We demonstrate this approach through three applications: (i) autonomous interpolation of entangled band structures with millielectronvolt accuracy starting from coarse Brillouin zone grids, (ii) thousand-fold acceleration of fully ab initio Boltzmann transport calculations via the use of minimal coarse Brillouin zone grids, and (iii) ultrafast high-throughput calculations of high-precision Wannier functions for large materials libraries. This work brings calculations that previously required supercomputers within the reach of personal computers.The data generated in this work and reported in the manuscript is attached in the Data.tar.gz with full description in the Readme.md.

en: Meta-optimization of maximally-localized Wannier functions

machine-learning

display_name: machine-learning

meta-optimization

display_name: meta-optimization

wannier-functions

display_name: wannier-functions


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: Functionalizing Silicon Nanowires (SiNWs) through covalent attachment of organic molecules offers diverse advantages, including surface passivation, introduction of new functionalities, and enhanced material performance in applications like electronic devices and biosensors. Given the wide range of available functional molecules, systematic large-scale screening is crucial. Therefore, we developed an automated computational workflow using Python scripts in conjunction with the AiiDa framework to explore structural configurations of functional molecules adsorbed onto silicon surfaces. This workflow generates multiple adhesion configurations corresponding to different binding orientations using surface and functional molecule structures as inputs. This dataset contains data related to the structural optimization of molecules with single, double, and triple carbon-carbon bonds attached to the nanowire surface in various adhesion configurations. We describe the chemisorption on SiNWs using the slab models for the Si facets since our reference are samples with diameters of SiNWs around 50 nm, while the quantum confinement effects are important for diameters below 10 nm. For each configuration, structural characterization was conducted by calculating quantities including the bond distance between the two carbons closest to the surface and their respective bond angle relative to the z-axis, the carbon-silicon bond distance and its respective bond angle relative to the z-axis, along with the molecule's rotation angle in the xy plane. The values obtained are summarized in the main folder. The version v1 of dataset contains data related to the Si(111) surface and alkanes, alkenes, and alkynes with lengths from C2 to C10. The version v2 and v3 include also the moieties from C12 to C18. The version v4 extends the dataset with the data related to the Si(110) surface and alkanes, alkenes, and alkynes with lengths from C2 to C18. The version v5 also includes the optimized structures of SiNWs oriented along ⟨112⟩, exposing Si(110) and Si(111) facets, and featuring three different sizes: small (d = 0.7 nm), medium (d = 1.7 nm), and large (d = 2.6 nm). In this version (v6) the relaxation calculations for all the considered moieties are provided. For each system the most stable configuration is identified, and the analysis of the electronic properties is conducted in the listed references.

en: Density functional theory study of silicon nanowire surfaces functionalized by grafting organic molecules

aliphatic-molecules

display_name: aliphatic-molecules

nanowires

display_name: nanowires

silicon

display_name: silicon

surface-adsorption

display_name: surface-adsorption


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

generative-ai

display_name: generative-ai

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

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).

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: Spin Hall effect (SHE) in two-dimensional (2D) materials is promising to effectively manipulate spin angular momentum and identify topological properties. In this work, we implemented an automated Wannierization with spin-orbit coupling on 426 non-magnetic monolayers including 210 metal and 216 insulators. Intrinsic spin Hall conductivity (SHC) has been calculated to find candidates exhibiting novel properties. We discover that Y₂C₂I₂ has an unconventional SHE with canted spin due to low crystal symmetry, Ta₄Se₂ is a metallic monolayer with exceptionally high SHC, and the semi-metal Y₂Br₂ possesses efficient charge-to-spin conversion induced by anti-crossing in bands. Moreover, quantum spin Hall insulators are investigated for quantized SHC. The present work provides a high-quality Wannier Hamiltonian database of 2D materials, and paves the way for the integration of 2D materials into high-performance and low-power-consumption spintronic devices.

en: High-throughput calculations of spin Hall conductivity in non-magnetic 2D materials

materialscloud

en: Materials Cloud

spin-hall-effect

display_name: spin-hall-effect


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

envi

en: Environment

electronic-structure

display_name: electronic-structure

equation-discovery

display_name: equation-discovery

gw

display_name: gw

machine-learning

display_name: machine-learning

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: IZ-DPscreen is a hybrid machine learning framework developed for the high-throughput screening of double perovskites. It employs a stacking regressor approach to accurately predict key material properties, including band gap, formation energy, and thermodynamic stability. By integrating multiple learning algorithms, the model enhances prediction reliability and robustness. The performance of IZ-DPscreen is validated through benchmarking against both theoretical (DFT) and available experimental data, demonstrating its capability for reliable materials screening. This approach enables the rapid identification of thermodynamically stable double perovskites with optimal band gaps, thereby accelerating the discovery of promising candidates for optoelectronic and energy applications.

en: A hybrid machine learning approach for high-throughput screening of thermodynamically stable double perovskites with optimal band gap

band-gap

display_name: band-gap

double-perovskite

display_name: double-perovskite

high-throughput-screening

display_name: high-throughput-screening

thermodynamic-stability

display_name: thermodynamic-stability


en: Nanoscale twin boundaries (TBs) effectively restrict the free motion of dislocations by intersecting with other TBs to realize high strength. However, the dislocations parallel to the TBs can glide along the TBs, resulting in detwinning for low strength. Herein, we present a triple-interlocked-nanotwinned (TIT) strategy for overcoming these inherent deficiencies. An Mg-9Li alloy was used as a proof-of-concept. The treated alloy had a large volume fraction of TIT interfaces (65.7%). It not only had a high yield strength of 508.6 MPa (approximately eleven times higher than that of the TIT-free sample) but also offered unprecedented ageing resistance, with a reduction in hardness of 6% after 730 days. The martensite-like phase transformation process concurrently triggers multiple-variant twins to form TIT structures. These TIT nets can effectively prohibit dislocation motion, reduce strengthening anisotropy, and disperse localized stress during the deformation process. This strategy of phase transformation twinning open a gate to fabricate other stronger metallic or ceramic nanotwinned bulk materials.

en: Triple-interlocked-nanotwinned bulk magnesium alloys with exceptional strength and ageing resistance

ageing-resistance

display_name: ageing-resistance

magnesium-alloys

display_name: magnesium-alloys

strength

display_name: strength

triple-interlocked-nanotwin

display_name: triple-interlocked-nanotwin


en: This dataset supports the theoretical investigation reported in the manuscript "Exceptionally high carrier mobility in hexagonal diamond". Based on first-principles analyses of the electronic structures, carrier mobilities, and electron-phonon coupling mechanisms in both cubic diamond and hexagonal diamond, this work demonstrates high carrier mobility in the heaxagonal phase and elucidates its physical origins. The dataset includes relevant data files for geometry optimisation, phonon dispersion, dynamical quadrupole tensors, G0W0 quasiparticle bands, and carrier mobility calculations.

carrier-mobility

display_name: carrier-mobility

electron-phonon-coupling

display_name: electron-phonon-coupling

hexagonal-diamond

display_name: hexagonal-diamond


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

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

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: 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

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).

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

materialscloud

en: Materials Cloud

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

envi

en: Environment

recessed-contact

display_name: recessed-contact

schottky-barrier

display_name: schottky-barrier


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

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: 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: 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: We investigate the interplay between charge, magnetic, and structural degrees of freedom in the isostructural and isoelectronic 5d1 double-perovskites Ba2MgReO6 and Ba2NaOsO6. Using first-principles-based electronic structure calculations, we show that both materials exhibit a tendency toward spontaneous quadrupolar order in the cubic paramagnetic phase, which is slightly weaker in Ba2NaOsO6 than in Ba2MgReO6. Our analysis further reveals an intimate coupling between the local magnetic moments and charge quadrupoles, mediated by the strong spin-orbit interaction, that leads to the unusual canted configuration of magnetic moments observed in these systems. When structural degrees of freedom are included, the two materials exhibit pronounced differences. In Ba2MgReO6 the strong coupling to Jahn-Teller distortions stabilizes the antiferroic Qx2−y2 order, yielding excellent agreement with available experimental data. In contrast, the Jahn-Teller coupling is significantly weaker in Ba2NaOsO6 and appears insufficient to stabilize the antiferroic quadrupolar order. While this is consistent with the absence of any measurable long-range structural distortion above the magnetic transition temperature, it contrasts with experimental results indicating a strong canting of the magnetic moments. Our analysis thus successfully describes the mechanisms shaping the properties of the Re-compound while a full quantitative description of the magnetic ground state of Ba2NaOsO6 is still elusive.

en: Quadrupole formation and coupling to magnetic and structural degrees of freedom in the 5d1 double perovskites Ba2MgReO6 and Ba2NaOsO6

dftu

display_name: dftu

multipoles

display_name: multipoles

spin-orbit-coupling

display_name: spin-orbit-coupling


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: 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 pathways underlying catalytic CO2 hydrogenation

envi

en: Environment

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: 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 promotion partially bridges a geometry-activation mismatch for N2 at O2-active MoS2 single-atom sites

alkali-promotion

display_name: alkali-promotion

cohp

display_name: cohp

density-functional-theory

display_name: density-functional-theory

nitrogen-activation

display_name: nitrogen-activation

single-atom-catalysis

display_name: single-atom-catalysis


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

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:
Accurately parameterizing the screened Coulomb interaction U is essential for predictive density-functional theory extensions, such as DFT+U and DFT plus dynamical mean-field theory (DFT+DMFT), which model strongly correlated electron systems. This dataset provides the comprehensive computational records for a systematic study mapping the physical equivalence between the two primary first-principles methods for determining this parameter: linear response theory (LRT) and the constrained random-phase approximation (cRPA). Using a unified framework of maximally localized Wannier functions applied to four prototypical transition-metal systems (CrO2, KCuF3, NiO, and Sr2FeO4), the work compares systematically the two methods for determining the U value.

To support the verification and extension of these physical insights, the repository contains the complete input and output files for the underlying quantum mechanical simulations. This includes cRPA calculations partitioned into distinct target orbital configurations, spanning d-only, d+p, and frontier Wannier subspaces, alongside LRT datasets computed both with and without exchange-correlation effects. The entire execution pipeline is preserved through shell scripts that document the automated workflows.

en: Bridging constrained random-phase approximation and linear response theory for computing Hubbard parameters

linear-response

display_name: linear-response

wannier-functions

display_name: wannier-functions


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

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: A machine-learned interatomic potential for Ge-rich GexTe alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials. The results are of interest for the operation of embedded phase change memories which exploits Ge-enrichment of GeSbTe alloys to raise the crystallization temperature. The potential is generated by fitting a large database of energies and forces computed within Density Functional Theory with the neural network scheme implemented in the DeePMD-kit package. The potential is highly accurate and suitable to describe the structural and dynamical properties of the liquid, amorphous and crystalline phases of the wide range of compositions from pure Ge and stoichiometric GeTe to the Ge-rich Ge₂Te alloy. Large scale molecular dynamics simulations revealed a crystallization mechanism which depends on temperature. At 600 K, segregation of most of Ge in excess was observed to occur on the ns time scale followed by crystallization of nearly stoichiometric GeTe regions. At 500 K, nucleation of crystalline GeTe was observed to occur before phase separation, followed by a slow crystal growth due to the concurrent expulsion of Ge in excess.

en: Crystallization kinetics in Ge-rich GexTe alloys from large scale simulations with a machine-learned interatomic potential

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).

crystallization

display_name: crystallization

electronic-memories

display_name: electronic-memories

phase-change-materials

display_name: phase-change-materials


en: Antiferromagnetic Heisenberg chains exhibit two distinct types of excitation spectra: gapped for integer-spin chains and gapless for half-integer-spin chains. However, in finite-length half-integer-spin chains, quantization induces a gap, requiring precise control over sufficiently long chains to study its evolution. In a recent publication, we created length-controlled spin-1/2 Heisenberg chains by covalently linking olympicenes—Olympic ring-shaped magnetic nanographenes. With large exchange interactions, tunable lengths, and negligible magnetic anisotropy, this system is ideal for investigating length-dependent spin excitations, probed via inelastic electron tunneling spectroscopy. We observe a power-law decay of the lowest excitation energy with length L, following a 1/L dependence in the large-L regime, consistent with theory. For L=50, a V-shaped excitation continuum confirms gapless behavior in the thermodynamic limit. Additionally, low-bias current maps reveal the standing wave of a single spinon in odd-numbered chains. Our findings provide evidence for the realization of a one-dimensional analog of a gapless spin liquid within an artificial graphene lattice. This record includes all the data that support the results discussed in the publication.

en: Spin excitations in nanographene-based antiferromagnetic spin-½  Heisenberg chains

materialscloud

en: Materials Cloud

dft

display_name: dft

fractional-excitation

display_name: fractional-excitation

spin-excitation

display_name: spin-excitation

spin-liquid

display_name: spin-liquid

spinon

display_name: spinon


en: Magnetic materials often exhibit complex energy landscapes with multiple local minima, each corresponding to a self-consistent electronic structure solution. Finding the global minimum is challenging, and heuristic methods are not always guaranteed to succeed. We apply an automated workflow to systematically explore the energy landscape of 194 magnetic monolayers from the Materials Cloud 2D crystals database and determine their ground-state magnetic order. Our approach enables effective control and sampling of orbital occupation matrices, allowing rapid identification of local minima. We reveal a diverse set of self-consistent collinear metastable states, further enriched by Hubbard-corrected energy functionals with U parameters computed from first principles using linear response theory. We categorize the monolayers by their magnetic ordering and highlight promising candidates for applications.

en: Exploring the magnetic landscape of easily-exfoliable two-dimensional materials

envi

en: Environment

energy-landscape

display_name: energy-landscape

first-principles

display_name: first-principles

hubbard-corrections

display_name: hubbard-corrections

marvel

display_name: marvel

pbe

display_name: pbe

pbeu

display_name: pbeu

quantum-espresso

display_name: quantum-espresso

romeodft

display_name: romeodft

two-dimensional-magnetic-materials

display_name: two-dimensional-magnetic-materials


en: Cluster expansion (CE) has gained an increasing level of popularity in recent years, and many strategies have been proposed for training and fitting the CE models to first-principles calculation results. The paper reports a new strategy for constructing a training set based on their relevance in Monte Carlo sampling for statistical analysis and reduction of the expected error. We call the new strategy a "bootstrapping uncertainty structure selection" (BUSS) scheme and compared its performance against a popular scheme where one uses a combination of random structure and ground-state search (referred to as RGS). The provided dataset contains the training sets generated using BUSS and RGS for constructing a CE model for disordered Cu2ZnSnS4 material. The files are in the format of the Atomic Simulation Environment (ASE) database (please refer to ASE documentation for more information https://wiki.fysik.dtu.dk/ase/index.html). Each `.db` file contains 100 DFT calculations, which were generated using iteration cycles. Each iteration cycle is referred to as a generation (marked with `gen` key in the database) and each database contains 10 generations where each generation consists of 10 training structures. See more details in the paper.

en: Training sets based on uncertainty estimates in the cluster-expansion method

big-map

display_name: big-map

bootstrapping

display_name: bootstrapping

cluster-expansion

display_name: cluster-expansion

energy-materials

display_name: energy-materials

machine-learning

display_name: machine-learning

monte-carlo

display_name: monte-carlo

phase-transition

display_name: phase-transition


en: We use first-principles density functional theory to investigate how the polar distortion is affected by doping in multiferroic hexagonal yttrium manganite, h-YMnO₃. While the introduction of charge carriers tends to suppress the polar distortion in conventional ferroelectrics, the behavior in improper geometric ferroelectrics, of which h-YMnO₃ is the prototype, has not been studied to date. Using both background charge doping and atomic substitution, we find an approximately linear dependence of the polar distortion on doping concentration, with hole doping reducing and electron doping enhancing it. We show that this behavior is a direct consequence of the improper geometric nature of the ferroelectricity. In addition to its doping effect, atomic substitution can further suppress or enhance the polar distortion through changes in the local chemistry and geometry.

en: Interplay between ferroelectricity and metallicity in hexagonal YMnO₃

dftu

display_name: dftu

erc

display_name: erc

ethz

display_name: ethz


en: Summing all Feynman diagrams with quantitative accuracy is a holy grail in theoretical physics. In condensed matter, the lattice vibration (phonon) field couples with the electrons, leading to the formation of entangled electron-phonon (e-ph) states called polarons.  In the intermediate- and strong-coupling regimes common to many conventional and quantum materials, a many-body treatment of polarons requires adding up a large number of e-ph diagrams. Diagrammatic Monte Carlo (DMC) is an efficient method for diagram summation and has been employed to study polarons within simplified e-ph models (Holstein, Frohlich, etc.).  Here we show DMC calculations based on accurate first-principles e-ph interactions, enabling numerically exact results for ground-state and dynamical properties of polarons in real materials, including the polaron formation energy, effective mass, spectral weight, phonon cloud distribution, optical conductivity, and mobility. We demonstrate such DMC calculations in systems with polarons ranging from small (localized) to large (delocalized), including LiF, SrTiO3, and TiO2 rutile and anatase. This advance is enabled by our recently developed technique for compressing first-principles e-ph interaction matrices, together with a matrix-product formalism that mitigates the DMC sign problem from multiple electronic bands. Our work enables precise modeling of e-ph interactions and polarons in coupling regimes ranging from weak to strong, opening doors to studies of transport, linear response, and superconductivity in the strong e-ph coupling regime.

en: Dataset for first-principles diagrammatic Monte Carlo for electron-phonon interactions and polaron

diagrammatic-monte-carlo

display_name: diagrammatic-monte-carlo

first-principles

display_name: first-principles

lif

display_name: lif

many-body-physics

display_name: many-body-physics

polaron

display_name: polaron

strong-e-ph-interactions

display_name: strong-e-ph-interactions


gebaude-und-wohnungen-im-kanton-zurich

name: Fachstelle Open Government Data des Kantons Zürich

de: Gebäude und Wohnungen im Kanton Zürich gemäss eidgenössischem Gebäude- und Wohnungsregister (eidg. GWR). Es verzeichnet die Gebäude und jede Wohnung im Kanton umfassend in Merkmalen gemäss Merkmalsbeschreibung (siehe Link). Die Ressourcen lassen sich über die EGID verknüpfen. Das eidg. GWR wird durch die Gemeinden des Kantons Zürich geführt, die kantonale Koordinationsstelle GWR-ZH begleitet und berät sie dabei. Die Koordinationsstelle GWR-ZH stellt die Daten alle 3 Monate zur Verfügung und werden zum Stichtag vollständig ersetzt. Ein Zeitverlauf ist in den Daten nicht ersichtlich. Datenquelle bildet das eidg. GWR des Bundesamts für Statistik (BFS). Die Daten bilden den Stand des eidg. GWR ab, wie er durch die Gemeinden erfasst wird. Abweichungen vom tatsächlichen Zustand der Gebäude und Wohnungen sind möglich und im Bereich Energiequellen (Solaranlagen) sowie Wärmequellen (Heizungen) wahrscheinlich. Hinweise auf Abweichungen senden Sie bitte direkt an die Gemeinde, auf deren Boden sich das Objekt befindet, respektive an Statistik Stadt Zürich für Daten auf dem Stadtgebiet Zürich.

de: Gebäude und Wohnungen im Kanton Zürich

de: wohnung strassen wohnungen wohnungsbestand gebaeude gebaeudeart heizungsart heizung gemeinden

fachstelle-ogd-kanton-zuerich

de: Die Fach- und Koordinationsstelle Open Government Data (OGD) ist das kantonale Kompetenzzentrum für offene Behördendaten im Statistischen Amt. Wir unterstützen die Organisationseinheiten und Organe des Kantons bei der Bereitstellung ihrer nicht schützenswerten Daten als Open Data.

de: Fach- und Koordinationsstelle Open Government Data Kanton Zürich

political_level: canton

de: Bezug tagesaktueller Daten des eidg. GWR über die Schnittstelle des Bundes

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

de: Merkmalsbeschreibung des eidg. Gebäude- und Wohnungsregisters

title_for_slug: gebaude-und-wohnungen-im-kanton-zurich

value: ZH-Kt Fachstelle OGD - DCAT Harvester

soci

de: Bevölkerung und Gesellschaft

en: Population and society

fr: Population et société

it: Popolazione e società

de: Daten des eidg. GWR mit Bezug zu ganzen Gebäuden und ihrer Haustechnik. Aus den Daten lassen sich umfassende Angaben zum Gebäudebestand ablesen, beispielsweise zum Gebäudealter oder den Wärme- und Energiequellen. Die Merkmale entsprechen denen im Merkmalskatalogs des eidg. GWR (siehe Link unter "weitere Informationen" im Datensatzbeschrieb) und sind dort beschrieben. Leerwerte wurden in den Gemeinden nicht ausgefüllt, ihr Wert ist unbekannt. Für die einfachere Nutzung fügt die Fach- und Koordinationsstelle OGD vor der Publikation folgende Merkmale hinzu: Für das Merkmal 'Kanton' wird für alle Beobachtungen ZH abgefüllt. Das Merkmal 'Gemeindename' wird mittels BFS-Nummer entsprechend der Gemeindetabelle des Statistischen Amts ausgegeben. Das Merkmal 'Anzahl_Wohnungen' wird anhand der Ressource Wohnungen berechnet.

de: Gebäude

format: CSV

media_type: text/csv

de: Daten des eidg. GWR mit Bezug zu den einzelnen Wohnungen in einem Gebäude. Aus den Daten lassen sich beispielsweise Alter, Fläche, Lage im Gebäude und Ausstattungsangaben herauslesen. Die Merkmale entsprechen denen im Merkmalskatalogs des eidg. GWR (siehe Link unter "weitere Informationen" im Datensatzbeschrieb) und sind dort beschrieben. Leerwerte wurden in den Gemeinden nicht ausgefüllt, ihr Wert ist unbekannt.

de: Wohnungen

de: Daten des eidg. GWR mit Bezug zu den einzelnen Eingängen zu einem Gebäude. Adressen beziehen sich auf einzelne Gebäudeeingänge und sind deswegen pro Eingang in diesem File zu finden. Die Merkmale entsprechen denen im Merkmalskatalogs des eidg. GWR (siehe Link unter 'weitere Informationen' im Datensatzbeschrieb) und sind dort beschrieben. Leerwerte wurden in den Gemeinden nicht ausgefüllt, ihr Wert ist unbekannt.

de: Gebäudeeingänge bzw. Adressen pro Gebäude

gebaeude

display_name: gebaeude

gebaeudeart

display_name: gebaeudeart

gemeinden

display_name: gemeinden

heizung

display_name: heizung

heizungsart

display_name: heizungsart

strassen

display_name: strassen

wohnung

display_name: wohnung

wohnungen

display_name: wohnungen

wohnungsbestand

display_name: wohnungsbestand


leerwohnungszahlen

name: Amt für Statistik und Daten, Analysen & Studien

de:
Leerwohnungsdaten der Gemeinden im Kanton Zürich. Die Daten werden für die Leerwohnungszählung erhoben.

Jährlich aktualisiert wird nur die Ressource "Anzahl Leerwohnungen pro Gemeinde im Kanton Zürich seit 1994". Weitere Informationen dieser Ressource finden sie in den dazugehörigen Metadaten

de: Leerwohnungszahlen

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

de: Kantonale Webseite zu den Leerwohnungszahlen

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

title_for_slug: leerwohnungszahlen

value: ZH-Kt Statistisches Amt - DCAT Harvester

de: Die Variablenliste bezieht sich auf eine alte Datenstruktur und ist nicht mehr aktuell. Die neue Datenstruktur sollte selbsterklärend sein.

de: Variablenliste zu Anzahl Leerwohnungen pro Gemeinde im Kanton Zürich seit 1994

format: XLS

de: Stichtag der Erhebung ist der 1. Juni, die Angaben beziehen sich auf den Gemeindestand (Variable bfs) zum Zeitpunkt der Erhebung. Erhoben werden die Daten von den Gemeinden nach Vorgaben des BFS. Weiter Angaben zur Erhebung finden sich hier unter Methode: https://www.zh.ch/de/planen-bauen/raumplanung/immobilienmarkt/leerwohnungen.html#-291192554

de: Anzahl Leerwohnungen pro Gemeinde im Kanton Zürich seit 1994 nach Anzahl Zimmer, Gebäudetyp, Eigentumsverhältnis und Alter der Wohnung (Alt vs. Neubau)

leerwohnungen

display_name: leerwohnungen

leerwohnungsziffer

display_name: leerwohnungsziffer

mietwohnungen

display_name: mietwohnungen


gemeindesteuern-der-zurcher-gemeinden

name: Amt für Statistik und Daten, Data Management

de: Gemeindesteuerdaten (Steuerertrag, Steuerkraft, berichtigte Steuerkraft, Steuerfüsse, Steuergrundlagen) seit 2011 gemäss Finanzausgleich

de: Gemeindesteuern der Zürcher Gemeinden

de: steuerertrag finanzausgleich steuerfuss steuerkraft steuern gemeinden

title_for_slug: gemeindesteuern-der-zurcher-gemeinden

gove

de: Regierung und öffentlicher Sektor

en: Government and public sector

fr: Gouvernement et secteur public

it: Governo e settore pubblico

econ

de: Wirtschaft und Finanzen

en: Economy and finance

fr: Économie et finances

it: Economia e finanze

de: Steuerkraft (Steuerertrag zu 100%) der Zürcher Gemeinden gemäss Finanzausgleich seit 2011.

de: Steuerkraft seit 2011

de: Steuerbares Einkommen, & Vermögen, Gewinn & Kapital seit 1990.

de: Steuergrundlagen seit 1990

de: Steuerfüsse der Zürcher Gemeinden seit 2012

de: Steuerfüsse seit 2012

de: Steuererträge der Zürcher Gemeinden gemäss Finanzausgleich seit 2011.

de: Steuerertrag seit 2011

de: Berichtige Steuerkraft der Zürcher Gemeinden gemäss Finanzausgleich seit 2011.

de: Berichtige Steuerkraft seit 2011

finanzausgleich

display_name: finanzausgleich

steuerertrag

display_name: steuerertrag

steuerfuss

display_name: steuerfuss

steuerkraft

display_name: steuerkraft

steuern

display_name: steuern


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

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

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