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

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

crystallization

display_name: crystallization

electronic-memories

display_name: electronic-memories

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics

phase-change-materials

display_name: phase-change-materials


$oid: OBSOLETE

en: This file contains all the data, as well as the code necessary to reproduce the exact diagonalization calculations in Phys. Rev. Lett. 133, 076502 (2024) in which the phenomenon of a non-Hermitian Mott skin effect is introduced theoretically. Skin effects are a key differentiating feature of (one-dimensional) non-Hermitian systems, and are characterized by the exponential accumulation of charge towards one side of the system in all its eigenstates. It appears in noninteracting systems. This work finds a novel analogous effect which is specific to interacting quantum particles, where the particle density is constant, but a flavour/spin degree of freedom shows the exponential localization. This is demonstrated with exact diagonalization calculations of the non-Hermitian many-body Hamiltonian as well as the time-evolution of the Lindbladian.

en: Non-Hermitian Mott skin effect

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

correlated-electron-systems

display_name: correlated-electron-systems

exact-diagonalization

display_name: exact-diagonalization

mott-insulators

display_name: mott-insulators

non-hermitian-systems

display_name: non-hermitian-systems

skin-effect

display_name: skin-effect


en:
The intermetallic quasi-one-dimensional binary superconductor V2Ga5 was recently found to exhibit a topologically nontrivial normal state, making it a natural candidate for a topological superconductor.
By combining dc-magnetization, nuclear magnetic resonance, and muon-spin rotation (µSR) measurements on high-quality V2Ga5 single crystals, we investigate the electronic properties of its normal- and superconducting ground states. NMR measurements in the normal state indicate a strong anisotropy in both the line shifts and the relaxation rates. Such anisotropy persists also in the superconducting state, as shown by the magnetization and µSR-spectroscopy results. In the latter case, data collected at different temperatures, pressures, and directions of the magnetic field (with respect to the crystalline axes) evidence a fully-gapped, strongly anisotropic superconductivity. At the same time, hydrostatic pressure is shown to only lower the Tc value, but not to change the superfluid density nor its temperature dependence. Lastly, we discuss the search for topological signatures in the normal state of V2Ga5, as well as a peak splitting in the FFT of the µSR spectrum, possibly related to an unconventional vortex lattice. Our results suggest that V2Ga5 is a novel system, whose anisotropy plays a key role in determining its unusual electronic properties.

en: Anisotropic superconductivity in the quasi-one-dimensional superconductor

nuclear-magnetic-resonance

display_name: nuclear-magnetic-resonance

superconductivity

display_name: superconductivity


en: Solidification control is crucial in manufacturing technologies, as it determines the microstructureand,  consequently,  the performance of the final product.  Investigating the mechanisms occurring during the early stages of nucleation remains experimentally challenging as it initiates on nanometer length and sub-picoseconds time scales.  Large scale molecular dynamics simulations using machinelearning interatomic potential with quantum accuracy appears the dedicated approach to complex,atomic level, multidimensional mechanisms with local symmetry breaking.  A potential trained ona high-dimensional neural network on density functional theory-based ab initio molecular dynamics(AIMD) trajectories for liquid and undercooled states for Al-Si binary alloys enables us to study the  nucleation  mechanisms  occurring  at  the  early  stages  from  the  liquid  phase  near  the  eutectic composition.  Our results indicate that nucleation starts with Al at hypoeutectic compositions and with Si at hypereutectic compositions.  Whereas Al nuclei grow in a globular shape, Si ones grow with polygonal faceting, whose underlying mechanisms are further discussed.

en: Crystal nucleation in eutectic Al-Si alloys by machine-learned molecular dynamics

aluminum-silicon

display_name: aluminum-silicon

hdnnp

display_name: hdnnp

machine-learning-interatomic-potential

display_name: machine-learning-interatomic-potential


en:
Long-range electrostatic interactions critically affect polar materials. However, state-of-the-art atomistic potentials, such as neural networks or Gaussian approximation potentials employed in large-scale simulations, often neglect the role of these long-range electrostatic interactions.This study introduces a novel model derived from first principles to evaluate the contribution of long-range electrostatic interactions to total energies, forces, and stresses. The model is designed to integrate seamlessly with existing short-range force fields without further first-principles calculations or retraining. The approach relies solely on physical observables, like the dielectric tensor and Born effective charges, that can be consistently calculated from first principles. We demonstrate that the model reproduces critical features, such as the LO-TO splitting and the long-wavelength phonon dispersions of polar materials, with benchmark results on the cubic phase of barium titanate (BaTiO3).

This dataset reports the raw dynamical matrices computed for BaTiO3 using DFPT, the bare short-range GAP potential, and the long-range corrected GAP potential.  A more accurate description of the methodology and all the parameters used to reproduce these data are discussed and reported in the referenced paper.

en: Electrostatic interactions in atomistic and machine-learned potentials for polar material: data

dft

display_name: dft

electrostatic-interactions

display_name: electrostatic-interactions

phonons

display_name: phonons


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

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

energy-landscape

display_name: energy-landscape

first-principles

display_name: first-principles

hubbard-corrections

display_name: hubbard-corrections

marvel

display_name: marvel

pbe

display_name: pbe

pbeu

display_name: pbeu

quantum-espresso

display_name: quantum-espresso

romeodft

display_name: romeodft

two-dimensional-magnetic-materials

display_name: two-dimensional-magnetic-materials


en:
The adsorption energies of perfluorobutanesulfonic acid, perfluorobutanoic acid, and trifluoroacetic acid on functionalized activated carbon are calculated from first principles. We introduce a novel approach based on a thermochemical cycle and a continuum solvation model to address neutral and charged adsorption complexes and account for concentration, pH, and pore-size effects. The results highlight the benefit of N-based functional groups for enhancing the removal of challenging, short-chain perfluoroalkyl substances from water by activated carbon.

This record includes the output files of Quantum ESPRESSO relaxation calculations of PFAS adsorption on functionalized activated carbon.

en: Adsorption of Short-Chain Perfluoroalkyl Substances (PFAS) on Functionalized Activated Carbon from First Principles

activated-carbon

display_name: activated-carbon

pfas

display_name: pfas


en: We report a first-principles study of the electronic and optical properties of recently identified one-dimensional semiconducting materials exfoliable from van der Waals-bonded bulk crystals. Specifically, we investigate four chalcogenide-based atomic chains, the covalently bonded S3 and Te3 chains, and polar-bonded As2S3 and Bi2Te3 chains, using a fully first-principles approach that combines density functional theory (DFT), density functional perturbation theory (DFPT), and many-body perturbation theory within the GW approximation and Bethe−Salpeter equation (BSE). Our vibrational analysis shows that the isolated, freestanding wires remain dynamically stable, with the zone-center optical phonon modes leading to infrared activity. The main finding of this study is the presence of very strong exciton binding energies (1−3 eV), which make these exfoliable 1D materials suitable platforms for room-temperature excitonic applications. Interestingly, the exciton character remains Wannier−Mott-like, as indicated by average electron−hole separations greater than the lattice constant. Notably, the optical gaps of these materials span a wide range - from infrared (0.8 eV, Bi2Te3), through the visible spectrum (yellow: 2.17 eV, Te3; blue: 2.71 eV, As2S3), up to ultraviolet (4.07 eV, S3) - highlighting their versatility for broadband optoelectronic applications. Our results offer a detailed, many-body perspective on the optoelectronic behavior of these low-dimensional materials and underscore their potential for applications in nanoscale optoelectronic devices.

en: Quasiparticle effects and strong excitonic features in exfoliable 1D semiconducting materials

exciton

display_name: exciton

gw

display_name: gw

one-dimensional-materials

display_name: one-dimensional-materials

van-der-waals-wires

display_name: van-der-waals-wires


en:
In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (Morgan fingerprints, the DRFP, the CGR representation-based Chemprop, SLATMd, B²Rl², EquiReact and language model BERT + RXNFP) for the prediction of computed activation barriers on three diverse datasets.
This record includes data to support the article "Benchmarking machine-readable vectors of chemical reactions on computed activation barriers". This supports the github repository https://github.com/lcmd-epfl/benchmark-barrier-learning which contains the codes and duplicates the data.

benchmark

display_name: benchmark

chemical-reactions

display_name: chemical-reactions

epfl

display_name: epfl

erc

display_name: erc

machine-learning

display_name: machine-learning

snsf

display_name: snsf


en:
Noncollinear magnets are notoriously difficult to describe within first-principles approaches based on density-functional theory (DFT) because of the presence of low-lying spin excitations. At the level of ground-state calculations, several methods exist to constrain the magnetic moments to a predetermined configuration, and thereby accelerate convergence towards self-consistency. Their use in a perturbative context, however, remains very limited. Here we present a general methodological framework to achieve parametric control over the local spin moments at the linear-response level. Our strategy builds on the concept of Legendre transform to switch between various flavors of magnetic functionals, and to relate their second derivatives via simple linear-algebra operations. Thereby, we can address an arbitrary response function at the time-dependent DFT level of theory with optimal accuracy and minimal computational effort. In the low frequency limit, we identify the leading correction to the existing adiabatic formulation of the problem [S. Ren , Phys. Rev. X 14, 011041 (2024)], consisting in a renormalization of the phonon and magnon masses due to electron inertia. As a demonstration, we apply our methodology to the THz optical response of bulk CrI3 and Cr2O3, where we identify contributions from hybrid (electro)magnons with mixed spin-lattice character.

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

en: Dynamical response of noncollinear spin systems at constrained magnetic moments

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

magnetic-insulators

display_name: magnetic-insulators

magnon-phonon-coupling

display_name: magnon-phonon-coupling

nonadiabatic-regime

display_name: nonadiabatic-regime


en: Altermagnetism exhibits unique physical properties such as spin-momentum locking, anomalous Hall effect, nontrivial topological phase, and giant magnetoresistance. Among all the predicted candidates, several room temperature altermagnets are suggested to host significant potential applications. RuO₂ has been proposed as the most promising candidate. However, recently, there is intense debate about whether RuO₂ exhibits magnetic order or not. Experiments by several different technologies claim the collinear magnetic order and spin splitting induced effects. However, very recent μSR results reveal no magnetic order in RuO₂ which indicates that the time reversal symmetry is not broken. Direct observation of the high-resolution bulk band structure is absent up to date, but essential to investigate the electronic structure of RuO₂. In this study, utilizing soft X-ray ARPES, we report systematic direct experimental observation of bulk band structure of RuO₂. We observed signature of bulk band splitting induced by unknown symmetry broken. The ARPES data and calculated band structureare attached in this record.

en: Bulk band structure of RuO₂ measured with soft X-ray angle-resolved photoemission spectroscopy

materialscloud

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

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

en: Materials Cloud

band-structure

display_name: band-structure

magnetism

display_name: magnetism


en: Earth-abundant spinel oxides are promising alkaline oxygen-reduction catalysts, yet mechanistic models still invoke a vacuum-DFT associative *OOH/*OO route. Here we combine >80,000 fully solvated joint-DFT calculations to map oxygen-reduction energetics across 442 Al-, Co-, Cr-, Fe-, Ga-, Mn-, Ni- and Zn-containing spinels on the (100) and (111) facets. Associative intermediates are >0.5 eV less stable than *OH/H states, revealing a four-step dissociative cycle in which surface-hydrogen passivation shuttles protons. These results establish solvated high-throughput DFT as a predictive lens on the kinetic oxygen-reduction limit of spinel oxides.

en: Dataset of 80,000 solvated joint-DFT free energies for ORR on spinel-oxide (100) and (111) facets

envi

en: Environment

candle

display_name: candle

dftu

display_name: dftu

electrochemistry

display_name: electrochemistry

fuel-cells

display_name: fuel-cells

jdft

display_name: jdft

minimum-energy-pathway

display_name: minimum-energy-pathway

orr

display_name: orr

spinel-oxides

display_name: spinel-oxides


en:
We report the synthesis of Al-TFS, a novel aluminum metal-organic framework (MOF) based on tetrafluorosuccinic acid (H₂TFS), of formula Al(OH)(TFS)·1.5H₂O, introducing a new member to the family of perfluorinated MOFs. The structure of the MOF, solved ab-initio from laboratory powder X-ray diffraction data, displays structural analogies with that of the commercially available Al-fumarate (Basolite A520). The structure is composed of 1D infinite OH-bridged Al octahedra chains connected by the dicarboxylic linkers, designing rhombic channels decorated by fluorine atoms. Upon water removal, the MOF undergoes a phase transition leading to a moderate expansion of the unit cell. Volumetric analysis revealed the presence of ultra-micropores with a size lower than 4 Å. Gas sorption measurements demonstrated for Al-TFS a slightly higher CO₂ selectivity compared to N₂ and CH₄ than the Al-fumarate analogue, with peculiar shapes of the isotherms suggesting a dynamic response of the framework to CO₂ adsorption. Using different complementary techniques (in situ infrared spectroscopy, powder X-ray diffraction, solid-state nuclear magnetic resonance spectroscopy, gas/vapor sorption), and density functional theory simulations, the flexibility of the new MOF was disclosed, highlighting the significant impact of fluorination in tailoring materials with structural flexibility and peculiar adsorption properties.
This record contains the structural models for the Al-TFS (TFS = tetrafluorosuccinate) in its hydrated and dehydrated phases. IR spectra have been simulated for both structures and allowed tracking the associated phase transition via comparison with experimental results.

en: A flexible, perfluorinated analogue of aluminum fumarate metal-organic framework

dft

display_name: dft

mof

display_name: mof

phase-transition

display_name: phase-transition

vibrational-properties

display_name: vibrational-properties


en: This file contains all the data, as well as the code necessary to reproduce the results of Jae-Mo Lihm and Samuel Ponce, "Non-perturbative self-consistent electron-phonon spectral functions and transport." Electron-phonon coupling often dominates the electron spectral functions and transport properties. However, studies of this effect in real materials have largely relied on perturbative one-shot methods due to the lack of a first-principles theoretical and computational framework. Here, we present a self-consistent theory and implementation for the non-perturbative calculations of spectral functions and conductivity due to electron-phonon coupling. Applying this method to monolayer InSe, we demonstrate that self-consistency qualitatively affects the spectral function and transport properties compared to state-of-the-art one-shot calculations and allow one to reconcile experimental angle-resolved photoemission experiments. The developed method can be widely applied to materials with dominant electron-phonon coupling at moderate computational cost.

electron-phonon-coupling

display_name: electron-phonon-coupling

electronic-structure

display_name: electronic-structure

transport

display_name: transport

wannier-functions

display_name: wannier-functions


en:
Semiconducting alloys, in particular SiGe, have been employed for several decades as high- temperature thermoelectric materials. Devising strategies to reduce their thermal conductivity may provide a substantial improvement in their thermoelectric performance also at lower temper- atures. We have carried out an ab initio investigation of the thermal conductivity of SiGe alloys with random and spatially correlated compositional disorder employing the Quasi-Harmonic Green- Kubo (QHGK) theory with force constants computed from density functional theory. Leveraging QHGK and the hydrodynamic extrapolation to achieve size convergence, we obtained a detailed understanding of lattice heat conduction in SiGe and demonstrated that colored disorder suppresses thermal transport across the acoustic vibrational spectrum, leading to up to a 4-fold enhancement in the intrinsic thermoelectric figure of merit.

This record contains input and analysis scripts to reproduce the findings of this article.

en: Effects of colored disorder on the heat conductivity of SiGe alloys from first principles

disorder

display_name: disorder

lattice-thermal-conductivity

display_name: lattice-thermal-conductivity

max

display_name: max

sige-alloys

display_name: sige-alloys

thermoelectric-efficiency

display_name: thermoelectric-efficiency


en: This study employed density functional theory with doubly screened dielectric-dependent hybrid (DSH) functional to predict the band gaps of Pb- and Sn-based inorganic and hybrid 3D halide perovskites, as well as layered hybrid perovskites. The DSH functional employs material-dependent mixing parameters derived from macroscopic electronic dielectric constant, and accurately predicts band gaps for 3D perovskites only if structural local disorder is taken into account. For layered hybrid perovskites, we propose using the calculated dielectric constant of the respective 3D perovskites to define the DSH screening. This dataset contains input and output files of all DFT and DSH calculations applied to Pb- and Sn-based layered halide perovskites with various organic spacers and multilayered structures, such as BA series with n =1, 2, 3. The computational framework introduced here provides an efficient parameter-free ab initio methodology suitable for predicting the electronic properties of 3D, layered halide perovskites and their heterostructures, towards modelling materials for advanced optoelectronic devices.

en: Three-dimensional to layered halide perovskites: a parameter-free hybrid functional method for predicting electronic band gaps

density-functional-theory

display_name: density-functional-theory

dielectric-dependent-hybrid-functional

display_name: dielectric-dependent-hybrid-functional

hybrid-perovskites

display_name: hybrid-perovskites

layered-halide-perovskite

display_name: layered-halide-perovskite


en: We present a simulation of dynamic electrochemical impedance spectroscopy using numerical methods based on the finite element solution of differential equations. While the study of electrochemical systems during operation is of great interest, one is always confronted with challenges due to non-linearities when exciting the system with both a cyclic voltammetry and a multi-sine. We therefore propose a two-component-model, which first solves for the cyclic voltammetry and then calculates the effect of the multi-sine by means of linearization around the cyclic voltammetry of all the variables. We provide two models: (i) the dynamic transfer function model of DEIS during a redox reaction and  (ii) a stationary version of model (i). Both models are based on an existing semi-analytical model described in reference #2.

en: Numerical simulation of dynamic electrochemical impedance spectroscopy using a linearization technique

electrochemical-system

display_name: electrochemical-system

finite-element-method

display_name: finite-element-method

linearization

display_name: linearization


en: The dataset contains records of Charpy V-notch impact tests of nuclear structural materials. The focus is on studying the influence of specimen dimensions and geometry on Charpy impact test properties. The dataset was created through an extensive literature review of scientific articles. The extracted data points from the literature review are organized into a tabular format database containing 4,775 test records with 55 parameters, including material type and composition, manufacturing information, irradiation conditions, specimen size and dimensions, and Charpy properties. Materials science experts conducted systematic validation checks to ensure the accuracy of the information related to material type, manufacturing processes, treatment methods, chemical composition, testing conditions, as well as other pertinent information.

en: Dataset of Charpy V-notch impact test records of nuclear structural materials

charpy-v-notch-impact-test

display_name: charpy-v-notch-impact-test

miniature-specimens

display_name: miniature-specimens

nuclear-structural-materials

display_name: nuclear-structural-materials

reactor-pressure-vessel-steels

display_name: reactor-pressure-vessel-steels

specimen-size-effect

display_name: specimen-size-effect

sub-sized-specimens

display_name: sub-sized-specimens


en: The thermoresponsiveness of polymer-based soft materials opens perspectives in many applicative fields. This property in hydrated systems has the simple origin of a reversible de-mixing across a lower critical solution temperature, and has been explored for several chemically different amphiphilic polymers, each of them with specific critical conditions and phase behaviours. This work investigates for the first time by extensive atomistic molecular dynamics simulations the temperature dependence of the infinitely diluted aqueous solution of two such thermoresponsive macromolecules, poly(N-isopropyl-methacrylamide), PNIPMAM, and poly(2-isopropyl-2-oxazoline), PIPOX, in comparison to poly(N-isopropylacrylamide), PNIPAM, the much-more studied prototype system for polymers exhibiting a lower consolute boundary in water. The evolution of conformation and hydration water of the macromolecules is detected in a temperature range from well-below to well-above the transition temperature, with a favourable comparison to available experimental data. The resulting molecular description provides an explanation for deviations in solution properties of these macromolecules, including the effect of chaotropic anions and of solvent isotopic composition on the value of the lower critical solution temperature and the transition enthalpy. Overall, simulation findings show how the trade-off between gain in translational entropy of water molecules, associated with the reduction in solvent-excluded volume during the transition, and polymer-specific conformational features, determines the temperature response of these macromolecules in aqueous solution. In light of this molecular characterisation, a correlation between the single chain behaviour across the transition and the type I or type II phase diagram of the polymer aqueous solution can be postulated.

en: Understanding the lower critical solution temperature of amphiphilic synthetic polymers in water: the role of conformation and hydration

coil-to-globule-transition

display_name: coil-to-globule-transition

hydration-water

display_name: hydration-water

lcst

display_name: lcst

thermoresponsive-polymers

display_name: thermoresponsive-polymers


en: In the "design-simulation-preparation-characterization" research framework for carbon nanomaterial-reinforced polyvinyl alcohol (PVA) composite fibers, molecular dynamics (MD) simulations play a core role. Using Materials Studio 2023 and LAMMPS, oriented composite models of carbon nanotubes (CNT), graphene (GN) with PVA, along with pure PVA control models, were constructed. Simulations included geometric optimization, NPT dynamic processes, and annealing treatments, followed by calculations of mechanical properties (Young's modulus, shear modulus, density) and Z-direction stress-strain curves. Confined shear simulations mimicked shear-stretching in wet spinning, with relative concentration (RC) analysis to evaluate defect formation, while cohesive energy density (CED) analysis assessed composite compatibility. These simulations guided experimental design (e.g., selecting functionalized GO and CNT via CED results), validated experimental observations through stress-strain curve comparisons, revealed reinforcement mechanisms (carbon nanomaterials regulate stress transfer, reduce defects, and promote PVA crystallization), and established a "simulation-guided, experiment-validated" research framework, providing theoretical tools and methodological references for interface engineering in polymer nanocomposites.

en: Based on interfacial strategy and topological design: simulation optimization of carbon nanomaterial reinforced PVA fibers

materialscloud

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

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

en: Materials Cloud

carbon-nanomaterials

display_name: carbon-nanomaterials

composites

display_name: composites

forcites

display_name: forcites

mechanical-properties

display_name: mechanical-properties

polymer-composites

display_name: polymer-composites

pva

display_name: pva


en: This study discloses the effect of hydrogen impurities on the local chemical bonding states and structure of amorphous alumina films by predicting measured Auger parameter shifts using a combination of atomistic and electrostatic modeling. Different amorphous alumina polymorphs with variable H-content and density, as grown by atomic layer deposition, were successfully modeled using a universal machine learning interatomic potential. The annealing of highly defective crystalline hydroxide structures with experimental H-contents at the corresponding atomic layer deposition temperatures led to excellent agreement between theory and experiment in the density and structure of the resulting amorphous alumina polymorphs. The measured Auger parameter shifts of Al cations in such polymorphs were accurately predicted with respect to the H content by assuming that all H atoms are present in the form of hydroxyl ligands in the randomly interconnected 4-fold, 5-fold, and 6-fold nearest-coordination spheres of Al. As revealed by a combination of atomistic and electrostatic modeling, the measured Auger shifts with an increase in the H content and an accompanying decrease in the oxide density depend on the complex correlations between local coordination, bond lengths, bond angles, and ligand type(s) around the core-ionized atoms. Moreover, cryogenic X-ray photoelectron spectroscopy is suggested to offer new insights into the local chemical and structural building blocks of crystalline and amorphous oxides by reducing thermal noise. These findings and fundamental knowledge contribute to advancing the design of e.g. hydrogen oxide barrier films, oxide membranes for H separation, H storage materials, and fuel cells for a hydrogen-based economy.

en: Effect of hydrogen on the local chemical bonding states and structure of amorphous alumina by atomistic and electrostatic modeling and first principles calculations of auger parameter shifts

envi

en: Environment

amorphous-oxide

display_name: amorphous-oxide

atomic-layer-deposition-ald

display_name: atomic-layer-deposition-ald

auger-parameter

display_name: auger-parameter

density-functional-theory-dft

display_name: density-functional-theory-dft

electrostatic-modeling

display_name: electrostatic-modeling

hydrogen

display_name: hydrogen

molecular-dynamics-md

display_name: molecular-dynamics-md

x-ray-photoelectron-spectroscopy-xps

display_name: x-ray-photoelectron-spectroscopy-xps


en:
A first-principles approach for calculating ion separation in solution through 2D membranes is proposed. Ionic energy profiles across the membrane are obtained first, where solvation effects are explicitly simulated by machine-learning molecular dynamics, electrostatic corrections are applied to remove finite-size capacitive effects, and a mean-field treatment of the electrochemical double layer charging is used. Entropic contributions are assessed analytically and through a thermodynamic integration scheme. Ionic separations are then inferred through a microkinetic model of the filtration process, accounting for steady-state charge separation effects across the membrane. The approach is applied to Li+, Na+, K+ sieving through a crown-ether functionalized graphene membrane, with a case study of the mechanisms for a highly selective and efficient extraction of lithium from aqueous solutions.
This record contains the MD trajectories used to generate the energy and free energy profiles of Fig. 4.

en: Ion sieving in 2D membranes from first principles

ion-sieving

display_name: ion-sieving

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en:
Reproducibility in running scientific simulations on high-performance computing (HPC) environments is a persistent challenge due to variations in software and hardware stacks. Differences in software versions or hardware-specific optimizations often lead to discrepancies in simulation outputs. While Linux containers are commonly used to standardize software environments, tools like Docker lack reproducibility in image creation, requiring archiving of binary image blobs for future use. This method turns containers into black boxes, preventing verification of how the contained software was built.

In the linked paper, we demonstrate how we use GNU Guix to create our software stack bit-by-bit reproducible from a source bootstrap. Our approach incorporates a portable OpenMPI implementation, optimized software builds, and deployment via Apptainer images across three HPC environments. We show that our reproducible software stack facilitates consistent multi-physics simulations and complex workflows on diverse HPC platforms, exemplified by the OpenGeoSys software project. To ensure provenance of our findings, we utilized the AiiDA workflow manager.

This dataset includes the complete AiiDA provenance database underlying the results presented in the paper. The AiiDA workflow itself is defined in and can be reproduced with this repository: https://gitlab.opengeosys.org/bilke/hpc-container-study.

Version 1 of this record did not use optimized packages (Guix `--tune` option was not passed properly). This has been fixed in version 2 of this record.

en: Reproducible HPC software deployments, simulations and workflows

aiida

display_name: aiida

gnu-guix

display_name: gnu-guix

hpc

display_name: hpc

opengeosys

display_name: opengeosys

reproducibility

display_name: reproducibility

workflows

display_name: workflows


en:
Rotational symmetry plays a central role in physics, providing an elegant framework to describe how the properties of 3D objects – from atoms to the macroscopic scale – transform under the action of rigid rotations. Equivariant models of 3D point clouds are able to approximate structure-property relations in a way that is fully consistent with the structure of the rotation group, by combining intermediate representations that are themselves spherical tensors. The symmetry constraints however make this approach computationally demanding and cumbersome to implement, which motivates increasingly popular unconstrained architectures that learn approximate symmetries as part of the training process.
In this work, we explore a third route to tackle this learning problem, where equivariant functions are expressed as the product of a scalar function of the point cloud coordinates and a small basis of tensors with the appropriate symmetry. We also propose approximations of the general expressions that, while lacking universal approximation properties, are fast, simple to implement, and accurate in practical settings.

en: Representing spherical tensors with scalar-based machine-learning models

epfl

display_name: epfl

equivariance

display_name: equivariance

erc

display_name: erc

metatrain

display_name: metatrain


en: Precise modeling and understanding of heat transport in the superionic phase are of great interest. Although simulations combining Green-Kubo (GK) molecular dynamics with machine-learned potentials (MLPs) stand as a promising approach, substantial challenges remain due to the crucial impact of atomic diffusion. Here, we first show that the thermal conductivity (κ) of superionic materials calculated via conventional GK integral of the energy flux varies notably with the MLP model. Subsequently, we highlight that reliable, model-independent κ values can be obtained by applying Onsager’s reciprocal relations to correctly capture the coupled heat and mass transport. Remarkably, an anomalously invariant κ is observed over a wide temperature range, distinct from the characteristic trends in traditional crystals and glasses. Finally, we illustrate that conventional κ decompositions into kinetic, potential, and cross terms suffer from ambiguities in the physical interpretation, despite their mathematical rigor.

en: Heat transport in superionic materials via machine-learned molecular dynamics


en: Chalcogenide perovskites have emerged as a promising class of materials for the next generation of optoelectronic applications, with BaZrS3 attracting significant attention due to its wide bandgap, earth-abundant composition, and thermal and chemical stability. However, previous studies have consistently reported weak and ambiguous photoluminescence (PL), regardless of synthesis method, raising questions about the intrinsic optoelectronic quality of this compound. In this work, strong, band-to-band-dominated PL is demonstrated at room temperature in high-quality BaZrS3 single crystals, with a PL quantum yield of ∼0.005%. Despite the narrow, single-component PL emission band, time-resolved PL measurements reveal a carrier lifetime of 1.0 ± 0.2 ns. To understand the origin of the strong PL and short carrier lifetime, multiwavelength excitation and polarization-dependent Raman measurements are performed, supported by first-principles lattice dynamics calculations. All 23 theoretically predicted Raman-active modes and their symmetries are identified, providing a comprehensive reference for future studies. These results indicate that phonon-assisted carrier decay and nontrivial electron-phonon interactions contribute to the short carrier lifetimes, as evidenced by Raman spectroscopy and DFT calculations. Further studies on compositional variations or partial cation/anion substitutions can mitigate electron-phonon coupling and enhance carrier lifetimes. By establishing a detailed reference for the intrinsic vibrational and optoelectronic properties of BaZrS3, this work paves the way for further advancements in chalcogenide perovskites for energy and optoelectronic technologies.

en: BaZrS3 lights up: the interplay of electrons, photons, and phonons in strongly luminescent single crystals

chalcogenide-perovskite

display_name: chalcogenide-perovskite

electron-phonon-coupling

display_name: electron-phonon-coupling

optoelectronic

display_name: optoelectronic

raman-spectroscopy

display_name: raman-spectroscopy


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

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

diamond-nanostructures

display_name: diamond-nanostructures

heat-transport

display_name: heat-transport

nanoscale-thermometry

display_name: nanoscale-thermometry

nitrogen-vacancy-centers

display_name: nitrogen-vacancy-centers

non-diffusive-thermal-conduction

display_name: non-diffusive-thermal-conduction

phonon-boltzmann-transport-equation

display_name: phonon-boltzmann-transport-equation

viscous-heat-equations

display_name: viscous-heat-equations


en: Skutterudites are promising materials for thermoelectric and spintronics applications. Here we explore spin fluctuations in the FeSb3 skutterudite and their effect on its electronic structure using Hubbard-corrected density-functional theory calculations. We identify multiple magnetic and charge-disproportionated configurations, with an antiferromagnetic metallic ground state. Paramagnetic fluctuations modeled through a special quasirandom spin structure open a 61 meV gap, consistent with experiments. This state features non-degenerate spin channels and band-avoided crossings, hinting at a potential altermagnetic transition with topological features. Mapping the electronic structure to a Heisenberg Hamiltonian fails to explain the low Néel temperature (10 K), highlighting the role of magnetic exchange frustration and the need for more in-depth experimental investigations.This dataset contains first-principles calculations supporting the study “Spin fluctuations steer the electronic behavior in the FeSb₃ skutterudite”. It includes linear-response Hubbard parameters, phonon dispersions, electronic band structures, special quasirandom structures modeling of paramagnetism, and magnetic exchange interactions for FeSb₃. The data document how spin fluctuations stabilize the structure, open a small electronic gap, and strongly affect magnetic energetics.

en: Spin fluctuations steer the electronic behavior in the FeSb3 skutterudite

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).

condensed-matter-physics

display_name: condensed-matter-physics

first-principles-simulations

display_name: first-principles-simulations

heisenberg-model

display_name: heisenberg-model

magnetism

display_name: magnetism

paramagnetism

display_name: paramagnetism

special-quasi-random-structures

display_name: special-quasi-random-structures

spin-fluctuations

display_name: spin-fluctuations


en: Defect engineering in two-dimensional semiconductors has been exploited to tune the optoelectronic properties and introduce new quantum states in the band gap. Chalcogen vacancies in transition metal dichalcogenides in particular have been found to strongly impact charge carrier concentration and mobility in 2D transistors as well as feature subgap emission and single-photon response. In this Letter, we investigate the layer-dependent charge-state lifetime of Se vacancies in WSe2. In one monolayer WSe2,we observe ultrafast charge transfer from the lowest unoccupied orbital of the top Se vacancy to the graphene substrate within 1 ± 0.2 ps measured via the current saturation in scanning tunneling approach curves. For Se vacancies decoupled by transition metal dichalcogenide (TMD) multilayers, we find a subexponential increase of the charge lifetime from 62 ±14 ps in bilayer to a few nanoseconds in four-layer WSe2, alongside a reduction of the defect state binding energy. Additionally, we attribute the continuous suppression and energy shift of the dI=dV in-gap defect state resonances at very close tip-sample distances to a current saturation effect. Our results provide a key measure of the layer-dependent charge transfer rate of chalcogen vacancies in TMDs.

en: Layer-dependent charge-state lifetime of single Se vacancies in WSe2

materialscloud

en: Materials Cloud

defect-engineering

display_name: defect-engineering

scanning-tunneling-microscopy-stm

display_name: scanning-tunneling-microscopy-stm

transition-metal-dichalcogenide-tmd

display_name: transition-metal-dichalcogenide-tmd


en: The origin of the metal-to-insulator transition (MIT) in RNiO3 perovskites with R = trivalent 4 f ion has challenged the condensed matter research community for almost three decades. A drawback for progress in this direction has been the lack of studies combining physical properties and accurate structural data covering the full nickelate phase diagram. Here we focus on a small region close to the itinerant limit (R = Pr, 1.5 K < T < 300 K), where we investigate the gap opening and the simultaneous emergence of charge order in PrNiO3. We combine electrical resistivity, magnetization, and heat capacity measurements with high-resolution neutron and synchrotron x-ray powder diffraction data that, in contrast to previous studies, we analyze in terms of symmetry-adapted distortion modes. Such analysis allow us to identify the contribution of the different modes to the global distortion in a broad temperature range. Moreover, it shows that the structural changes at the MIT, traditionally described in terms of the evolution of the interatomic distances and angles, appear as abrupt increases of all nonzero mode amplitudes at TMIT = TN ∼ 130 K accompanied by the appearance of new modes below this temperature. A further interesting observation is the existence of a nearly perfect linear correlation between the amplitude of the breathing mode associated to the charge order and the staggered magnetization below the MIT. Our data also uncover a previously unnoticed anomaly at T ∗ ∼ 60 K (∼0.4 × TMIT), clearly visible in the electrical resistivity, lattice parameters and some mode amplitudes. Since phase coexistence is only observed in a small temperature region around TMIT (∼±10 K), these observations suggest the existence of a hidden symmetry in the insulating phase. We discuss some possible origins, among them the theoretically predicted existence of polar distortions induced by the noncentrosymmetric magnetic order [Perez-Mato et al., J. Phys.: Condens. Matter 28, 286001 (2016); Giovanetti et al., Phys. Rev. Lett. 103, 156401 (2009)].

en: Distortion mode anomalies in bulk PrNiO3: Illustrating the potential of symmetry-adapted distortion mode analysis for the study of phase transitions

envi

en: Environment

distortion-mode-amplitudes

display_name: distortion-mode-amplitudes

metal-insulator-transition

display_name: metal-insulator-transition

neutron-diffraction

display_name: neutron-diffraction

physical-properties

display_name: physical-properties

synchrotron-x-ray-diffraction

display_name: synchrotron-x-ray-diffraction


en:
If one carries out a molecular simulation of N particles using periodic boundary conditions, linear momentum is conserved and hence the number of degrees of freedom is set to 3N-3. In most programs, this number of degrees of freedom is the default setting. However, if one carries out a molecular simulation in an external field, one needs to ensure that degrees of freedom are changed from this default setting to 3N, as in an external field the velocity of the center of mass can change. Using the correct degrees of freedom is important in calculating the temperature and in some algorithms to simulate at constant temperature.
For sufficiently large systems, the difference between 3N and 3N-3 is negligible in the way. However, there are systems in which the comparison with experimental data requires molecular dynamics simulations of a small number of particles. In this work, we illustrate the effect of an incorrect setting of degrees of freedom in molecular dynamic simulations studying the diffusion properties of guest molecules in nanoporous materials. We show that previously published results have reported a surprising diffusion dependence on the loading, which could be traced back to an incorrect setting of the degrees of freedom. As the correct settings are convoluted and counter-intuitive in some of the most commonly used molecular dynamics programs, we carried out a systematic study on the consequences of the various commonly used (incorrect) settings. Our conclusion is that for systems smaller than 50 particles the results are most likely unreliable as these are either performed at an incorrect temperature or the temperature is incorrectly used in some of the results.
Furthermore, a novel and efficient method to calculate diffusion coefficients of guest molecules into nanoporous materials at zero loading conditions is introduced.

en: On the effects of the degrees of freedom on calculating diffusion properties in nanoporous materials

diffusion

display_name: diffusion

marvel

display_name: marvel

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nanoporous-materials

display_name: nanoporous-materials

prace

display_name: prace

snsf

display_name: snsf

zeolites

display_name: zeolites


en: Cu(I)-containing oxide semiconductors containing early transition-metal cations have been of growing interest as small bandgap semiconductors. Though, their lack of thermodynamic stability has slowed the progress of their synthetic discovery. Two new Cu(I)-molybdovanadates and one new Cu(I)-tungstovanadate have been discovered in high temperature reactions and their structures characterized using single crystal X-ray diffraction. Their chemical compositions are given by CuVMoO6, Cu1.51(1)V3.57(1)Mo0.43(1)O11 and Cu1.59(1)V3.52(1)W0.48(1)O11. The latter two structures possess a Cu site with ~25% occupancy, whereas all structures show a statistical disorder of V/Mo or V/W cations over equivalent crystallographic sites. Using a supercell approach, density functional theory calculations have been performed to understand their thermodynamic stabilities and their relationships to their band structures and densities-of-states (DOS). Results of the calculations showed lower total energies are attained when the partially-filled Cu sites are oxygen-bridged to the V atoms rather than the Mo/W atoms. Results of their DOS and band structures were also found to provide deeper insights into their physical properties as ternary, Cu(I)-containing oxides.

en: Electronic structure calculations of Cu(I) molybdovanadate and tungstovanadate compounds

bulk-structures

display_name: bulk-structures

copper-oxides

display_name: copper-oxides

electronic-structure

display_name: electronic-structure


en: Electron-phonon coupling is a key interaction that governs diverse physical processes such as carrier transport, superconductivity, and optical absorption. Calculating such interactions from first-principles with methods beyond density-functional theory  remains a challenge. We introduce here a finite-difference framework for computing electron-phonon couplings for any electronic structure method that provides eigenvalues and eigenvectors, and showcase applications for hybrid and Koopmans functionals, and $GW$ many-body perturbation theory. Our approach introduces a novel projectability scheme based on eigenvalue differences and bypasses many of the limitations of the direct finite difference methods. It also leverages symmetries to reduce the number of independent atomic displacements, thereby keeping computational costs manageable. This approach enables seamless integration with established first-principles codes for generating displaced supercells, performing Wannier interpolations, and evaluating transport properties. Applications to silicon and gallium arsenide show that advanced electronic-structure functionals predict different electron-phonon couplings and modify band curvatures, resulting in much more accurate estimates of intrinsic carrier drift mobilities and effective masses. In general, our method provides a robust and accessible framework for exploring electron-phonon interactions in complex materials with state-of-the-art electronic structure methods.

en: Carrier mobilities and electron-phonon interactions beyond DFT

beyond-dft-methods

display_name: beyond-dft-methods

boltzmann-transport-equation

display_name: boltzmann-transport-equation

electron-phonon-coupling

display_name: electron-phonon-coupling

koopmans-functional

display_name: koopmans-functional


en: We investigate the absorption spectra of the F centers in MgO and CaO using material-specific hybrid functionals combined with time-dependent hybrid-functional theory calculations. Our study accounts for electron-hole interaction, zero-phonon renormalization, and finite-size effects, which are all found to contribute to the achieved accuracy. The spectra of the neutral states solely present absorption lines stemming from internal defect-to-defect transitions without involving conduction band states of the host. At variance, the spectra of the singly positively charged states show two lines, one analogous to those seen in the neutral states and an extra line corresponding to electron excitations from the valence band to the unoccupied defect level in the band gap. In MgO, the three transition energies pertaining to these two charged states are found to coincide, but are spread out in the case of CaO. The agreement with the energies of experimental lines is excellent, indicating that our scheme reaches an accuracy of 0.2 eV or better.

en: Absorption lines of F centers in MgO and CaO through time-dependent hybrid-functional theory calculations

defect

display_name: defect

dft

display_name: dft

oxide

display_name: oxide

time-dependent-dft

display_name: time-dependent-dft


en:
Phonon interactions from lattice anharmonicity govern thermal properties and heat transport in materials.These interactions are described by n-th order interatomic force constants (n-IFCs), which can be viewed as high-dimensional tensors correlating the motion of n atoms, or equivalently encoding n-phonon scattering processes in momentum space. Here, we introduce a tensor decomposition to efficiently compress n-IFCs for arbitrary order n. Using tensor learning, we find optimal low-rank approximations of n-IFCs by solving the resulting optimization problem. Our approach reveals the inherent low dimensionality of phonon-phonon interactions and allows compression of the 3 and 4-IFC tensors by factors of up to 10^3-10^4 while retaining high accuracy in calculations of phonon scattering rates and thermal conductivity. Calculations of thermal conductivity using the compressed n-IFCs achieve a speed-up by nearly three orders of magnitude with >98% accuracy relative to the reference uncompressed solution. These calculations include both 3- and 4-phonon scattering and are shown for a diverse range of materials (Si, HgTe, MgO, TiNiSn and monoclinic ZrO2).  In addition to accelerating state-of-the-art thermal transport calculations, the method shown here paves the way for modeling strongly anharmonic materials and higher-order phonon interactions.

This data contains the phonon scattering rates data with full and compressed phonon-phonon interactions. The data corresponds to Figure 3 in the paper and includes information on the phonon scattering rates for two materials: silicon and HgTe.

en: Phonon scattering rates data for silicon and HgTe

phonon-phonon-interactions

display_name: phonon-phonon-interactions

phonon-scattering-rate

display_name: phonon-scattering-rate

thermal-transport

display_name: thermal-transport


en: Charge carrier mobilities are critical parameters in halide perovskite solar cells, governing their average carrier velocity under an applied electric field and overall efficiency. Recent advances in first-principles calculations of electron-phonon interactions and carrier mobilities have enabled predictive computations for perovskite solar cells. However, the flexible octahedral frameworks and cationic displacements in these materials challenge the harmonic approximation, leading to significant difficulties in accurately calculating transport properties. To address these issues, we combine temperature-dependent effective potentials with the ab initio Boltzmann transport equations to compute carrier mobilities in a representative lead-free perovskite, CsSnBr₃. At room temperature, the electron/hole Hall mobilities in CsSnBr₃ are 106/256 cm²/Vs when neglecting anharmonic effects and 59/145 cm²/Vs when included. This overestimation of the harmonic approximation arises from the neglect of scattering coming from soft modes. We provide a workflow for performing first-principles carrier mobility calculations in anharmonic systems, advancing the predictive modeling of perovskite solar cells.

en: Impact of anharmonicity on the carrier mobility of the Pb-free CsSnBr₃ perovskite

materialscloud

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

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

ab-initio

display_name: ab-initio

anharmonicity

display_name: anharmonicity

first-principles

display_name: first-principles

mobility

display_name: mobility

perovskite

display_name: perovskite


en: Cu(In,Ga)Se2 (CIGSe) is a highly efficient absorber material in thin-film solar cells that demonstrates a unique ability to tolerate massive Cu deficiency, which is often manifested in the formation of ordered defect compounds (ODCs). The conventional explanation is that the off-stoichiometry is mediated by the charge-neutral complexes forming in high concentrations. In this work, we refute this simplistic picture by treating the off-stoichiometry as an integral part of the crystal structure. We discover a topotactic transformation series of stable ODC-like structures with different compositions that enable large Cu deficiency in the material while being practically invisible to X-ray and neutron diffraction. These structures reproduce the known band gap widening and allow reconcile several lasting conflicts between experimental findings and first-principles calculations. This dataset contains the PBEsol-optimized primitive cells of the near-stable ODC structures in the topotactic series.

en: Functional off-stoichiometry in Cu(In,Ga)Se2

materialscloud

en: Materials Cloud

copper-deficiency

display_name: copper-deficiency

ordered-vacancy-compounds

display_name: ordered-vacancy-compounds

topotactic-transformation

display_name: topotactic-transformation

vacancy-arrangements

display_name: vacancy-arrangements


en: The limited choice of oxygen evolution reaction catalysts for proton exchange membrane water electrolyzers hinders their large-scale commercialization. Cobalt-based catalysts are promising candidates and usually undergo surface reconstruction into CoOOH-like structures. Despite this, the directly synthesized CoOOH has not yet been investigated in acidic environments. Here, we show that the CoOOH is active across the whole pH range, while its redox features is pH dependent. Operando hard X-ray absorption spectroscopy characterizations show a pH-induced change Co oxidation onset, but no change in the coverage of redox-active Co species before the oxygen evolution reaction. The pH-dependent catalytic performance is connected to the interfacial Co oxidative transformations under OER conditions. Combining the kinetic isotope effect and the apparent activation energy with theoretical verification, we provide the mechanistic insight on the possible OER pathway for CoOOH. In addition, CoOOH demonstrate a stable cell potential at 100 mA cm-2 for 400 hours in a proton exchange membrane water electrolyzer. These results provide insight into both the fundamental electrochemical properties of CoOOH and its potential for practical device performance.

en: Directly synthesized cobalt oxyhydroxide as an oxygen evolution catalyst in proton exchange membrane water electrolyzers

envi

en: Environment

coooh

display_name: coooh

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en: Metal organic frameworks (MOFs) have a promising perspective as oxygen evolution reaction (OER) electrocatalysts due to their high surface areas and tunable structures. However, one of the main challenges for their further application is inferior stability during alkaline OER. Herein, operando x-ray absorption spectroscopy and operando x-ray diffraction of NiCo-MOF-74 materials unveil their electrochemical transformations differentiating between electrolyte-induced, beam-induced, and electrochemically induced changes of the electronic state and local structure around the transition metal centers in addition to their overall crystal structure. An inferior electrolyte- and beam stability of Co-MOF-74 is revealed in comparison to a more stable performance of Ni-MOF-74 and Ni0.25Co0.75-MOF-74. Based on the operando measurement results, good experimental practices for future MOF OER electrocatalyst studies are presented.

en: Operando X-ray absorption and diffraction investigations on Ni- and Co-MOF-74 oxygen evolution electrocatalysts

chemical-stability

display_name: chemical-stability

operando-x-ray-absorption-spectroscopy

display_name: operando-x-ray-absorption-spectroscopy

operando-x-ray-diffraction

display_name: operando-x-ray-diffraction

radiation-stability

display_name: radiation-stability

stability-of-mofs

display_name: stability-of-mofs


en: The substantial consumption of lithium (Li) ions and sluggish reaction kinetics at the anode detrimentally impact the deliverable energy and fast-charging capability of lithium-ion batteries (LIBs) with silicon-based anodes. The prevailing contact prelithiation method using an electrolyte medium can replenish the active Li, but it may cause materials/electrode instability and bring barrier for Li-ion transport due to the nonuniform reaction. Here we explore a contact prelithiation methodology employing cyclic carbonate mediums that can enable spatially and temporally uniform prelithiation reaction. These mediums enable a delicate equilibrium between a Li-ion diffusion and the intrinsic prelithiation reaction rate of the material throughout the electrode depth. To gain an in-depth insight into the mechanism for producing Li-ions under the contact between Li metal and cyclic carbonate solvents, we undertook molecular dynamics simulations. Our calculation results of the self-produced Li alkyl carbonates reveal that while the long-chain Li alkyl carbonates are capable of forming an extensive polymer network in FEC and participating in the formation of a stable SEI on the Li metal surface, short-chain Li alkyl carbonates can dissolve into the solvent to produce Li-ions.

en: A salt-free medium facilitating electrode prelithiation towards fast-charging, high-energy lithium-ion batteries

battery

display_name: battery

lithium

display_name: lithium

molecular-dynamics

display_name: molecular-dynamics


en: The behavior of charge carriers in polar materials is governed by electron-phonon interactions, which affect their mobilities via phonon scattering and may localize carriers into self-induced deformation fields, forming self-trapped polarons. We present a first-principles study of self-trapped polaron formation in paradigmatic polar semiconductors and insulators using the variational polaron equations framework and self-consistent gradient optimization. Our method incorporates long-range corrections to the electron-phonon interaction, essential for finite-size systems. We demonstrate how the variational approach enables the identification of multiple polaronic states and supports the analysis of polarons with arbitrarily large spatial extent via energy filtering. The potential energy surfaces of the resulting polarons exhibit multiple local minima, reflecting distinct, symmetry-broken polaronic configurations in systems with degenerate band edges. Our findings align with previous theoretical studies and establish a robust foundation for future ab initio studies of polarons, especially those employing variational methods.This archive contains the data necessary to reproduce the results of the "Variational first-principles approach to self-trapped polarons" article by Vasilii Vasilchenko, Matteo Giantomassi, Samuel Poncé, and Xavier Gonze.

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles-calculations

display_name: first-principles-calculations

polarons

display_name: polarons

variational-wave-functional-methods

display_name: variational-wave-functional-methods


en: The direct conversion of methane-to-methanol remains a critical challenge in methane valorization. In this study, we unveil the crucial role of PdAu/CeO2 catalysts in enabling selective methane transformation under mild conditions, using only water as the sole oxidant. Through a combination of experimental techniques, including XPS and catalytic testing, alongside density functional theory (DFT) calculations, we demonstrate that a Pd0.3Au0.7/CeO2 catalyst, which predominantly exposes isolated Pd atoms, achieves remarkable methanol selectivity (∼80%) at 500 K with a 1:1 methaneto-water ratio. While Pd/CeO2 efficiently activates methane, its tendency for overreaction leads to complete methanol decomposition, thereby limiting selectivity. Alloying Pd withAu on ceria mitigates this over-reactivity, preventing methanol degradation while maintaining sufficient catalytic activity. The PdAu/CeO2 composite exhibits a synergistic effect: Pd in contact with the ceria support facilitates methane activation and water dissociation, while Au fine-tunes reactivity to promote methanol formation. DFT calculations confirm that isolated Pd sites at the PdAu/CeO2 interface play a key role in balancing activity and selectivity. This work underscores the importance of alloy/oxide interfaces in controlling selective methane conversion with water and offers valuable insights for designing highly efficient catalysts for methanol synthesis.

en: Engineering PdAu/CeO2 alloy/oxide interfaces for selective methane-to-methanol conversion with water

alloy

display_name: alloy

bimetallic

display_name: bimetallic

ceria

display_name: ceria

computational-chemistry

display_name: computational-chemistry

dft

display_name: dft

gofee

display_name: gofee

gold

display_name: gold

heterogeneous-catalysis

display_name: heterogeneous-catalysis

methane-to-methanol

display_name: methane-to-methanol

nanoparticle

display_name: nanoparticle

palladium

display_name: palladium


en: Two-dimensional topological insulators are characterized by an insulating bulk and conductive edge states protected by the nontrivial topology of the bulk electronic structure. They remain robust against moderate disorder until Anderson localization occurs and destroys the topological phase. Interestingly, disorder can also induce a topological phase—known as a topological Anderson insulator—starting from an otherwise pristine trivial phase. While topological invariants are generally regarded as global quantities, we argue that space-resolved topological markers can act as local order parameters, revealing the role of fluctuations and correlations in the local topology under Anderson disorder and vacancies. With this perspective, we perform numerical simulations of disorder-driven topological phase transitions in the Haldane and Kane-Mele models, using supercells with both open and periodic boundary conditions. We find that short-scale fluctuations of topological markers vanish upon coarse-graining, except at the topological phase transition, where their correlation length peaks and large-scale fluctuations remain. Notably, such a topological correlation function is characterized by critical exponents that appear universal across disorder types, yet they can resolve different topological phase transitions.

en: Fluctuations and correlations of local topological order parameters in disordered two-dimensional topological insulators

anderson-disorder

display_name: anderson-disorder

critical-exponents

display_name: critical-exponents

fluctuations-and-correlations

display_name: fluctuations-and-correlations

haldane-model

display_name: haldane-model

kane-mele-model

display_name: kane-mele-model

local-topology

display_name: local-topology

topological-order-parameters

display_name: topological-order-parameters

vacancies

display_name: vacancies


en: Polymers are versatile materials with a wide range of applications. The improvement of polymer properties rises the importance on the way that the repeating units are connected (head-to-tail,head-to-head,tail-to-tail) to build the polymer structure since it directly influences the morphology, chain topology and consequently its properties. Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymer. One key enabling factor for the essential foundations for Polymer Informatics is the machine-readable polymer representation. Polymer have been represented in a string format with special characters used to tag the head and tail positions indicating where the linking bond happens between repeat units. Available tools to assign the head and tail position limits its applicability in a broad sense. In this work we show a new tool to assign the head and tail atoms for a given monomer. From a database of 206 polymer precursors curated from the literature, our algorithm correctly predicted the class of 201 data points, which represents 97.6% of accuracy and regarding the the head and tail assignment, correctly assigned the positions for 188 data points, which translates to 91.3% of accuracy.

en: HTA - An open-source software for assigning heads and tails to SMILES in polymerization reactions

head-and-tail-atom-position

display_name: head-and-tail-atom-position

polymer

display_name: polymer

repeat-unit

display_name: repeat-unit


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

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

en: GapExtractor v2.0

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).

band-gap-energy

display_name: band-gap-energy

diffuse-reflectance-spectroscopy

display_name: diffuse-reflectance-spectroscopy

polyphasic-samples

display_name: polyphasic-samples

tauc-method

display_name: tauc-method


en:
Water inherently contains trace amounts of various salts, yet the microscopic processes by which salts influence some of its physical properties remain elusive. Notably, the mechanisms that reduce the dielectric constant of water upon salt addition are still debated. The primary absorption peak for electromagnetic radiation-commonly used in microwave heating-shifts towards higher frequencies in saline solutions, suggesting faster water molecular dynamics. This observation, however, contrasts with the simultaneous increase in viscosity and experimental reports that ionic solutes would slow down water molecular motion. In this work, we use molecular dynamics (MD) simulations with deep-neural-network models trained on high-quality quantum mechanical data to mimic interatomic forces and molecular dipoles, to compute the dielectric spectra of perchlorate water saline solution, which may be relevant to the recent discovery of liquid water beneath the thick ice crust at Mars’s south pole. Our results reveal that both the reduction in the dielectric constant and the absorption peak shift can be attributed to ion-induced changes in the orientational ordering of water molecules. Additionally, we demonstrate that the self-part of the molecular dipole-dipole correlation function reveals clear signatures of the slowing dynamics within the first cationic solvation shell, consistent with the experimentally observed increase in viscosity.

This record contains the DeepMD neural network potential and the underlying training data as well as the neural network for prediciting Wannier centroids and the underlying training data.

en: How salt solvation slows water dynamics while blue-shifting its dielectric spectrum

materialscloud

en: Materials Cloud

deepmd

display_name: deepmd

max

display_name: max

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potential

display_name: neural-network-potential

salt

display_name: salt

water

display_name: water


en: Thermal transport in low-dimensional materials is of great fundamental and applied interest due to their unusual and widely tunable properties, as demonstrated by numerous studies of two- and one-dimensional (1D) crystals. In grim contrast, low-dimensional amorphous materials remain largely unexplored, despite their potentially unique characteristics and applications. Here, we theoretically explore thermal transport in quasi-1D single-walled amorphous carbon nanotubes (a-CNTs) by combining homogeneous nonequilibrium molecular dynamics and lattice dynamics, based on a custom-trained high-accuracy machine-learned potential. For both zigzag and armchair a-CNTs of different diameters, the quantum-corrected thermal conductivity (κ) consistently drops by about an order of magnitude as the degree of disorder increases from 0.01 to 0.08, which is quantified by the concentration of Stone-Wales defects. Spectral analysis reveals the predominant role of the low-frequency (<3 THz) vibrational modes, the mean free paths (MFPs) of which are long but finite, thus leading to converged κ as the tube length reaches several micrometers. Further, lattice dynamics calculations yield much larger MFPs for the longitudinal acoustic modes compared to the transverse acoustic modes, which is primarily attributed to the distinctly higher group velocity of the former. Moreover, the κ suppression appears to be dictated by the structural disorder while anharmonicity barely contributes. Our work expands the understanding of thermal transport in low-dimensional amorphous materials and may help promote their eventual application. This record contains data and scripts that support the findings of this article.

en: Thermal transport in amorphous carbon nanotubes

envi

en: Environment

amorphous-carbon-nanotube

display_name: amorphous-carbon-nanotube

lattice-dynamics

display_name: lattice-dynamics

machine-learned-potential

display_name: machine-learned-potential

molecular-dynamics

display_name: molecular-dynamics

thermal-transport

display_name: thermal-transport


en: Spinodal decomposition is a phenomenon involving spontaneous phase separation in a solid alloy. It is often considered as the prototype of second order phase transitions and is known for the formation of non-trivial patterns. In the presence of a lattice mismatch f between the components, even more complex, qualitatively different patterns emerge, depending on the specific f values. Despite being interesting in practical and theoretical settings, modeling this class of phenomena may be hindered by computational costs. Lately, applications of Machine Learning (ML) promise to mitigate these issues. The availability of suitable datasets is therefore of primary importance for the development of said models. We here provide a collection of phase field simulations of spinodal decomposition involving elastic effects under different mismatch conditions f. These may be used in training a ML model both for the forward problem of predicting the evolution given an initial condition and for the inverse problem of extracting the misfit parameter from a sequence. The dataset is conveniently already divided into training, validation and test sets. The data also support the ML framework, based on a convolutional recurrent scheme, discussed in the related publication in refrences and allows for the full reproduction of the reported findings. Once trained, the neural network is able to accurately reproduce ground-truth evolution even in critical regions of the parameter space (e.g., near the onset of metastability) and predict the misfit parameters to a high degree of accuracy (~0.01% absolute strain).

en: Strained alloy microstructure dataset for ML property prediction and time-evolution simulation

alloy-microstructures

display_name: alloy-microstructures

convolutional-neural-network

display_name: convolutional-neural-network

elastic-field

display_name: elastic-field

lattice-mismatch

display_name: lattice-mismatch

machine-learning

display_name: machine-learning

phase-field-simulations

display_name: phase-field-simulations

recurrent-neural-network

display_name: recurrent-neural-network

spinodal-decomposition

display_name: spinodal-decomposition

surrogate-modeling

display_name: surrogate-modeling


en: Gallium nitride (GaN) is a wide-bandgap semiconductor of significant interest for applications in solid-state lighting, power electronics, and radio-frequency amplifiers. An important limitation of this semiconductor is its low intrinsic hole mobility, which hinders the development of p-channel devices and the large-scale integration of GaN CMOS in next-generation electronics. Prior research has explored the use of strain to improve the hole mobility of GaN, but a systematic analysis of all possible strain conditions and their impact on the mobility is lacking. In this study, we introduce a piezomobility tensor notation to characterize the relationship between applied strain and hole mobility in GaN. To map the strain-dependence of the hole mobility, we solve the ab initio Boltzmann transport equation, accounting for electron-phonon scattering and GW quasiparticle energy corrections.

en: Design of high-mobility p-type GaN via the piezomobility tensor

electron-phonon-coupling

display_name: electron-phonon-coupling

hole-mobility

display_name: hole-mobility

nitrides

display_name: nitrides


en: Understanding the behavior of materials under irradiation is crucial for the design and safety of nuclear reactors, spacecraft, and other radiation environments. The threshold displacement energy (Ed) is a critical parameter for understanding radiation damage in materials, yet its determination often relies on costly experiments or simulations.This work presents a compilation of threshold displacement energies (Ed) and fundamental material parameters (e.g., density, atomic mass, melting temperature) designed to enable the application of the machine learning-based Sure Independence Screening and Sparsifying Operator (SISSO) method. The goal is to develop accurate, analytical models for predicting Ed based on intrinsic material properties. The models outperform traditional approaches for monoatomic materials, capturing key trends with high accuracy. While predictions for polyatomic materials highlight challenges due to dataset complexity, they reveal opportunities for improvement with expanded data. This study identifies cohesive energy and melting temperature as key factors influencing Ed, offering a robust framework for efficient, data-driven predictions of radiation damage in diverse materials.

en: Machine learning-driven analytical models for threshold displacement energy prediction in materials

materials

display_name: materials

radiation-damage

display_name: radiation-damage

threshold-displacement-energy

display_name: threshold-displacement-energy


en: Super-exchange most often leads to antiferromagnetism in transition-metal perovskite oxides, yet ferromagnetism or ferrimagnetism would be preferred for many applications, for example in data storage. While alloying, epitaxial strain and defects were shown to lead to ferromagnetism, engineering this magnetic order remains a challenge. We propose, based on density functional theory calculations, a novel route to defect-engineer ferrimagnetism, which is based on preferential displacements of oxygen vacancies due to finite temperature vibrations. This mechanism has an unusual temperature dependence, as it is absent at 0K, strengthens with increasing temperature before vanishing once oxygen vacancies disorder, giving it a unique experimentally detectable signature.

en: Ferrimagnetism induced by thermal vibrations in oxygen-deficient manganite heterostructures

ferrimagnetism

display_name: ferrimagnetism

oxygen-vacancy

display_name: oxygen-vacancy

perovskite-manganite

display_name: perovskite-manganite


en: Lithium thiophosphate (LPS) has demonstrated promising properties for use as a solid electrolyte for the next-generation of lithium ion batteries. However, the high reactivity of LPS with common contaminants such as atmospheric water hinders commercialization of the cells. We employ a machine learning interatomic potential to gain fundamental, atomistic understanding on the mechanical, chemical and electronic properties of an LPS surface. Our focus lies first on the identification of relevant surface complexions formed by surface reconstructions, which differ greatly in properties from the bulk and define the form of the reactive sites. Then, we reveal the complex reactivity of the LPS surface with water and identify the surface moieties that play a relevant role in the material’s degradation. Thus, we uncover the impact of dynamical changes of the surfaces and their electronic structure on the material’s reactivity.This dataset includes all files necessary to reproduce the data reported in Reconstructions and dynamics of 𝛽 -lithium thiophosphate surfaces.This includes the MLIP model and dataset, generated surface structures with certain Miller indices, Wulff constructions, as well the input and result files of LAMMPS and DFT calculations.

deutsche-forschungsgemeinschaft

display_name: deutsche-forschungsgemeinschaft

lps

display_name: lps

machine-learning-interatomic-potentials

display_name: machine-learning-interatomic-potentials

marvel

display_name: marvel

molecular-dynamics

display_name: molecular-dynamics

smoothsoap

display_name: smoothsoap

solid-electrolyte

display_name: solid-electrolyte

surface-complexion

display_name: surface-complexion


en: Fullerides forms a big familiy of molecular crystals exhibiting various useful electro- and magnetochemical properties. Therefore, an efficient in silico method is desirable to look into such chemical system. To alleviate the burden of computationally-heavy first-principles calculations, here we present the successful attempts of using Machine Learning Force Field (MLFF) in predicting dynamical properties of the alkali-doped fulleride K₃C₆₀. Two on-the-fly Gaussian-Process-MLFF schemes based on different atomistic descriptors, Smooth Overlap of Atomic Position (SOAP) and Atomic Cluster Expansion (ACE), have been experimented. The performance of generated K₃C₆₀ MLFFs are validated by accurate prediction on energy and forces of 1,000 randomly disturbed K₃C₆₀ structures compared to respective DFT results. Several other dynamical properties including phonon dispersion, elastic moduli and heat capacity obtained by MLFFs have also shown good agreement with results from DFT calculations, whose computational cost on such post-processing is several times more expensive than training a single MLFF that performs effortless post-processing. This shows the potential of applying MLFF to complex molecular crystal systems and pave the way to the investigation of much more intricate fulleride properties.

en: Efficient modeling of dynamic properties in K₃C₆₀ using machine learning force fields

dynamical-properties

display_name: dynamical-properties

machine-learning-force-field

display_name: machine-learning-force-field

molecular-crystal

display_name: molecular-crystal


en:
This database contains the reference data used for direct force training of Artificial Neural Network (ANN) interatomic potentials using the atomic energy network (ænet) and ænet-PyTorch packages (https://github.com/atomisticnet/aenet-PyTorch). It also includes the GPR-augmented data used for indirect force training via Gaussian Process Regression (GPR) surrogate models using the ænet-GPR package (https://github.com/atomisticnet/aenet-gpr).
Each data file contains atomic structures, energies, and atomic forces in XCrySDen Structure Format (XSF). The dataset includes all reference training/test data and corresponding GPR-augmented data used in the four benchmark examples presented in the reference paper, "Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation".
A hierarchy of the dataset is described in the README.txt file, and an overview of the dataset is also summarized in supplementary Table S1 of the reference paper.

en: Database of scalable training of neural network potentials for complex interfaces through data augmentation

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).

aenet

display_name: aenet

ann

display_name: ann

first-principles

display_name: first-principles

gpr

display_name: gpr

li-metal-battery

display_name: li-metal-battery

machine-learning

display_name: machine-learning

potential-energy-surface

display_name: potential-energy-surface


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

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

materialscloud

en: Materials Cloud

compensating-defect

display_name: compensating-defect

jellium-counter-charge

display_name: jellium-counter-charge

nv-center

display_name: nv-center

siv-center

display_name: siv-center


en:
Experiments involving resonant optical excitation of infrared-active phonons in crystals have emerged as a powerful new way to tune materials properties. A puzzling and so far unexplained aspect of some so-called nonlinear phononics experiments is that the observed lifetimes of the optically created metastable phases are sometimes orders of magnitude longer than expected based on the nonlinear phononics mechanism assumed in most works. We use a combination of phenomenological theory and first-principles calculations to demonstrate that strong coupling between different lattice degrees of freedom (strains and Raman-active phonons) can give rise to a long-lived metastable phase recently observed in experiments on perovskite LaAlO3 [Hortensius et al. npj Quantum Mater. 5 95 (2020)]. We show that the long-timescale oscillatory response in the experimental optical reflectivity data is not due solely to shear strains, as originally suggested, but arises from a “hybrid” mode involving displacements of Raman-active phonons of the same symmetry. Our work suggests that strong coupling between different order parameters can provide a mechanism for long-lived optically created metastable phases and points towards strategies, such as strain engineering, for modifying or increasing the lifetime of light-induced phases in ultrafast optical experiments.

This archive contains the data files used to produce Figures 2-4 of the described paper.

en: Understanding long-lived metastable phases in ultrafast optical experiments

envi

en: Environment

complex-oxides

display_name: complex-oxides


en: Experiments reveal negative (non-Laplacian) surface stresses in metal oxide nanoparticles, partly associated with humidity during fabrication and annealing. Using a neural network interatomic potential for MgO, we prove that water adsorption induces surface hydroxylation, shifting facets from {100} to {110} to {111} and switching the average surface stress from positive to negative. Predicted lattice strains versus nanoparticle size agree well with experiments, clarifying experimental correlations. The new framework informs broad applications in catalysis, sensors, batteries, and biomedicine.

en: Hydroxylation-driven surface reconstruction at the origin of compressive-to-tensile stress transition in metal oxide nanoparticles

nanoparticles

display_name: nanoparticles

surface-reconstruction

display_name: surface-reconstruction

surface-stress

display_name: surface-stress

xrd

display_name: xrd


en: Ultrathin nanowires could play a central role in next-generation downscaled electronics. Here, we explore some of the most promising candidates identified from previous high-throughput screening: CuC2, TaSe3, and AuSe2, to gain insight into the thermodynamic and anharmonic behaviors of nanowires that could be exfoliated from weakly-bonded three-dimensional materials. We analyze thermal stability, linear thermal expansion, and anharmonic heat capacity using the stochastic self-consistent harmonic approximation. Notably, our work unveils exotic features common among all the 1D wires: a colossal record negative thermal expansion and very large deviations from the Dulong-Petit law due to strong anharmonicity.

en: Extreme anharmonicity and thermal contraction of 1D wires

anharmonicity

display_name: anharmonicity

density-functional-theory

display_name: density-functional-theory

heat-capacity

display_name: heat-capacity

negative-thermal-expansion

display_name: negative-thermal-expansion

one-dimensional-materials

display_name: one-dimensional-materials

sscha

display_name: sscha


en: The confining walls made by 2D materials are often considered as solid boundary conditions in studies of fluid transport through nanochannels, while the atomically thin walls inherently exhibit thermal fluctuations at finite temperature. In this work, we investigate the solid-liquid interfacial friction properties of water confined within flexible nanochannels using machine-learning-potential molecular dynamics. Surprisingly, we find that the friction coefficient (λ) increases with lateral size L in the flexible nanochannels, following a linear relationship with 1/L, which is absent in rigid channels. For thicker nanochannel walls with large bending stiffness (D), this size dependence weakens. This 1/L scaling resembles the size-dependent thermal fluctuation amplitude of 2D sheets, suggesting that λ can be well controlled by applying the mechanical strain to the channel walls via controlling the phonon modes associated with surface fluctuations. Further analysis reveals that the λ can be decomposed into the contribution from lattice roughness and the thermal-fluctuation-induced ripple of 2D surface, where the second term scales with D−1/2. These findings offer insights into the manipulation of nanoscale flow through precise control of local curvature and fluid-solid coupling.

en: Machine-learning potential molecular dynamics reveals the critical role of flexibility in solid-liquid nanofluidic friction

flexibility

display_name: flexibility

liquidsolid-friction

display_name: liquidsolid-friction

machine-learning-potential

display_name: machine-learning-potential