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

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).

coo

display_name: coo

dftu

display_name: dftu

dftuv

display_name: dftuv

feo

display_name: feo

mno

display_name: mno

nio

display_name: nio

zno

display_name: zno


$oid: OBSOLETE

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

materialscloud

approval_status: approved

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

dft

display_name: dft

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

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

allen-cahn-equation

display_name: allen-cahn-equation

convolutional-recurrent-neural-network

display_name: convolutional-recurrent-neural-network

crystal-growth

display_name: crystal-growth

surrogate-model

display_name: surrogate-model


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

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

accommodation-coefficients

display_name: accommodation-coefficients

air

display_name: air

alumina

display_name: alumina

coating

display_name: coating

defects

display_name: defects

drag

display_name: drag

graphene

display_name: graphene

graphite

display_name: graphite

rarefied-gas

display_name: rarefied-gas


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

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

creep-test

display_name: creep-test

nuclear-structural-materials

display_name: nuclear-structural-materials

size-effect

display_name: size-effect

sub-sized-specimens

display_name: sub-sized-specimens


en: 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

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).

graphene

display_name: graphene

reverse-osmosis

display_name: reverse-osmosis

solventsolvent-separation

display_name: solventsolvent-separation


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

materialscloud

en: Materials Cloud

battery-materials

display_name: battery-materials

correlated-electron-materials

display_name: correlated-electron-materials

flat-band

display_name: flat-band

materials-informatics

display_name: materials-informatics


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

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

envi

en: Environment

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

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

biocondensates

display_name: biocondensates

chemfit

display_name: chemfit

coarse-grained-model

display_name: coarse-grained-model

computational-physics

display_name: computational-physics

lennard-jones

display_name: lennard-jones

parameter-optimization

display_name: parameter-optimization

water-model

display_name: water-model


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

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

en: Performance improvement of deorbitalized exchange-correlation functionals

computation

display_name: computation

density-functional-theory

display_name: density-functional-theory

deorbitalization

display_name: deorbitalization

metagga

display_name: metagga


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

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

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

irradiation-damage

display_name: irradiation-damage

lattice-distortion

display_name: lattice-distortion

metallic-alloys

display_name: metallic-alloys


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

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).

altermagnetism

display_name: altermagnetism

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"

materialscloud

en: Materials Cloud

condensed-matter-physics

display_name: condensed-matter-physics

dft

display_name: dft

hexagonal-ge

display_name: hexagonal-ge

model-bse

display_name: model-bse

optoelectronic-materials

display_name: optoelectronic-materials

polytypes

display_name: polytypes


en: 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

envi

en: Environment

cohp

display_name: cohp

electronic-structure-descriptors

display_name: electronic-structure-descriptors

oxygen-activation

display_name: oxygen-activation

single-atom-catalyst

display_name: single-atom-catalyst

strain-engineering

display_name: strain-engineering

sulfur-vacancy

display_name: sulfur-vacancy

transition-metal-doping

display_name: transition-metal-doping


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

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

dislocation

display_name: dislocation

magnesium

display_name: magnesium

neural-network-potential

display_name: neural-network-potential


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

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

electrocatalysis

display_name: electrocatalysis

oer

display_name: oer

operando

display_name: operando

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

synchrotron

display_name: synchrotron

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy

x-ray-diffraction

display_name: x-ray-diffraction


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

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

dft

display_name: dft

machine-learning

display_name: machine-learning

nanoclusters

display_name: nanoclusters


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

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

adsorption

display_name: adsorption

dimethyl-oxalate

display_name: dimethyl-oxalate

electron-density-difference

display_name: electron-density-difference

moox

display_name: moox


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

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

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

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

aiida

display_name: aiida

anomalous-density

display_name: anomalous-density

bogoliubov-de-gennes-bdg

display_name: bogoliubov-de-gennes-bdg

co

display_name: co

coherent-potential-approximation-cpa

display_name: coherent-potential-approximation-cpa

kkr-green-function-method

display_name: kkr-green-function-method

magnetic-impurities

display_name: magnetic-impurities

mn

display_name: mn

pair-breaking

display_name: pair-breaking

spin-orbit-coupling

display_name: spin-orbit-coupling

superconductivity

display_name: superconductivity

transition-metal-dichalcogenide

display_name: transition-metal-dichalcogenide

yu-shiba-rusinov-states

display_name: yu-shiba-rusinov-states


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

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

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).

active-learning

display_name: active-learning

crystal-structure-prediction

display_name: crystal-structure-prediction

high-pressure-ice

display_name: high-pressure-ice

ice-rules

display_name: ice-rules

ice-vii

display_name: ice-vii

machine-learning-force-fields

display_name: machine-learning-force-fields


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

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

materialscloud

en: Materials Cloud

aiida

display_name: aiida

dft

display_name: dft

generative-ai

display_name: generative-ai

machine-learning

display_name: machine-learning

mlip

display_name: mlip


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

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

envi

en: Environment

hydrogen-boride-nanosheet

display_name: hydrogen-boride-nanosheet

hydrogen-release

display_name: hydrogen-release

interlayer-space

display_name: interlayer-space

two-dimensional-materials

display_name: two-dimensional-materials


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

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

input-files

display_name: input-files

output-files

display_name: output-files

quantum-espresso

display_name: quantum-espresso


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

en: Electron correlation in semiconductors and insulators via symbolic regression

electronic-structure

display_name: electronic-structure

equation-discovery

display_name: equation-discovery

gw

display_name: gw

many-body-perturbation-theory

display_name: many-body-perturbation-theory

symbolic-regression

display_name: symbolic-regression


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

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

anisotropy

display_name: anisotropy

bone-substitutes

display_name: bone-substitutes

characterization

display_name: characterization

experimental

display_name: experimental

fracture-toughness

display_name: fracture-toughness

sawbones

display_name: sawbones


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

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

interface-passivation

display_name: interface-passivation

magnesium-electrodeposition

display_name: magnesium-electrodeposition

magnesium-metal-negative-electrodes

display_name: magnesium-metal-negative-electrodes

twinning

display_name: twinning


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

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

atomistic-simulations

display_name: atomistic-simulations

dataset

display_name: dataset

density-functional-theory

display_name: density-functional-theory

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning

universal-models

display_name: universal-models


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

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

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).

all-optical-control-in-bulk-crystals

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

nonlinear-optical-phase-matching

display_name: nonlinear-optical-phase-matching

nonlinear-susceptibility

display_name: nonlinear-susceptibility

resonant-lattice-dynamics

display_name: resonant-lattice-dynamics

second-harmonic-generation

display_name: second-harmonic-generation

terahertz-phonon-excitation

display_name: terahertz-phonon-excitation


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

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

materialscloud

en: Materials Cloud

d-d-excitations

display_name: d-d-excitations

dftu

display_name: dftu

resonant-inelastic-x-ray-scattering

display_name: resonant-inelastic-x-ray-scattering

shastry-sutherland-model

display_name: shastry-sutherland-model


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

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

envi

en: Environment

database

display_name: database

interatomic-potential

display_name: interatomic-potential


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

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

recessed-contact

display_name: recessed-contact

schottky-barrier

display_name: schottky-barrier


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

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

active-learning

display_name: active-learning

deepmd

display_name: deepmd

knowledge-distillation

display_name: knowledge-distillation

mace

display_name: mace

machine-learning-interatomic-potential

display_name: machine-learning-interatomic-potential


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

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

dft

display_name: dft

electron-phonon-coupling

display_name: electron-phonon-coupling


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

en: Noncollinear kagome magnet driven by giant antisymmetric exchange

diffuse-magnetic-scattering

display_name: diffuse-magnetic-scattering

spinw

display_name: spinw


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

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

electronic-structure

display_name: electronic-structure


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

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

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).

free-energy

display_name: free-energy

lammps

display_name: lammps

molecular-dynamics

display_name: molecular-dynamics

statistical-mechanics

display_name: statistical-mechanics


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

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

en: How unconstrained machine-learning models learn physical symmetries

materialscloud

en: Materials Cloud

atomistic-simulations

display_name: atomistic-simulations

cscs

display_name: cscs

equivariance

display_name: equivariance

erc

display_name: erc

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning

snsf

display_name: snsf


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

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

The pseudopotential files are truncated at rcut=5.99 bohr.

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

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

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

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

envi

en: Environment

automation

display_name: automation

equation-of-state

display_name: equation-of-state

pseudopotentials

display_name: pseudopotentials

verification

display_name: verification


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

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

carrier-mobility

display_name: carrier-mobility

magnetism

display_name: magnetism

transparent-conducting-oxides

display_name: transparent-conducting-oxides


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

en: In search of novel ductile superconductors

ductility

display_name: ductility

elastic-tensor

display_name: elastic-tensor

generalized-stacking-fault-energy

display_name: generalized-stacking-fault-energy

pettifors-ratio

display_name: pettifors-ratio

pughs-ratio

display_name: pughs-ratio

rices-ratio

display_name: rices-ratio

superconductor

display_name: superconductor

surface-energy

display_name: surface-energy


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

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

en: Dielectric-dependent hybrid functional from meta-GGA

density-functional-theory

display_name: density-functional-theory

dft

display_name: dft

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure


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

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

ceria-inverse-catalysts

display_name: ceria-inverse-catalysts

dftu

display_name: dftu

lanthanide-doping

display_name: lanthanide-doping

leem

display_name: leem

oxygen-vacancies

display_name: oxygen-vacancies

redox-dynamics

display_name: redox-dynamics


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

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

alkali-promotion

display_name: alkali-promotion

cohp

display_name: cohp

nitrogen-activation

display_name: nitrogen-activation

single-atom-catalysis

display_name: single-atom-catalysis


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

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

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).

fair-principle

display_name: fair-principle

llm-agent

display_name: llm-agent

materials-informatics

display_name: materials-informatics

multi-principal-element-alloys

display_name: multi-principal-element-alloys

universal-machine-learning-potentials

display_name: universal-machine-learning-potentials


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

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

materialscloud

en: Materials Cloud

dft

display_name: dft

reaction-pathway

display_name: reaction-pathway

vibrational-properties

display_name: vibrational-properties


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

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

envi

en: Environment

proximity-effect

display_name: proximity-effect

superconductivity

display_name: superconductivity

topological-materials

display_name: topological-materials


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

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

active-sites

display_name: active-sites

irras

display_name: irras

magnesium-oxide

display_name: magnesium-oxide

schottky-defects

display_name: schottky-defects

step-edges

display_name: step-edges


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

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

automated-reaction-enumeration

display_name: automated-reaction-enumeration

machine-learning

display_name: machine-learning

microkinetic-modeling

display_name: microkinetic-modeling

themocatalysis

display_name: themocatalysis


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

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

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

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

spin-phonon-coupling

display_name: spin-phonon-coupling


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

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

copper

display_name: copper

gallium

display_name: gallium

isolated-dual-atom-sites

display_name: isolated-dual-atom-sites

methanol

display_name: methanol


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

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

first-principles-spectra-database

display_name: first-principles-spectra-database

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


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

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

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).

halide-based-sse

display_name: halide-based-sse

machine-learning-potential

display_name: machine-learning-potential

solid-state-electrolytes

display_name: solid-state-electrolytes

universal-machine-learning-potential

display_name: universal-machine-learning-potential


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

materialscloud

en: Materials Cloud

phonons

display_name: phonons

spectral-function

display_name: spectral-function