en:
Machine Learning (ML) can be conveniently applied to continuum materials simulations, allowing for the investigation of larger systems and longer timescales, pushing the limits of tractable systems. Here we provide a comprehensive dataset of strained Ge films on Si and their corresponding strain states, which can be used to train a ML model capable of such acceleration. Approximately 80k 2D cases are included, reporting the profiles h(x) and the corresponding elastic energy densities and strain fields. The profiles are conveniently sampled using Perlin-noise and pure-sine waves. A 100nm-large computational domain is considered. The mechanical equilibrium problem is solved using Finite Element Method (FEM). Ge is modeled as an isotropic material and an eigenstrain of 3.99% is used, as in Ge/Si(001).
The database has been exploited for training a (fully) Convolutional Neural Network (CNN) which maps the free surface profile h(x) to the corresponding energy density. If plugged into the proper time-dependent Partial Differential Equation, this term can be used to accelerate continuum simulations of the morphological evolution of strained films while retaining FEM-level accuracy. Tests of the reliability of such CNN model are also provided in the repository, together with the output of surface morphology minimization procedures and morphological evolution simulations during coarsening and growth. In the latter, evolution by surface diffusion has been considered as an important case, but applications to other mechanisms are possible. Generalization examples to larger computational cells with respect to those in the dataset are also available.

en: A FEM dataset of Ge film profiles and elastic energies for machine learning approximation of strain state and morphological evolution

continuum-models

display_name: continuum-models

convolutional-neural-network

display_name: convolutional-neural-network

elastic-energy

display_name: elastic-energy

finite-element-method

display_name: finite-element-method

germanium

display_name: germanium

strained-films

display_name: strained-films

surface-evolution

display_name: surface-evolution

thin-films

display_name: thin-films


$oid: OBSOLETE

en: Crafting neural-network interatomic potentials (NNIPs) remains a complex task, demanding specialized expertise in both machine learning and electronic-structure calculations. Here, we introduce AiiDA-TrainsPot, an automated, open-source, and user-friendly workflow that streamlines the creation of accurate NNIPs by orchestrating density-functional-theory calculations, data augmentation strategies, and classical molecular dynamics.Our active-learning strategy leverages on-the-fly calibration of committee disagreement against ab initio reference errors to ensure reliable uncertainty estimates. We use electronic-structure descriptors and dimensionality reduction to analyze the efficiency of this calibrated criterion, and show that it minimizes both false positives and false negatives when deciding what to compute from first principles. AiiDA-TrainsPot has a modular design that supports multiple NNIP backends, enabling both the training of NNIPs from scratch and the fine-tuning of foundation models. We demonstrate its capabilities through automated training campaigns targeting pristine and defective carbon allotropes, including amorphous carbon, as well as structural phase transitions in monolayer WxMo1-xTe2 alloys.

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

en: AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

active-learning

display_name: active-learning

aiida

display_name: aiida

aiida-trainspot

display_name: aiida-trainspot

carbon-allotropes

display_name: carbon-allotropes

mace

display_name: mace

machine-learning

display_name: machine-learning

neural-network-interatomic-potentials

display_name: neural-network-interatomic-potentials

uncertainty-quantification

display_name: uncertainty-quantification


en: We present a combined density-functional theory and dynamical mean-field theory (DFT+DMFT) study of the full structural phase space of rutile-based vanadium dioxide (VO2), including also the less studied M2 and T phases, using an unconventional bond-centered orbital basis. The use of bond-centered orbitals allows us to treat all main phases of VO2, and the structural transitions between them, using one consistent approach with moderate computational cost and without pre-pattering of the structure into dimerized and undimerized V--V pairs. We obtain two distinct insulating states on the two different types of vanadium chains in the M2 phase, a singlet-insulator on the dimerized chains and a Mott-insulator on the zigzag-distorted chains, which, however, are strongly coupled in the M2 phase and thus the metal-insulator transition always occurs concomitantly for both types of sites. We also demonstrate that the M2 phase corresponds to a local energy minimum in the structural phase space of VO2, the stability of which, apart from the internal structural distortion, depends crucially on the unit cell strain relative to the undistorted rutile phase. Our calculations further indicate that the symmetry-distinct triclinic T phase corresponds electronically to either an M1 or an M2-type insulator with an abrupt transition as a function of distortion. Finally, we disentangle the effect of the dimerization and zigzag distortions by constructing hypothetical structures that contain only one site type, finding that the zigzag distortion strongly favors emergence of the Mott-insulating state, both as function of distortion and on-site interaction.

en: Towards a unified first-principles-based description of VO2 using DFT+DMFT with bond-centered orbitals

dft

display_name: dft

dftdmft

display_name: dftdmft

mit

display_name: mit


en:
This is a summary of previous work, presenting a simulation dataset for the interactions between functionalized/unfunctionalized carbon nanomaterials and PVA. The primary objective is to investigate flow-assisted orientation and surface properties. Unreliable LAMMPS stress-strain simulations were excluded, and the rationality of the pull-out, nanoscratch, and cooling simulations was validated.

The synergistic reinforcement of polyvinyl alcohol (PVA) fibers using carbon nanomaterials is heavily constrained by interfacial compatibility and dynamic spatial orientation during the wet-spinning process. In this study, confined-shear molecular dynamics (MD) simulations were employed to systematically investigate the interfacial mechanics, structural evolution, and shear-induced orientation of PVA composites reinforced with one-dimensional (SWNT) and two-dimensional (graphene/graphene oxide) carbon nanofillers. The results reveal that surface functionalization (-COOH, -OH) significantly enhances interfacial adhesion via dense hydrogen bond networks, shifting the failure mechanism to highly dissipative plastic deformation and improving wear resistance. Interestingly, under static equilibrium, the functionalized 1D/2D hybrid system (GO+SWNT-COOH/PVA) exhibits an anomalous antagonistic effect in mechanical moduli due to spatial crowding and conformational frustration. However, the application of a dynamic shear flow field effectively overcomes this static steric hindrance, inducing a highly ordered axial alignment of both polymer chains and nanofillers. Consequently, the post-shear hybrid system unlocks its multi-scale synergistic reinforcement potential, achieving a remarkable axial Young's modulus of 8.10 GPa. These atomistic insights elucidate the critical transition from static interfacial chemistry to dynamic macroscopic orientation, providing robust theoretical guidance for the fabrication of high-performance PVA composite fibers.

en: Molecular dynamics insights into the shear-induced orientation and interfacial enhancement in functionalized CNTs and graphene/PVA composite fibers

carbon-nanotubes

display_name: carbon-nanotubes

graphene

display_name: graphene

md

display_name: md

pva

display_name: pva


en: Vacancy-ordered double perovskites have emerged as lead-free alternatives, offering remarkable stability and compositional tunability for optoelectronic applications. In this study, we provide first-principles insights into their electronic properties, surface stability, and energy level alignment using a non-empirical dielectric-dependent hybrid functional. For representative family of the Cs2MX6, with M = Zr, Sn, Te, and X= Cl, Br, I, the predicted electronic band gaps are comparable to those obtained with the state-of-the-art GW method. We investigate the stability of these materials under simulated experimental conditions, considering both the rich and poor chemical potentials of their precursor salts. Our results indicate distinct regions of surface energy stability that favor CsX terminations. In contrast, MX4 terminations show in-gap surface states, which can act as trap states and reduce carrier lifetime. Finally, based solely on the intrinsic absolute energy levels, we identify promising candidates as charge transport/injection layers for typical photovoltaic and light-emitting applications.

en: Dataset for Energy level alignment of vacancy-ordered halide double perovskites

absolute-energy-levels

display_name: absolute-energy-levels

charge-transport-layer

display_name: charge-transport-layer

density-functional-theory

display_name: density-functional-theory

dielectric-dependent-hybrid-functional

display_name: dielectric-dependent-hybrid-functional

surface

display_name: surface

vacancy-ordered-double-perovskites

display_name: vacancy-ordered-double-perovskites


en: Germanium–silicon–germanium (Ge/SixGe1-x) heterostructures have emerged as a prominent platform for high-mobility electronic devices and hole-spin–based quantum technologies. In this work, we present an ab initio study of strained germanium, demonstrating that biaxial compressive strain in a Ge quantum well strongly alters the valence-band structure. Specifically, the strain lifts the heavy-hole/light-hole degeneracy and modifies the effective masses (significantly enhances hole mobility). Our findings offer a comprehensive theoretical description of the combined effects of strain and quantum confinement on the valence bands of Ge quantum wells, providing a solid foundation for predictive modeling of Ge-based high-mobility electronics and hole-spin qubits.

en: Ab initio study of strain and quantum confinement shaping the valence-band structure of Ge quantum wells

aiida

display_name: aiida

quantum-confinement

display_name: quantum-confinement

quantum-espresso

display_name: quantum-espresso

strained-germanium

display_name: strained-germanium

tight-binding

display_name: tight-binding

wannier-interpolation

display_name: wannier-interpolation


en:
Magnet/superconductor hybrid systems have been put forward as a platform for realizing topological superconductivity. We investigated the heterostructure of ferromagnetic monolayer CrCl₃ and superconducting NbSe₂. Using low-temperature scanning tunneling microscopy, we observe topologically trivial Yu-Shiba-Rusinov (YSR) states localized at the edge of CrCl₃ islands. DFT simulations reveal that the Cr atoms at the edge have an enhanced d-orbital DOS close to the Fermi energy. This leads to an exchange coupling between these atoms and the substrate that rationalizes the edge-localization of the YSR states.

This dataset contains the first-principles calculations performed on a nanoribbon of CrCl₃ on NbSe₂ and the associated notebooks used to generate figures from this data.

en: One dimensional edge localized YSR states in CrCl₃ on NbSe₂

aiida-siesta

display_name: aiida-siesta

heterostructure

display_name: heterostructure

horizon-europe

display_name: horizon-europe

magnetism

display_name: magnetism

prace

display_name: prace

siesta

display_name: siesta

superconductivity

display_name: superconductivity

van-der-waals

display_name: van-der-waals

yu-shiba-rusinov

display_name: yu-shiba-rusinov


en: Density-functional theory with on-site U and inter-site V Hubbard corrections (DFT+U+V) is widely used to predict properties of transition-metal and rare-earth compounds, but its accuracy depends critically on how these parameters are obtained. While they can be determined empirically, first-principles approaches offer better consistency, though their results can differ and a systematic comparison is lacking. Here, we compare two widely used approaches, linear-response theory (LRT) and the Hartree–Fock-based pseudohybrid functional formalism, applied to representative oxides (MnO, NiO, CoO, FeO, BaTiO3, ZnO, and ZrO2). For partially occupied transition-metal d states, both methods yield comparable U values, but they differ significantly for nearly empty or fully filled d shells. For O-2p states, LRT systematically predicts large U values (~10 eV), whereas the pseudohybrid approach gives system-dependent values. Even larger differences appear for the inter-site V: the former yields small values (<1 eV), while the latter gives larger values (~3 eV) due to its dependence on charge redistribution. We further show that the pseudopotential choice, which defines Hubbard projectors, affects the parameters and their agreement. Overall, while parallels between these two methods exist, they rely on different assumptions, leading to variations in predictions of material properties.

en: Comparing Hubbard parameters from linear-response theory and a Hartree-Fock-based approach

coo

display_name: coo

dftu

display_name: dftu

dftuv

display_name: dftuv

feo

display_name: feo

mno

display_name: mno

nio

display_name: nio

zno

display_name: zno


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

density-functional-theory

display_name: density-functional-theory

nonadiabatic-regime

display_name: nonadiabatic-regime

pseudopotentials

display_name: pseudopotentials


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.

materialscloud

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

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

en: Materials Cloud

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

dft

display_name: dft

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

envi

en: Environment

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: 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

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: 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

active-learning

display_name: active-learning

anharmonicity

display_name: anharmonicity

machine-learning

display_name: machine-learning

phase-transitions

display_name: phase-transitions

phonons

display_name: phonons

thermodynamics

display_name: thermodynamics


en: Correlated electron molecular orbital (CEMO) materials host emergent electronic states built from molecular orbitals localized over clusters of transition metal ions yet have historically been discovered sporadically and generally been treated as isolated case studies. Here we establish CEMO materials as a systematically discoverable class and introduce a graph-based framework to identify, classify, and organize transition-metal cluster motifs in inorganic solids. Starting from crystal structures in the Materials Project, we construct transition metal connectivity graphs, extract cluster motifs using a bond-cutting algorithm, and determine cluster point groups, effective cluster sublattice dimensionality, and translational symmetry. Applying this approach in a high-throughput screen of 34,548 compounds yields 5,306 cluster-containing materials, including 2,627 stable or metastable compounds with isolated clusters and 984 materials featuring mixed-metal clusters. The resulting dataset reveals symmetry and element dependent trends in cluster formation. By integrating cluster classification with flat band lattice topology and battery-relevant information, we provide further relevant information to multiple scientific communities. The accompanying open dataset, Cluster Finder software, and interactive web platform enable systematic exploration of cluster driven electronic phenomena and establish a general pathway for discovering correlated quantum materials and functional materials with cluster-based or extended metal-metal bonding in inorganic solids.

en: Discovery of correlated electron molecular orbital materials using graph representations

battery-materials

display_name: battery-materials

correlated-electron-materials

display_name: correlated-electron-materials

flat-band

display_name: flat-band

materials-informatics

display_name: materials-informatics


en: This electrochemical, X-ray diffraction, and operando X-ray abosorption spectroscopy data characterize and decouple the effects of bulk vs. electrolyte Fe incorporation in crystalline, defective crystalline, and amorphous catalysts derived from the same CoSn(OH)₆ precursor. Electrolyte Fe incorporation equalizes the OER activity across all three structural variants, demonstrating that surface Fe chemistry can dominate initial structural effects on the reconstructed catalyst interface.

materialscloud

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

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

en: Materials Cloud

en: Characteristic data of activation of crystalline and amorphous OER catalysts with bulk and surface Fe incorporation.

catalyst

display_name: catalyst

cobalt

display_name: cobalt

diffraction

display_name: diffraction

electrocatalysis

display_name: electrocatalysis

iron

display_name: iron

local-structure

display_name: local-structure

operando

display_name: operando

oxygen-evolution

display_name: oxygen-evolution

surface-reconstruction

display_name: surface-reconstruction

x-ray-apbsorption-spectroscopy

display_name: x-ray-apbsorption-spectroscopy


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]

envi

en: Environment

aiida

display_name: aiida

dft

display_name: dft

magnetic-exchanges

display_name: magnetic-exchanges


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

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

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

finite-temperature

display_name: finite-temperature

krylov

display_name: krylov

lanczos

display_name: lanczos

quantum

display_name: quantum

simulation

display_name: simulation


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

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

cohp

display_name: cohp

electronic-structure-descriptors

display_name: electronic-structure-descriptors

oxygen-activation

display_name: oxygen-activation

single-atom-catalyst

display_name: single-atom-catalyst

strain-engineering

display_name: strain-engineering

sulfur-vacancy

display_name: sulfur-vacancy

transition-metal-doping

display_name: transition-metal-doping


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

materialscloud

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

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

en: Materials Cloud

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

envi

en: Environment

aliphatic-molecules

display_name: aliphatic-molecules

dft

display_name: dft

nanowires

display_name: nanowires

silicon

display_name: silicon

surface-adsorption

display_name: surface-adsorption


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

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

en: Dielectric-dependent hybrid functional from meta-GGA

density-functional-theory

display_name: density-functional-theory

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure


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

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

active-learning

display_name: active-learning

crystal-structure-prediction

display_name: crystal-structure-prediction

high-pressure-ice

display_name: high-pressure-ice

ice-rules

display_name: ice-rules

ice-vii

display_name: ice-vii

machine-learning-force-fields

display_name: machine-learning-force-fields


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

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

aiida

display_name: aiida

generative-ai

display_name: generative-ai

machine-learning

display_name: machine-learning

mlip

display_name: mlip


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

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

hydrogen-boride-nanosheet

display_name: hydrogen-boride-nanosheet

hydrogen-release

display_name: hydrogen-release

interlayer-space

display_name: interlayer-space

two-dimensional-materials

display_name: two-dimensional-materials


en: 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

spin-hall-effect

display_name: spin-hall-effect

wannier-functions

display_name: wannier-functions


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.

materialscloud

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

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

en: Materials Cloud

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: We introduce P3MaZe, a real-space particle–mesh electrostatic method that combines the standard short-range/long-range decomposition of Particle-Particle Particle-Mesh (P3M) electrostatics with the Mass-Zero constrained dynamics (MaZe) framework. In this formulation, the smooth long-range electrostatic potential is represented on a mesh as a zero-inertia auxiliary field, while the discretized Poisson equation is enforced as a holonomic constraint during molecular dynamics. By retaining the standard P3M decomposition, P3MaZe preserves the systematic accuracy controls associated with the real-space cutoff, the Ewald splitting, the mesh spacing, and the charge assignment procedure, while replacing the conventional multigrid Poisson solver by a constrained correction problem. The method is validated for molten NaCl and simple point-charge flexible water (SPC/Fw). Structural, translational, collective, and rotational dynamical observables are in quantitative agreement with those obtained with established electrostatic methods, including real-space P3M, and Ewald summation. The constrained formulation consistently requires fewer multigrid iterations than the corresponding real-space P3M solver while retaining the expected linear scaling with system size. These results establish P3MaZe as a promising new direction for scalable real-space electrostatics in large-scale molecular simulations.

en: P3MaZe: a Mass-Zero constrained-dynamics formulation of particle–mesh electrostatics

envi

en: Environment

constrained-dynamics

display_name: constrained-dynamics

electrostatic-interactions

display_name: electrostatic-interactions

long-range-interactions

display_name: long-range-interactions

molecular-dynamics

display_name: molecular-dynamics


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

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

database

display_name: database

interatomic-potential

display_name: interatomic-potential


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

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

recessed-contact

display_name: recessed-contact

schottky-barrier

display_name: schottky-barrier


en: We 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.

materialscloud

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

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

en: 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.

materialscloud

en: Materials Cloud

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

envi

en: Environment

dftu

display_name: dftu

multipoles

display_name: multipoles

spin-orbit-coupling

display_name: spin-orbit-coupling


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

en: In search of novel ductile superconductors

ductility

display_name: ductility

elastic-tensor

display_name: elastic-tensor

generalized-stacking-fault-energy

display_name: generalized-stacking-fault-energy

pettifors-ratio

display_name: pettifors-ratio

pughs-ratio

display_name: pughs-ratio

rices-ratio

display_name: rices-ratio

superconductor

display_name: superconductor

surface-energy

display_name: surface-energy


en: Materials are often represented in machine learning applications by (chemical-)geometric descriptions of their atomic structure. In this work, we propose an alternative framework for representing materials using descriptions of their electronic structure called Spectral Operator Representations (SOREPs). This record contains the code and data used to study carbon nanotubes (CNTs), barium titanate polymorphs, and the accelerated screening of transparent conducting materials with SOREPs. A data set for each application is provided: pz tight binding band structures for the three CNT configurations studied; the structures, band dispersions, and SOREP features of 127 BaTiO₃ polymorphs; and the SOREP features and ML targets for the MC3D materials considered in the accelerated screening. Additionally, code including patch files for Quantum ESPRESSO, the "sorep" python package, and the set of scripts used to prepare these data, train ML models, and plot results is provided.

en: Spectral operator representations

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure

epfl

display_name: epfl

machine-learning

display_name: machine-learning

marvel

display_name: marvel


en: Due to their high accuracy, Double-Hybrid Density functionals emerged to important methods for molecular electronic-structure calculations. The high computational costs of double-hybrid calculations in condensed phase and the lack of efficient gradient implementations thereof inhibit a wide applicability for periodic systems. We present an implementation of gradients for Double-Hybrid functional theory into CP2K. The Auxiliary Density Matrix Method (ADMM) reduces the overhead from the Hartree-Fock calculations providing an efficient and accurate methodology to tackle condensed phase systems. First applications to water containing systems of different densities and molecular crystals pave the way for advanced studies. We present large benchmark systems to discuss the efficiency of our methodology on modern super computing hardware.

en: Double-Hybrid Density functionals for the condensed phase: gradients, stress tensor, and Auxiliary-Density Matrix Method acceleration

analytical-gradients

display_name: analytical-gradients

auxiliary-density-matrix-method

display_name: auxiliary-density-matrix-method

benchmark

display_name: benchmark

condensed-matters

display_name: condensed-matters

cscs

display_name: cscs

density-functional-theory

display_name: density-functional-theory

double-hybrid-functional

display_name: double-hybrid-functional

electronic-structure-method

display_name: electronic-structure-method

molecular-crystals

display_name: molecular-crystals

snsf

display_name: snsf


en: Superlattices made of alternating blocks of the phase change compound Sb₂Te₃ and of TiTe₂ confining layers have been recently proposed for applications in neuromorphic devices. The Sb₂Te₃/TiTe₂ heterostructure allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. However, Sb₂Te₃ suffers from a low data retention due to a low crystallization temperature Tx. Substituting Sb₂Te₃ with a phase change compound with a higher Tx, such as GeTe, seems an interesting option in this respect. Nanoconfinement might, however, alters the crystallization kinetics with respect to the bulk. In this work, we investigated the crystallization process of GeTe nanoconfined in geometries mimicking GeTe/TiTe₂ superlattices by means of molecular dynamics simulations with a machine learning potential. The simulations reveal that nanoconfinement induces a mild reduction in the crystal growth velocities which would not hinder the application of GeTe/TiTe₂ heterostructures in neuromorphic devices with superior data retention.

en: Crystallization kinetics of nanoconfined GeTe slabs in GeTe/TiTe-like superlattices for phase change memories

crystallization

display_name: crystallization

gete

display_name: gete

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nanoconfinement

display_name: nanoconfinement

neural-network-potential

display_name: neural-network-potential

neuromophic-computing

display_name: neuromophic-computing

phase-change-materials

display_name: phase-change-materials

phase-change-memories

display_name: phase-change-memories

superlattices

display_name: superlattices


en: To investigate charged defects in gapped materials by first-principles calculations, they must be charged by either adding/removing electrons or compensating donors/acceptors. The former approach is more common, but it is not without drawbacks. We tested the latter method for a collection of model systems consisting of charge-compensated point defects in diamond (NV/SiV-centers, substitutional nitrogen/phosphorous/oxygen/sulfur donors, and substitutional boron/beryllium acceptors), comparing the geometrical and electronic properties of the compensated defect pairs with those of the individual defects in charged supercells. We find that the charging by explicit donors/acceptors works well and can be advantageous if properly designed, although interpretation of the results can be challenging. In this archive, we share the final optimized geometries of all studied structural models (in CIF format).

en: Neutral supercells for charged impurities by explicit acceptor/donor compensation - defects in diamond

compensating-defect

display_name: compensating-defect

jellium-counter-charge

display_name: jellium-counter-charge

nv-center

display_name: nv-center

siv-center

display_name: siv-center


en: The deposited structure models of DNA adsorbed on prisitne PG/p-Si and metal (Au/W) doped PG/p-Si sheets, have been subjected to first principles calculations  based on DFT (PBE+vdW). The calculated binding affinity on PG and p-Si surface by M062X/6-31G* level of theory and adsorption energies by DFT predicts that PG has higher sensitivity towards DNA nucleic bases compared to p-Si with evident changes in their band structure properties.

materialscloud

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

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

en: 2D pentagonal-graphene and pentagonal-silicene sheets engineered for the detection of DNA nucleobases for genetic biomarker: A DFT study

dft

display_name: dft

electronic-structure

display_name: electronic-structure


nomenklatur-ur

de: Flur-, Orts- und Geländenamen (= geografische Namen der amtlichen Vermessung) von geografischen Gebieten, Landschaftsteilen, Flurstücken, topografischen Objekten usw.

geoinformation-kanton-uri

de: Die Lisag AG bewirtschaftet das geografische Informationssystem des Kantons Uri (GIS Uri), ist die verantwortliche Stelle für den ÖREB-Kataster Uri und zudem zuständig für die Durchführung der amtlichen Vermessung auf Urner Kantonsgebiet.

de: Lisag AG, Geoinformation Kanton Uri

political_level: canton

de: Justizdirektion Kt. Uri

de: geocat.ch Permalink

fr: geocat.ch permalien

it: geocat.ch link permanente

en: geocat.ch permalink

spatial: Canton of Uri (UR)

de: Nomenklatur (UR)

title_for_slug: nomenklatur-ur

regi

de: Regionen und Städte

en: Regions and cities

fr: Régions et villes

it: Regioni e città

de: WMS-Dienst des Kanton Uri

format: WMS

de: WFS-Dienst des Kanton Uri

format: WFS

value: Uri-Kt - Geocat Harvester


gebaudeadressen-ur

de: Die Gebäudeadresse setzt sich zusammen aus einer Ortschaft mit Postleitzahl, einem Lokalisationsnamen (in der Regel Strassennamen) und einer Hausnummer. Zusätzlich werden die Strassenstücke und benannten Gebiete der amtlichen Vermessung dargestellt.

de: Gebäudeadressen (UR)

title_for_slug: gebaudeadressen-ur


einzelobjekte-ur

de: Die Informationsebene Einzelobjekte umfasst Objekte, die Merkmale der Bodenbedeckung enthalten, welche aufgrund ihrer Eigenschaft oder Ausdehnung aber keine oder unwesentliche flächenmässige Bedeutung haben.

de: Einzelobjekte (UR)

title_for_slug: einzelobjekte-ur


de: Die OGD-Datensammlungen zum Kantonsrat Luzern (Kantonsparlament) werden aus dem aktuellen Geschäftsverwaltungssystem gespiesen: Welche Personen bekleiden wann welche Ämter im Rat und in seinen Kommissionen? Wann finden die Sessionen mit welchen Traktanden statt? Wer nimmt an den Sessionen teil? Die Daten reichen für die Sessionsplanung bis 4 Jahre in die Zukunft und meist bis 2013 zurück. Wo vorhanden sind Rückerfassungen vor 2013 und ältere Datensammlungen integriert. Eine ausführliche Beschreibung findet sich auf der Webseite www.lu.ch/kr/OGD

it: Le raccolte di dati OGD relative al Consiglio cantonale di Lucerna (Parlamento cantonale) provengono dall'attuale sistema di gestione delle attività: quali persone ricoprono quali mandati nel Consiglio e nelle sue commissioni e quando? Quando si tengono le sessioni e quali sono gli ordini del giorno? Chi partecipa alle sessioni? I dati sono sufficienti per pianificare le sessioni fino a 4 anni nel futuro e risalgono per lo più al 2013. Ove disponibili, sono stati integrati i dati registrati prima del 2013 e le raccolte di dati più vecchie. Una descrizione dettagliata è disponibile sul sito web www.lu.ch/kr/OGD (in tedesco).

fr: Les collections de données OGD relatives au Grand Conseil lucernois (parlement cantonal) sont alimentées par le système actuel de gestion des affaires : quelles personnes occupent quelles fonctions au sein du Conseil et de ses commissions, et à quel moment ? Quand ont lieu les sessions et quels sont leurs ordres du jour ? Qui participe aux sessions ?  Les données couvrent la planification des sessions jusqu'à quatre ans dans le futur et remontent généralement jusqu'en 2013. Lorsque cela est possible, les données antérieures à 2013 et les collections de données plus anciennes sont intégrées. Une description détaillée est disponible sur le site web www.lu.ch/kr/OGD (en almand).

en: The OGD data collections on the Lucerne Cantonal Council (cantonal parliament) are fed from the current business management system: Which individuals hold which positions in the council and its committees, and when? When do the sessions take place, and what is on the agenda? Who participates in the sessions?  The data covers session planning up to four years into the future and usually goes back to 2013. Where available, retroactive entries prior to 2013 and older data collections are integrated. A detailed description can be found on the website www.lu.ch/kr/OGD (in German).

en: cantonal-council committees luzern mandates members parliament session

de: gremien kantonsrat luzern mandate mitglieder parlament session

fr: commissions conseil-cantonal lucerne mandats membres parlement session

it: commissioni consiglio-cantonale lucerna mandati membri parlamento sessione

staatskanzlei-luzern

de: Die Staatskanzlei ist die Stabsstelle von Regierung und Parlament des Kantons Luzern

en: The State Chancellery is the administrative office of the government and parliament of the canton of Lucerne

fr: La Chancellerie d'État est le service administratif du gouvernement et du parlement du canton de Lucerne

it: La Cancelleria dello Stato è l'organo di supporto del Governo e del Parlamento del Cantone di Lucerna

en: State Chancellery Luzern

de: Staatskanzlei Luzern

fr: Chancellerie d'Etat Lucerne

it: Cancelleria dello Stato Lucerna

de: Staatskanzlei des Kantons Luzern

fr: Chancellerie d'État du canton de Lucerne

en: State Chancellery of the Canton of Lucerne

it: Cancelleria di Stato del Cantone di Lucerna

spatial: Kanton Luzern

en: Cantonal Parliament - Committees - People - Mandates - Sessions

it: Gran Consiglio - Organi - Persone - Mandati - Sessioni

fr: Grand Conseil - Organes - Personnes - Mandats - Sessions

de: Kantonsrat Gremien - Personen - Mandate - Sessionen

value: I14Y DCAT Staatskanzlei Luzern

gove

de: Regierung und öffentlicher Sektor

en: Government and public sector

fr: Gouvernement et secteur public

it: Governo e settore pubblico

de: Die Distribution enthält alle relevanten Gremien des Kantonsrates: Als Gremium werden der Kantonsrat selbst, seine Fraktionen, die Kommissionen sowie weitere ständige und temporäre Organe des Kantonsrates bezeichnet (Geschäftsleitung des Kantonsrates, Wahlprüfungskommission, PUK, ...).

de: Gremien

format: CSV

media_type: text/csv

de: Sitzungen des Kantonsrates werden Sessionen genannt und dauern bis zu drei Tage (§ 35 KRG und §31 ff. GOKR). Die Planung erfolgt vier Jahre voraus.

de: Session

de: Die vorberatenden, ständigen Kommissionen und die bedarfsorientierten, nichtständigen Kommissionen werden durch Wahl von Mitgliedern des Kantonsrats zusammengesetzt. Neben der Funktion "Mitglied" kann ein Mandat weitere Funktionen der Kommission umfassen. Die Erfassung der Kommissionsmitglieder erfolgt seit 2011 (inkl. Rückerfassung der damals aktiven Parlamentsmitglieder)

de: Kommissionsmandat

de: Die Fraktionen (siehe § 19 KRG) sind entlang der Parteien organisiert und entsprechend die Fraktions-/Partei-Mitgliedschaft eines Kantonsratsmitglieds ist über ein "Fraktionsmandat" mit der Funktion "Mitglied" zu einem Gremium des Typs "Fraktion" zugeordnet (siehe bzw. Datensatz lu_kr_gremium). Solche Fraktionszugehörigkeiten sind bei abhängigen Tabellen als Redundanz mitgegeben. Kantonsratsmitglieder können während ihrer Amtszeit ihre Fraktion wechseln oder verlassen (d. h. "fraktionslos" werden). Dieses Subset gibt nur die aktuell laufenden Mandate wieder.

de: Fraktionsmandate - aktuell

de: Aktive Parlamentsmitglieder sind gemäss § 49a KRG verpflichtet, ihre Interessenbindungen offen zu legen. Der Datensatz berücksichtigt alle Mutationen seit dem letzten Export aus dem Quellsystem. Nach dem Austritt einer Person aus dem Kantonsrat werden die Interessenbindungen nicht mehr aktualisiert. Die Meldepflicht besteht seit dem 1.8.2008. Der Datensatz enthält Interessenbindungen ab Erstellungsdatum im Januar 2019.

de: Interessenbindungen

de: Die Fraktionen (siehe § 19 KRG) sind entlang der Parteien organisiert und entsprechend die Fraktions-/Partei-Mitgliedschaft eines Kantonsratsmitglieds ist über ein "Fraktionsmandat" mit der Funktion "Mitglied" zu einem Gremium des Typs "Fraktion" zugeordnet (siehe bzw. Datensatz lu_kr_gremium). Solche Fraktionszugehörigkeiten sind bei abhängigen Tabellen als Redundanz mitgegeben. Kantonsratsmitglieder können während ihrer Amtszeit ihre Fraktion wechseln oder verlassen.

de: Fraktionsmandat

de: Kantonsratsmandate sind an eine Person vergebene Mandate, wobei die Zuteilung auf die Kandidierenden (Funktion "Mitglied") gemäss der Anzahl Listen- und Personenstimmen der letzten Kantonsratswahlen pro Wahlkreis und der Rangfolge der Kandidierenden gemäss Stimmenzahl - ebenso bei Nachrückenden während der Legislatur. Eine gewählte Person kann während der Legislatur fraktionslos werden oder die Fraktion wechseln. Neben der Funktion "Mitglied" gibt es weitere Funktionen wie "Kantonsratspräsident/in" etc., deren Mandat durch Wahl im Kantonsrat vergeben werden. Dieses Subset umfasst nur die aktuellen Mandate: 120 Mitglieder, Kantonsratspräsiden/in und Vizekantonsratspräsiden/in.

de: Kantonsratsmandat - aktuell

de: Eine Legislatur des Kantonsrats dauert vier Jahre und beginnt mit dem ersten Tag der Juni-Session und endet am Tag vor der Juni-Session des vierten Jahres. Bis 2003 war der Wechsel jeweils bereits im April.

de: Legislatur

de: Die vorberatenden, ständigen Kommissionen und die bedarfsorientierten, nichtständigen Kommissionen werden durch Wahl von Mitgliedern des Kantonsrats zusammengesetzt. Neben der Funktion "Mitglied" kann ein Mandat weitere Funktionen der Kommission umfassen. Dieses Subset zeigt nur die aktuellen Kommissionsmandate.

de: Kommissionsmandat - aktuell

de: Kantonsratsmandate - bzw. frühere Grossratsmandate - sind an eine Person vergebene Mandate, wobei die Zuteilung auf die Kandidierenden (Funktion "Mitglied") gemäss der Anzahl Listen- und Personenstimmen der jeweiligen Kantonsratswahlen pro Wahlkreis und der Rangfolge der Kandidierenden gemäss Stimmenzahl - ebenso bei Nachrückenden während der Legislatur. Eine gewählte Person kann während der Legislatur fraktionslos werden oder die Fraktion wechseln. Neben der Funktion "Mitglied" gibt es weitere Funktionen wie "Kantonsratspräsident/in" etc., deren Mandat durch Wahl im Kantonsrat vergeben werden. Der Datensatz wird aus zwei sich teilweise überschneidenden Datenquellen gespiesen: Der aktuellen Geschäftsverwaltung (Vorrang) und einer historischen Liste ab 1807.

de: Kantonsratsmandat

de: Die Distribution "Person" umfasst Personen, die irgend eine Relation zu einem Mandat im Kantonsrat Luzern bzw. im Grossen Rat hat oder hatte. Pro Person sind mehrere Adressen (Geschäft, Privat, ...) möglich und mit einer zeitlichen Gültigkeit versehen. In speziellen Fällen können Adressen für die Veröffentlichung gesperrt sein und erscheinen entsprechend nicht in den OGD-Daten. Die Relationen zur Tabelle Person sind in anhängigen OGD-Datensätzen zwar via Fremdschlüssel angegeben, zur bequemeren Nutzbarkeit ist aber oft in den abhängigen Tabellen die die Redundanz der Felder Vorname, Name und Jahrgang eingetragen. Im Entity-Relationen-Diagramm sind solche Redundanzfelder farblich markiert. Nach einem Austritt findet keine Aktualisierung mehr statt (ausser bekannte Todesdaten).

de: Person

de: Die Teilnahme der Parlamentsmitglieder an Sessionen des Kantonsrats wird automatisch durch die Abstimmungsanlage pro Halbtag erfasst.

de: Sessionsteilnahme