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

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

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

computational-chemistry

display_name: computational-chemistry

nudged-elastic-band

display_name: nudged-elastic-band

transition-state

display_name: transition-state


$oid: OBSOLETE

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

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente


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

quantum-espresso

display_name: quantum-espresso

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

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

dft

display_name: dft

vacancy

display_name: vacancy


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

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

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

li-ion-conductors

display_name: li-ion-conductors

molecular-dynamics

display_name: molecular-dynamics

solid-state-electrolytes

display_name: solid-state-electrolytes


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

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

antisite-defect

display_name: antisite-defect

multiferroic

display_name: multiferroic


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

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

ageing-resistance

display_name: ageing-resistance

magnesium-alloys

display_name: magnesium-alloys

strength

display_name: strength

triple-interlocked-nanotwin

display_name: triple-interlocked-nanotwin


en: 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


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

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

en: Materials Cloud

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

aiida

display_name: aiida

aiida-kkr-ksbdg

display_name: aiida-kkr-ksbdg

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

superconductivity

display_name: superconductivity


envi

en: Environment


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

molecular-dynamics

display_name: molecular-dynamics

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

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

first-principles

display_name: first-principles

halide-double-perovskite

display_name: halide-double-perovskite

machine-learned-interatomic-potentials

display_name: machine-learned-interatomic-potentials

phonons

display_name: phonons

polymorphs

display_name: polymorphs


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

en: Predicting challenging phase transitions with Bayesian active learning

active-learning

display_name: active-learning

anharmonicity

display_name: anharmonicity

machine-learning

display_name: machine-learning

phase-transitions

display_name: phase-transitions

thermodynamics

display_name: thermodynamics


en: Solvent reverse osmosis is a pressure-driven, liquid-phase process for separating solvent mixtures, offering a potential low-energy alternative to thermal operations. Graphene oxide (GO) laminates provide tunable nanochannels to probe confined solvent transport, yet solvent–solvent separations remain underexplored due to solvation-induced structural instabilities and the small molecular sizes. Here we construct solvent-stable, supported GO nanochannel membranes that preserves integrity under pressurized solvents, and tune interlayer confinement and surface polarity via controlled chemical reduction. Across 51 solvent systems and 5 distinct nanochannels, we demonstrate that separation is governed by coupled nanoconfinement and solvent affinity, where selective interfacial association can surpass simple size-exclusion expectations. Maximum permselectivity arises from balancing channel size with retained polarity, indicating that channel shrinking alone does not optimize performance. These findings identify channel surface chemistry as a key design factor for polarity-rich solvent systems and provide a framework for rationally tailoring nanochannels for complex solvent separations.

en: Solvent reverse osmosis beyond size exclusion in 2D nanochannel membranes

graphene

display_name: graphene

reverse-osmosis

display_name: reverse-osmosis

solventsolvent-separation

display_name: solventsolvent-separation


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

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

fair-principle

display_name: fair-principle

llm-agent

display_name: llm-agent

materials-informatics

display_name: materials-informatics

multi-principal-element-alloys

display_name: multi-principal-element-alloys

universal-machine-learning-potentials

display_name: universal-machine-learning-potentials


materialscloud

en: Materials Cloud


envi

en: Environment


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


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.

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

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

electronic-memories

display_name: electronic-memories

electronic-structure

display_name: electronic-structure

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics

phase-change-materials

display_name: phase-change-materials


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

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

aiida

display_name: aiida

dft

display_name: dft

magnetic-exchanges

display_name: magnetic-exchanges


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

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

electron-phonon

display_name: electron-phonon

phosphors

display_name: phosphors

vibronic

display_name: vibronic


en: This entry provides the training and test datasets, final datasets, trained models, and simulation results associated with four machine learning interatomic potentials (MLIPs) of liquid water: DeePMD and MACE models trained from scratch via active learning, a fine-tuned MACE foundation model, and a DeePMD potential obtained by knowledge distillation. All input scripts for the active learning and AIMD simulations have been included. Source data for reproducing the figures in the manuscript are provided. Detailed explanations for each subdirectory are provided inside the folder.

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: 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


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:
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: Materials Cloud

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


envi

en: Environment


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

en: Multipoles as quantitative order parameters for altermagnetic spin splitting

altermagnetism

display_name: altermagnetism

dft

display_name: dft

multipoles

display_name: multipoles


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

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

condensed-matter-physics

display_name: condensed-matter-physics

hexagonal-ge

display_name: hexagonal-ge

model-bse

display_name: model-bse

optoelectronic-materials

display_name: optoelectronic-materials

polytypes

display_name: polytypes


en: 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

density-functional-theory

display_name: density-functional-theory

electronic-structure-descriptors

display_name: electronic-structure-descriptors

oxygen-activation

display_name: oxygen-activation

single-atom-catalyst

display_name: single-atom-catalyst

strain-engineering

display_name: strain-engineering

sulfur-vacancy

display_name: sulfur-vacancy

transition-metal-doping

display_name: transition-metal-doping


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

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

dislocation

display_name: dislocation

magnesium

display_name: magnesium

neural-network-potential

display_name: neural-network-potential


materialscloud

en: Materials Cloud


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

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

envi

en: Environment

electrocatalysis

display_name: electrocatalysis

oer

display_name: oer

operando

display_name: operando

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

synchrotron

display_name: synchrotron

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy

x-ray-diffraction

display_name: x-ray-diffraction


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

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

dft

display_name: dft

machine-learning

display_name: machine-learning

nanoclusters

display_name: nanoclusters


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

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

adsorption

display_name: adsorption

dimethyl-oxalate

display_name: dimethyl-oxalate

electron-density-difference

display_name: electron-density-difference

moox

display_name: moox


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

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

aliphatic-molecules

display_name: aliphatic-molecules

nanowires

display_name: nanowires

silicon

display_name: silicon

surface-adsorption

display_name: surface-adsorption


en:
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: Abstract [Accepted in Phys. Rev. Lett.]: Accurate prediction of carrier mobility is critical for the discovery and design of next-generation electronic materials. Despite sustained progress, state-of-the-art ab initio methods remain limited by the approximate treatment of electron-phonon interactions at the density functional theory level. Here, we demonstrate that incorporating many-body GW corrections to both the electronic band structure and electron-phonon couplings when solving the ab initio Boltzmann transport equation yields a mean absolute relative error of just 11% for electron mobilities across benchmark semiconductors, including Si, GaAs, GaP, diamond, and SiC. The common practice of neglecting GW corrections to the electron–phonon interaction can lead to mobility errors exceeding 50%. The present findings highlight the importance of many-body self-energy effects in carrier transport simulations, and provides fundamental insights into how many-body electron--phonon interactions govern charge transport in crystalline solids.

en: Supporting data for "Many-body first principles calculations of carrier mobility with record accuracy using GW perturbation theory"

aibte

display_name: aibte

carrier-transport

display_name: carrier-transport

gw

display_name: gw

gwpt

display_name: gwpt

mobility

display_name: mobility

quantum-many-body

display_name: quantum-many-body

semiconductors

display_name: semiconductors


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: The ethanol steam reforming reaction, together with the adsorption and decomposition of ethanol was studied on CeO2 and gallium-doped ceria (CeGaOx) by a combined experimental and theoretical approach using infrared spectroscopy (IR), mass spectrometry (MS) and density functional theory (DFT) calculations. At 100 °C, different types of monodentate ethoxy species were identified as standing-up (SU) on Ce4+ and lying-down (LD) on Ce4+ and Ga3+, with the alkyl chain more perpendicular or parallel to the surface, respectively. It is suggested that the incorporation of Ga into the ceria lattice changes the decomposition pathway of LD species, which converts to acetate instead of ethylene, attributed to the increased lattice oxygen lability in the Ce―O―Ga interface upon doping and the propensity to form Ga―H surface species. Under ethanol steam reforming conditions, Ga doping of ceria-based materials has a drastic effect by improving the H2:CO2 ratio, changing the product distribution and reducing coke formation.

en: Controlled selectivity for ethanol steam reforming reaction over doped CeO2 surfaces: The role of gallium

bioethanol

display_name: bioethanol

coke-precursors

display_name: coke-precursors

deactivation

display_name: deactivation

gallia

display_name: gallia

hydrogen-production

display_name: hydrogen-production

prace

display_name: prace


en: Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have been successfully synthesized or exfoliated. Here, we search for novel 2D materials that can be easily exfoliated from their parent compounds. Starting from 108423 unique, experimentally known three-dimensional compounds we identify a subset of 5619 that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van-der-Waals density-functional theory, validated against experimental structural data and calculated random-phase-approximation binding energies, allow to identify 1825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 compounds we explore vibrational, electronic, magnetic, and topological properties, identifying 56 ferromagnetic and antiferromagnetic systems, including half-metals and half-semiconductors. This archive entry contains the database of 2D materials (structural parameters, band structures, binding energies, phonons for the subset of the 258 easily exfoliable materials with less than 6 atoms, structures and binding energies for the remaining 1567 materials) together with the provenance of all data and calculations as stored by AiiDA.

materialscloud

en: Materials Cloud

en: Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

database

display_name: database

dft

display_name: dft

electronic-properties

display_name: electronic-properties

high-throughput

display_name: high-throughput

magnetic-properties

display_name: magnetic-properties

marvel

display_name: marvel

monolayer

display_name: monolayer

phonons

display_name: phonons

two-dimensional

display_name: two-dimensional

vibrational-properties

display_name: vibrational-properties


en: The energy level alignment across solvated molecule/semiconductor interfaces is a crucial property for the correct functioning of dye-sensitized photo-electrodes, where, following the absorption of solar light, a cascade of interfacial hole/electron transfer processes has to efficiently take place. In light of the difficulty of performing X-ray photoelectron spectroscopy measurements at the molecule/solvent/metal-oxide interface, being able to accurately predict the level alignment by first-principles calculations on realistic structural models would represent an important step toward the optimization of the device. In this respect dye/NiO surfaces, employed in p-type dye-sensitized solar cells, are undoubtedly challenging for ab initio methods and, also for this reason, much less investigated than the n-type dye/TiO2 counterpart. Here we consider the C343-sensitized NiO surface in water and combine ab initio Molecular Dynamics (AIMD) simulations with GW (G0W0) calculations, performed along the MD trajectory, to reliably describe the structure and energetics of the interface when explicit solvation and finite temperature effects are accounted for. We show that the differential perturbative correction on the NiO and molecule states obtained at GW level is mandatory to recover the correct (physical) interfacial energetics, allowing hole transfer from the semiconductor valence band to the HOMO of the dye. Moreover, the calculated average driving force quantitatively agrees with the experimental estimate.

en: Structure and energetics of dye-sensitized NiO interfaces in water from ab-initio MD and large-scale GW calculations

envi

en: Environment

ab-initio

display_name: ab-initio

energy-level-alignment

display_name: energy-level-alignment

gw

display_name: gw

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nio

display_name: nio

p-type-dssc

display_name: p-type-dssc


en:
The high-throughput molecular exploration and screening of organic electronic materials often starts with either a 'top-down' mining of existing repositories, or the 'bottom-up' assembly of fragments based on predetermined rules and known synthetic templates. In both instances, the datasets used are often produced on a case-by-case basis, and require the high-quality computation of electronic properties and extensive user input: curation in the top-down approach, and the construction of a fragment library and introduction of rules for linking them in the bottom-up approach. Both approaches are time-consuming and require significant computational resources. Here, we generate a top-down set named FORMED consisting of 117K synthesized molecules containing their optimized structures, associated electronic and topological properties and chemical composition, and use these structures as a vast library of molecular building blocks for bottom-up fragment-based materials design. A tool is developed to automate the coupling of these building block units based on their available Csp2-H bonds, thus providing a fundamental link between the two philosophies of dataset construction. Statistical models are trained on this dataset and a subset of the resulting hybrid top-down/bottom-up compounds (selected dimers), which enable on-the-fly prediction of key ground state (frontier molecular orbital gaps) and excited state (S1 and T1 energies) properties from molecular geometries with high accuracy across all known p-block organic compound space.
With access to ab initio-quality optical properties in hand, it is possible to apply this bottom-up pipeline using existing compounds as molecular building blocks to any materials design campaign. To illustrate this, we construct and screen over a million molecular candidates (predicted dimers) for efficient intramolecular singlet fission, the leading candidates of which provide insight into the structural features that may promote this multiexciton-generating process.

en: Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design

crystal-structures

display_name: crystal-structures

donor-acceptor-copolymers

display_name: donor-acceptor-copolymers

optical-properties

display_name: optical-properties

organic-molecules

display_name: organic-molecules

photophysical-properties

display_name: photophysical-properties


en: The chemical versatility of carbon imparts manifold properties to organic compounds, where magnetism remains one of the most desirable but elusive. Polycyclic aromatic hydrocarbons, also referred to as nanographenes, show a critical dependence of electronic structure on the topologies of the edges and the π-electron network, which makes them model systems with which to engineer unconventional properties including magnetism. In 1972, Erich Clar envisioned a bow-tie-shaped nanographene, C38H18, where topological frustration in the π-electron network renders it impossible to assign a classical Kekulé structure without leaving unpaired electrons, driving the system into a magnetically non-trivial ground state. In this record we include data needed to support our recent work where we demonstrate the experimental realization and in-depth characterization of this emblematic nanographene, known as Clar's goblet. Scanning tunnelling microscopy and spin excitation spectroscopy of individual molecules on a gold surface reveal a robust antiferromagnetic order with an exchange-coupling strength of 23 meV, exceeding the Landauer limit of minimum energy dissipation at room temperature. Through atomic manipulation, we realize switching of magnetic ground states in molecules with quenched spins. Our results provide direct evidence of carbon magnetism in a hitherto unrealized class of nanographenes, and prove a long-predicted paradigm where topological frustration entails unconventional magnetism, with implications for room-temperature carbon-based spintronics.

en: Topological frustration induces unconventional magnetism in a nanographene

clar-goblet

display_name: clar-goblet

erc

display_name: erc

kondo

display_name: kondo

nanographene

display_name: nanographene

snsf

display_name: snsf


en: This archive includes the raw data for atomistic simulations in the work as titled. Transition to a hydrogen-based economy requires a thorough understanding of hydrogen interaction with dislocations in metals, especially in body-centered cubic (BCC) steels. Past experimental and computational investigations regarding these interactions often demonstrate two opposing results: hydrogen-induced mobility or hydrogen-induced pinning of dislocations. Through in-situ scanning electron microscopy experiments enabled by a custom-built setup, we address here this discrepancy. Our experiments reveal hydrogen-induced dislocation motion in a BCC metal at room temperature. Interestingly however, we also observe that the same dislocations are later pinned as well, again induced by the steady hydrogen flux. Molecular dynamics simulations of the phenomena confirm the attraction of the dislocations towards the hydrogen flux, and the pinning that follows after, upon increased hydrogen trapping at the dislocation core. Future experimental or computational studies of hydrogen thus should take into account these different regimes in order to present a full picture of hydrogen defect interactions.

en: Hydrogen can both move or pin dislocations in Body-Centered Cubic metals

body-centered-cubic-metal

display_name: body-centered-cubic-metal

dislocation-hydrogen-interactions

display_name: dislocation-hydrogen-interactions

hydrogen-embrittlement

display_name: hydrogen-embrittlement


a-unified-approach-to-enhanced-sampling

en: The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested towards its solution. These methods are often grouped into two broad families. On the one hand methods such as umbrella sampling and metadynamics that build a bias potential based on few order parameters or collective variables. On the other hand tempering methods such as replica exchange that combine different thermodynamic ensembles in one single expanded ensemble. We adopt instead a unifying perspective, focusing on the target probability distribution sampled by the different methods. This allows us to introduce a new method that can sample any of the ensembles normally sampled via replica exchange, but does so in a collective-variables-based scheme. This method is an extension of the recently developed on-the-fly probability enhanced sampling method [Invernizzi and Parrinello, J. Phys. Chem. Lett. 11.7 (2020)] that has been previously used for metadynamics-like sampling. The method is thus very general and can be used to achieve different types of enhanced sampling. It is also reliable and simple to use, since it presents only few and robust external parameters and has a straightforward reweighting scheme. Furthermore, it can be used with any number of parallel replicas. We show the versatility of our approach with applications to multicanonical and multithermal-multibaric simulations, thermodynamic integration, umbrella sampling, and combinations thereof.

en: A unified approach to enhanced sampling

title_for_slug: a-unified-approach-to-enhanced-sampling

expanded-ensembles

display_name: expanded-ensembles

free-energy

display_name: free-energy

importance-sampling

display_name: importance-sampling

metadynamics

display_name: metadynamics

parallel-tempering

display_name: parallel-tempering

replica-exchange

display_name: replica-exchange

thermodynamic-integration

display_name: thermodynamic-integration

umbrella-sampling

display_name: umbrella-sampling


en: The data contained in this record, raw data of images and input files to reproduce calculations, support our recent report for the on-surface synthesis and characterization of two ultralow-gap open-shell molecules, namely peri-tetracene, a benzenoid graphene fragment with zigzag edge topology, and dibenzo[a,m]dicyclohepta[bcde,nopq]rubicene, a nonbenzenoid nonalternant structural isomer of peri-tetracene with two embedded azulene units. Our results provide an understanding of the ramifications of altered bond topologies at the single-molecule scale, with the prospect of designing functionalities in carbon-based nanostructures via engineering of bond topology

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: Tailoring Bond Topologies in Open-Shell Graphene Nanostructures

atom-manipulation

display_name: atom-manipulation

density-functional-theory

display_name: density-functional-theory

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy

scanning-tunneling-spectroscopy

display_name: scanning-tunneling-spectroscopy


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

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

antisite-defect

display_name: antisite-defect

multiferroic

display_name: multiferroic


en: Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

materialscloud

en: Materials Cloud

en: On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events

bayesian-inference

display_name: bayesian-inference

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en: High nickel content in LiNixCoyMnzO2 (NCM, x ≥ 0.8, x + y + z = 1) layered cathode material allows high energy density in lithium-ion batteries (LIBs). However, Ni-rich NCM cathodes suffer from performance degradation, mechanical and structural instability upon prolonged cell cycling. Although the use of single-crystal Ni-rich NCM can mitigate these drawbacks, the ion-diffusion in large single-crystal particles hamper its rate capability. Herein, we report a strategy to construct an in situ Li1.4Y0.4Ti1.6(PO4)3 (LYTP) ion/electron conductive network which interconnects single-crystal LiNi0.88Co0.09Mn0.03O2 (SC-NCM88) particles. The LYTP network facilitates the lithium-ion transport between SC-NCM88 particles, mitigates mechanical instability and prevents detrimental crystalline phase transformation. When used in combination with a Li metal anode, the LYTP-containing SC-NCM88-based cathode enables a coin cell capacity of 130 mAh g-1 after 500 cycles at 5 C rate in the 2.75-4.4 V range at 25 °C. Tests in Li-ion pouch cell configuration (i.e., graphite used as negative electrode active material) demonstrate capacity retention of 85% after 1000 cycles at 0.5 C in the 2.75-4.4 V range at 25 °C for the LYTP-containing SC-NCM88-based positive electrode.

en: In situ inorganic conductive network enables superior high-voltage operation of single-crystal Ni-rich cathode

envi

en: Environment

conductive-network

display_name: conductive-network

crystalline-phase-transformation

display_name: crystalline-phase-transformation

experimental

display_name: experimental

in-situ

display_name: in-situ

lithium-ion-transport

display_name: lithium-ion-transport

single-crystal-ni-rich-ncm

display_name: single-crystal-ni-rich-ncm


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

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

en: GapExtractor v2.0

band-gap-energy

display_name: band-gap-energy

diffuse-reflectance-spectroscopy

display_name: diffuse-reflectance-spectroscopy

polyphasic-samples

display_name: polyphasic-samples

tauc-method

display_name: tauc-method


en: We present a first-principles investigation of the structural, electronic, and magnetic properties of the pristine and Fe-doped α-MnO₂ using density-functional theory with extended Hubbard functionals. The onsite U and intersite V Hubbard parameters are determined from first principles and self-consistently using density-functional perturbation theory in the basis of Löwdin-orthogonalized atomic orbitals. First, we analyze the pristine α-MnO₂ and show that the C2-AFM spin configuration is the most energetically favorable, in agreement with the experimentally observed antiferromagnetic state. For the Fe-doped α-MnO₂ two types of doping are considered: Fe insertion in the 2 × 2 tunnels and partial substitution of Fe for Mn. The calculated formation energies show that the experimentally observed Fe insertion is energetically favorable only when intersite Hubbard interactions are taken into account. Moreover, we find that both types of doping preserve the C2-AFM spin configuration of the host lattice only when Hubbard V corrections are included. The oxidation state of Fe is found to be +2 and +4 in the case of the interstitial and substitutional doping, respectively, while the oxidation state of Mn is +4 in both cases. This work opens a door for accurate studies of other Mn oxides and complex transition-metal compounds when the localization of 3d electrons occurs in the presence of strong covalent interactions with ligands.

en: Pivotal role of intersite Hubbard interactions in Fe-doped α-MnO₂

band-gap

display_name: band-gap

bond-angles

display_name: bond-angles

bond-length

display_name: bond-length

crystal-structure

display_name: crystal-structure

density-functional-theory

display_name: density-functional-theory

density-of-states

display_name: density-of-states

dftu

display_name: dftu

dftuv

display_name: dftuv

hubbard-parameters

display_name: hubbard-parameters

hubbard-projectors

display_name: hubbard-projectors

interstitial-doping

display_name: interstitial-doping

magnetic-moment

display_name: magnetic-moment

orthogonalized-atomic-orbitals

display_name: orthogonalized-atomic-orbitals

oxidation-state

display_name: oxidation-state

self-interactions

display_name: self-interactions

spin-configuration

display_name: spin-configuration

substitutional-doping

display_name: substitutional-doping


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: Materials composed of elements from the third and fifth columns of the periodic table display a very rich behavior, with the phase diagram usually containing a metallic liquid phase and a polar semiconducting solid. As a consequence, it is very hard to achieve transferable empirical models of interactions between the atoms that can reliably predict their behavior across the temperature and composition range that is relevant to the study of the synthesis and properties of III/V nanostructures and devices. We present a machine-learning potential trained on density functional theory reference data that provides a general-purpose model for the Ga/As system. We provide a series of stringent tests that showcase the accuracy of the potential, and its applicability across the whole binary phase space, computing with ab initio accuracy a large number of finite-temperature properties as well as the location of phase boundaries. We also show how a committee model can be used to reliably determine the uncertainty induced by the limitations of the machine-learning model on its predictions, to identify regions of phase space that are predicted with insufficient accuracy, and to iteratively refine the training set to achieve consistent, reliable modeling.

en: Modeling the Ga/As binary system across temperatures and compositions from first principles

gaas

display_name: gaas

gallium-arsenide

display_name: gallium-arsenide

marvel

display_name: marvel

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potential

display_name: neural-network-potential

snsf

display_name: snsf


en: This dataset contains experimental and theoretical results from the study of heat transport in suspended single-crystal diamond cantilevers with sub-micrometer widths. "Imaging heat transport in suspended diamond nanostructures with integrated spindefect thermometers", Physical Review Letters, 2026Temperature profiles are measured using embedded nitrogen-vacancy (NV) spin defects acting as in-situ thermometers, enabling spatially resolved thermal mapping under laser heating. The data include measured temperature gradients, extracted effective thermal conductivities as a function of cantilever width, and first-principles simulations based on the linearized phonon Boltzmann transport equation and viscous heat equations. The combined experimental and computational results demonstrate non-diffusive, sub-ballistic heat transport arising from the interplay of intrinsic phonon scattering and boundary effects.

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: Imaging heat transport in suspended diamond nanostructures with integrated spin defect thermometers

diamond-nanostructures

display_name: diamond-nanostructures

heat-transport

display_name: heat-transport

nanoscale-thermometry

display_name: nanoscale-thermometry

nitrogen-vacancy-centers

display_name: nitrogen-vacancy-centers

non-diffusive-thermal-conduction

display_name: non-diffusive-thermal-conduction

phonon-boltzmann-transport-equation

display_name: phonon-boltzmann-transport-equation

viscous-heat-equations

display_name: viscous-heat-equations


en: CeO₂ greatly enhances the electrocatalytic oxygen evolution reaction (OER) activity of CoOx, though the enhancement mechanism beyond this synergy is yet to be understood. Here, operando hard X-ray adsorption spectroscopy (hXAS) is applied to monitor the Co K edge and Ce L₃ edge in CoOx/CeO₂ to shed light on the evolution of the Co and Ce oxidation states during OER. In addition, ex situ soft XAS (sXAS) characterizations provide information on the irreversible surface-specific transformations of the Co L₃ edge as well as of the O K edge. Combining the operando and ex situ spectroscopic characterizations with comprehensive electrochemical analyses, we confirm CeO₂ is not the active center for the OER. However, coupling CeO₂ with CoOx introduces significant modifications in the Co and O species at the CoOx surface and alters the flat band potential (Efb), leading to more favorable Co oxidation state transformations during OER and possibly modifying the preferential reaction pathway. This work establishes the connections between electronic structures, Co oxidation state and the OER reaction mechanism for CoOx/CeO₂ composites electrodes.

en: Operando tracking the interactions between CoOx and CeO₂ during oxygen evolution reaction

co-oxidation

display_name: co-oxidation

co-redox

display_name: co-redox

operando-xas

display_name: operando-xas

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en:
We present JuDiT (Jülich Database of impurities embedded into a Topological insulator) which collects first principles calculation of impurities embedded into the prototypical topological insulator Sb2Te3. The density functional calculations of this work were performed with the JuKKR package [1], which allows to embed translational invariance breaking impurities into a crystalline host system based on the Korringa-Kohn-Rostoker Green function method, and were performed with the AiiDA-KKR package [2]. Our database collects, among others, predicted impurity properties like charge doping introduced by the defects, magnetic moments of the impurities and impurity density of states calculations. We include calculations for the intrinsic Fermi level in the middle of the bulk band gap as well as for shifted Fermi level into valence and conduction band which models different experimental conditions. The impurities were embedded into different layers throughout a 6 quintuple layer thick film which allows to investigate the effect of the topological surface state (localized at the surface) on impurity properties. The JuDiT database allows to uncover chemical trends in impurity properties and can help optimizing the next generation of topological materials.

[1] https://jukkr.fz-juelich.de and www.judft.de
[2] https://github.com/JuDFTteam/aiida-kkr

materialscloud

en: Materials Cloud

en: The JuDiT database of impurities embedded into a topological insulator

aiida-kkr

display_name: aiida-kkr

density-functional-theory

display_name: density-functional-theory

impurities

display_name: impurities

impurity

display_name: impurity

impurity-density-of-states

display_name: impurity-density-of-states

impurity-properties

display_name: impurity-properties

judit

display_name: judit

kkr

display_name: kkr

max

display_name: max

quantum-anomalous-hall-insulator

display_name: quantum-anomalous-hall-insulator

topological-insulator

display_name: topological-insulator

viti

display_name: viti


en: Doped alkaline-earth chalcogenides are interesting photoluminescent materials for opto-electronic applications. It is crucial to have an extended knowledge about the undoped bulk CaS and CaO since all the excited state properties of the doped material heavily depend on it. In this work we investigate the structural parameters, electronic band structures, macroscopic dielectric constants and absorption spectra for CaS and CaO compounds. Their quasi-particle band structure in the GW approximation yields a value of 4.28 eV and 6.02 eV for the indirect theoretical particle gap of CaS and CaO, respectively. The imaginary part of the macroscopic dielectric function e(omega) is computed including excitonic effects through the Bethe–Salpeter equation. The onset of absorption is within 0.1 eV of the experimental one and the calculated spectrum shows a qualitative agreement with experiment. Our computed exciton binding energies are 0.27 eV and 0.40 eV for CaS and CaO, respectively.

en: First-principles and experimental characterization of the electronic and optical properties of CaS and CaO

envi

en: Environment

ab-initio

display_name: ab-initio

absorption-spectrum

display_name: absorption-spectrum

bethesalpeter

display_name: bethesalpeter

calcium-chalcogenides

display_name: calcium-chalcogenides

cao

display_name: cao

cas

display_name: cas

exciton

display_name: exciton

first-principles

display_name: first-principles

macroscopic-dielectric-function

display_name: macroscopic-dielectric-function


en: Designing an appropriate set of collective variables is crucial to the success of several enhanced sampling methods. Here we focus on how to obtain such variables from information limited to the metastable states. We characterize these states by a large set of descriptors and employ neural networks to compress this information in a lower-dimensional space, using Fisher's linear discriminant as an objective function to maximize the discriminative power of the network. We test this method on alanine dipeptide, using the non-linearly separable dataset composed by atomic distances. We then study an intermolecular aldol reaction characterized by a concerted mechanism. The resulting variables are able to promote sampling by drawing non-linear paths in the physical space connecting the fluctuations between metastable basins. Lastly, we interpret the behavior of the neural network by studying its relation to the physical variables. Through the identification of its most relevant features, we are able to gain chemical insight into the process.

en: Data-Driven Collective Variables for Enhanced Sampling

collective-variables

display_name: collective-variables

enhanced-sampling

display_name: enhanced-sampling

erc

display_name: erc

metadynamics

display_name: metadynamics

neural-networks

display_name: neural-networks

opes

display_name: opes

plumed

display_name: plumed


en: In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies). This data entry contains all data to reproduce the results, as well as the resulting curated all-electron dataset and the scripts to generate the figures of the paper.

en: How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

automation

display_name: automation

dft

display_name: dft

equation-of-state

display_name: equation-of-state

pseudopotentials

display_name: pseudopotentials

verification

display_name: verification