en: An atomic-level understanding of radiation-induced damage in simple polymers like polyethylene is essential for determining how these chemical changes can alter the physical and mechanical properties of important technological materials such as plastics. We performed ensembles of quantum simulations of radiation damage in a polyethylene analog using the Density Functional Tight Binding method to help bind its radiolysis and subsequent degradation as a function of radiation dose. Chemical degradation products are categorized with a graph theory approach, and we compute occurrence rates of unsaturated carbon bond formation, crosslinking, cycle formation, chain scission reactions, and out-gassing products. Statistical correlations between product pairs show significant correlations between chain scission reactions, unsaturated carbon bond formation, and out-gassing products, though these correlations decrease with increasing atom recoil energy. Our results present relatively simple chemical descriptors as possible indications of network rearrangements in the middle range of excitation energies. The data set contains how DFTB+ simulations were setup and running isomorphic analysis and graphs to obtain chemical structures.

en: Quantum simulations of radiation damage in a molecular polyethylene analog

dftb

display_name: dftb

graph-theory

display_name: graph-theory

isomorphic-analysis

display_name: isomorphic-analysis

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


$oid: OBSOLETE

en: The effect of dimensionality reduction on the crystallization kinetics of phase change materials is of relevance for the operation of ultrascaled memory devices. Therefore, the crystallization of amorphous nanoparticles (NPs) of the prototypical phase change compounds, GeTe and Ge₂Sb₂Te₅, has been addressed by several experimental works in recent years. In this work, we performed molecular dynamics simulations of the crystallization process of amorphous GeTe NPs with diameter in the range 3-6 nm (512-4096 atoms) by exploiting a machine-learned interatomic potential. We saw a few crystal nucleation events in the larger NPs but no crystallization in the smallest NP, 3 nm in diameter, in simulations lasting up to 80 ns in the temperature range 500-750 K. The analysis of the crystallization kinetics suggests that the nucleation rate per volume decreases with the NP size to an extent that prevents us from seeing crystallization in the smallest NP on our simulation time scale. This result is consistent with the large raise in crystallization temperature observed experimentally for NPs with diameters shorter than 3.5 nm.

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

en: Atomistic simulations of the crystallization of amorphous GeTe nanoparticles

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

crystallization

display_name: crystallization

gete

display_name: gete

neural-network-potential

display_name: neural-network-potential

neuromophic-computing

display_name: neuromophic-computing

phase-change-materials

display_name: phase-change-materials


en:
A database of 600 atomistic models of reduced graphene oxide (rGO) and hydrogenated rGO (H-rGO) was constructed, comprising 120 rGO and 480 H-rGO structures. The dataset spans a broad range of oxygen concentrations, –O–:–OH (epoxy/ether to hydroxyl) ratios, and hydrogenation levels. The database is designed for applications in computational modeling, machine learning, and structure–property analyses. The models were generated following a three-step simulation protocol:
 – Step 1 | Generation of Pseudo-GO Models: Graphene sheets were functionalized with oxygen groups (–O– or –OH), randomly distributed according to three –O–:–OH ratios (25:75, 50:50, and 75:25). Functionalization was applied symmetrically on both sides of the sheet, avoiding adjacent carbon atoms.
 – Step 2 | Thermal Reduction: Each pseudo-GO structure was subjected to annealing at four different temperatures (1000, 1500, 2000, and 2500 K). After initial relaxation at 300 K, the models were heated, equilibrated, annealed back to room temperature, and re-optimized. This step produced 120 rGO models exhibiting a range of oxygen contents, functional group distributions, and defect patterns.
 – Step 3 | Hydrogenation of rGO Models: rGO models were exposed to atomic hydrogen using a combined Grand Canonical Monte Carlo and molecular dynamics (GCMC/MD) scheme at 300 K and four hydrogen pressures (1, 2, 4, and 8 bar). Hydrogenation was performed with 50 GCMC exchange steps every 1 ps of MD, followed by relaxation and removal of unstable species. This step generated 480 H-rGO models with varying hydrogen coverage and bonding configurations.
All simulations were performed using the LAMMPS software package with the ReaxFF reactive force field.

en: Reduced graphene oxide membrane models for hydrogen storage

functionalized-carbon-nanostructures

display_name: functionalized-carbon-nanostructures

graphene-oxide-models

display_name: graphene-oxide-models

hydrogenated-graphene-oxide

display_name: hydrogenated-graphene-oxide

reduced-graphene-oxide

display_name: reduced-graphene-oxide


en: Cement hydration is crucial for the strength development of cement-based materials; however, the mechanism that underlies this complex reaction remains poorly understood at the molecular level. An in-depth understanding of cement hydration is required for the development of environmentally friendly cement and consequently the reduction of carbon emissions in the cement industry. Here, we use molecular dynamics simulations with a reactive force field to investigate the initial hydration processes of tricalcium silicate (C₃S) and dicalcium silicate (C₂S) up to 40 ns. Our simulations provide theoretical support for the rapid initial hydration of C₃S compared to C₂S at the molecular level. The dissolution pathways of calcium ions in C₃S and C₂S are revealed, showing that, two dissolution processes are required for the complete dissolution of calcium ions in C₃S. Our findings promote the understanding of the calcium dissolution stage and serve as a valuable reference for the investigation of the initial cement hydration.

en: The initial stages of cement hydration at the molecular level

cement-hydration

display_name: cement-hydration

reaxff-reactive-force-field

display_name: reaxff-reactive-force-field


en:
We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in high-dimensional supervised regression tasks. We provide a tight asymptotic description of the biases and variances estimated by these methods in the context of generalized linear models, such as ridge and logistic regression, taking the limit where the number of samples n and dimension d of the covariates grow at a comparable fixed rate α = n/d. Our findings are three-fold: i) resampling methods are fraught with problems in high dimensions and exhibit the double-descent-like behavior typical of these situations; ii) only when α is large enough do they provide consistent and reliable error estimations (we give convergence rates); iii) in the over-parametrized regime α < 1 relevant to modern machine learning practice, their predictions are not consistent, even with optimal regularization.
This record provides the code to reproduce the numerical experiments of the related paper "Analysis of bootstrap and subsampling in high-dimensional regularized regression".

en: Analysis of bootstrap and subsampling in high-dimensional regularized regression (code)

neural-networks

display_name: neural-networks

numerical-simulation

display_name: numerical-simulation

uncertainty-quantification

display_name: uncertainty-quantification


en: Topological Weyl semimetals represent a novel class of non-trivial materials, where band crossings with linear dispersions take place at generic momenta across reciprocal space. These crossings give rise to low-energy properties akin to those of Weyl fermions, and are responsible for several exotic phenomena. Up to this day, only a handful of Weyl semimetals have been discovered, and the search for new ones remains a very active area. The main challenge on the computational side arises from the fact that many of the tools used to identify the topological class of a material do not provide a complete picture in the case of Weyl semimetals. In this work, we propose an alternative and inexpensive, criterion to screen for possible Weyl fermions, based on the analysis of the band structure along high-symmetry directions in the absence of spin-orbit coupling. We test the method by running a high-throughput screening on a set of 6000 inorganic bulk materials and identify 49 possible candidates for topological properties. A further analysis, carried out by identifying and characterizing the crossings in the Brillouin zone, shows us that 3 of these candidates are Weyl semimetals. Interestingly, while these 3 materials underwent other high-throughput screenings, none had revealed their topological behavior before.

en: High-throughput screening of Weyl semimetals

high-throughput

display_name: high-throughput

topological-materials

display_name: topological-materials

weyl-semimetal

display_name: weyl-semimetal


en: Metal-organic frameworks (MOFs) as electrocatalysts for the alkaline oxygen evolution reaction (OER) show promising catalytic activity by offering great variability and high surface areas, enabling performance optimization and mechanistic studies. However, their stability during reaction and the structure-performance relationship defining the origin of the high OER activity, are still vigorously debated. Herein, we leverage operando X-ray absorption spectroscopy and operando X-ray diffraction to unveil the structural and electronic transformations of Ni-MOF-74 during OER. We identify the irreversible destruction of the MOF-74 crystal into a highly OER active, amorphous NiOOH-metal organic compound. Based on these findings, we propose an amorphous Ni metal organic compound (Ni-MOC*) for achieving high current densities both in a three-electrode cell (14 A gNi-1 at 1.5 VRHE) and in an anion exchange membrane water electrolyzer (AEM-WE) with a stable AEM-WE performance exceeding 100 h at 500 mA cm-2.

en: From operando investigations to implementation of Ni-MOF-74 oxygen evolution electrocatalysts

alkalineaem-water-electrolysis

display_name: alkalineaem-water-electrolysis

electrocatalysis

display_name: electrocatalysis

marvel

display_name: marvel

oer

display_name: oer

operando-xasxrd

display_name: operando-xasxrd


en:
We reassess the modeling of amorphous silica bilayers as a two-dimensional classical system whose particles interact with an effective pairwise potential. We show that it is possible to reparameterize the potential developed by Roy, Heyde, and Heuer to quantitatively match the structural details of the experimental samples. We then study the glassy dynamics of the reparameterized model at low temperatures.  Using appropriate cage-relative correlation functions, which suppress the effect of Mermin-Wagner fluctuations, we highlight the presence of two well-defined Arrhenius regimes separated by a narrow crossover region, which we connect to the thermodynamic anomalies and the changes in the local structure. We find that the bond-orientational order grows steadily below the crossover temperature and is associated to transient crystalline domains of nanometric size.  These findings raise fundamental questions about the nature of glass structure in two dimensions and provide guidelines to interpret the experimental data.

This dataset is associated with "Glassy dynamics and crystalline local order in two-dimensional amorphous silica", Marco Dirindin and Daniele Coslovich (2024) [arXiv:2410.10500].
It includes scripts and data files to allow for the replication of the figures. The EPS figures in the Figures folder were generated using gnuplot version 5.4.

glass-transition

display_name: glass-transition

silica-bilayer

display_name: silica-bilayer


en: Covalent organic frameworks (COFs) are materials of growing interest for electronic applications due to their tunable structures, chemical stability, and layered architectures that support extended π-systems and directional charge transport. While their electronic properties are strongly influenced by the choice of molecular building blocks and the stacking arrangement, experimental control over these features remains limited, and the number of well-characterized COFs is still relatively small. Here, we explore two alternative strategies, hydrostatic pressure and metal intercalation, to tune the electronic structure of COFs. Using periodic density functional theory (DFT) calculations, we show that the band gap of pristine COF-1 decreases by ∼1 eV under compression up to 10 GPa. Metal intercalation induces an even greater reduction, in some cases leading to metallic behavior. We demonstrate that pressure and intercalation offer effective, continuous control over COF electronic properties, providing powerful means to complement and extend conventional design approaches.

en: Extensive band gap tunability in covalent organic frameworks via metal intercalation and high pressure

band-gap

display_name: band-gap

covalent-organic-frameworks

display_name: covalent-organic-frameworks

dft

display_name: dft

electronic-bands

display_name: electronic-bands

high-pressure

display_name: high-pressure

intercalation

display_name: intercalation


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 4,961 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.

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: 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: This dataset contains the results of density functional theory (DFT) calculations performed using Quantum ESPRESSO to study surface binding energies (SBE) and the formation energies of interstitial hydrogen (H-IFE) in various plasma-facing materials (PFMs). These calculations support the findings reported in the article Comprehensive Screening of Plasma-Facing Materials for Nuclear Fusion, where a combination of peer-reviewed data from the PAULING FILE database and first-principles calculations are used to evaluate potential PFM candidates. Key results include a detailed comparison of tungsten and alternative refractory materials, focusing on their behavior under intense neutron bombardment and plasma interactions in nuclear fusion reactors. The dataset includes input and output files from the Quantum ESPRESSO simulations, offering valuable insight into defect energetics in candidate materials.

materialscloud

en: Materials Cloud

en: DFT calculations of surface binding and interstitial hydrogen formation energies for plasma-facing materials

envi

en: Environment

eurofusion

display_name: eurofusion

interstitial-formation-energy

display_name: interstitial-formation-energy

marvel

display_name: marvel

nuclear-fusion

display_name: nuclear-fusion

plasma-facing-materials

display_name: plasma-facing-materials

surface-binding-energy

display_name: surface-binding-energy


en: We determine the complete atomic-level structure of the amorphous form of the drug atuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, via chemical-shift-driven NMR crystallography. The ensemble of preferred structures allows us to identify a number of specific conformations and interactions that stabilize the amorphous structure. These include preferred hydrogen-bonding motifs with water and with other drug molecules, as well as conformations of the cyclohexane and pyrazole rings that stabilize structure by indirectly allowing for optimization of hydrogen bonding.

en: Atomic-level structure of the amorphous drug Atuliflapon via NMR crystallography

atuliflapon

display_name: atuliflapon

nmr-crystallography

display_name: nmr-crystallography

solid-state-nmr

display_name: solid-state-nmr


en: Predicting the ground state spin of transition metal complexes is a challenging task. Previous attempts have been focused on specific regions of chemical space, whereas a more general automated approach is required to process crystallographic structures for high-throughput quantum chemistry computations. In this work, we developed a method to predict ground state spins of transition metal complexes. We started by constructing a dataset which contains 2,063 first row transition metal complexes taken from experimental crystal structures and their computed ground state spins. This dataset showed large chemical diversity in terms of metals, metal oxidation states, coordination geometries, and ligands. Then, we analyzed the trends between structural and electronic features of the complexes and their ground state spins, and put forward an empirical spin state assignment model. We also used simple descriptors to build a statistical model with >95% predictive accuracy across the board. With this, we provide a practical and automated way to determine the ground state spin of transition metal complex from structure, enabling the high-throughput exploration of crystallographic repositories.

en: Automated prediction of ground state spin for transition metal complexes

machine-learning

display_name: machine-learning

spin-state

display_name: spin-state

transition-metal

display_name: transition-metal

transition-metal-complex

display_name: transition-metal-complex


en: Identification of the hydrogen lattice location in crystals is key to understanding and controlling hydrogen-induced properties. Combining nuclear reaction analysis with the ion channeling technique, we experimentally determined the locations of H and D in epitaxial nanofilms of titanium hydrides. It was found that 11 at.% of H are located at the octahedral site with the remaining H atoms in the tetrahedral site. Density functional theory calculations revealed that the structures with the partial octahedral site occupation are stabilized by the Fermi level shift and Jahn-Teller effect induced by hydrogen. In contrast, D was found to solely occupy the tetrahedral site owing to the mass effect on the zero-point vibrational energy. These findings suggest that site occupation of hydrogen can be controlled by changing the isotope mixture ratio, which leads to promising manifestation of novel hydrogen-related phenomena.

en: Isotope-dependent site occupation of hydrogen in epitaxial titanium hydride nanofilms

dft

display_name: dft

hydrogen

display_name: hydrogen


en: Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix composites and nano-multilayers (NMLs) are finding applications as brazing fillers and shielding material for plasma and radiation.  Due to the large lattice mismatch between fcc Cu and bcc W, these systems have complex interfaces that are beyond the scales suitable for ab initio methods, thus motivating the development of chemically accurate interatomic potentials. Here, a neural network potential (NNP) for Cu-W is developed within the Behler-Parrinello framework using a curated training dataset that captures metallurgically-relevant local atomic environments. The Cu-W NNP accurately predicts (i) the metallurgical properties (elasticity, stacking faults, dislocations, thermodynamic behavior) in elemental Cu and W, (ii) energies and structures of Cu-W intermetallics and solid solutions, and (iii) a range of fcc Cu/bcc W  interfaces, and exhibits physically-reasonable behavior for solid W/liquid Cu systems.  As will be demonstrated in forthcoming work, this near-ab initio-accurate NNP can be applied to understand complex phenomena involving interface-driven processes and properties in Cu-W composites.

en: Machine learning potential for the Cu-W system

copper-tungsten

display_name: copper-tungsten

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nanomultilayer

display_name: nanomultilayer

neural-network-potentials

display_name: neural-network-potentials


en: We introduce an automated, flexible framework (aiida-hubbard) to self-consistently calculate Hubbard U and V parameters from first-principles. By leveraging density-functional perturbation theory, the computation of the Hubbard parameters is efficiently parallelized using multiple concurrent and inexpensive primitive cell calculations. Furthermore, the intersite V parameters are defined on-the-fly during the iterative procedure to account for atomic relaxations and diverse coordination environments. We devise a novel, code-agnostic data structure to store Hubbard related information together with the atomistic structure, to enhance the reproducibility of Hubbard-corrected calculations. We demonstrate the scalability and reliability of the framework by computing in high-throughput fashion the self-consistent onsite U and intersite V parameters for 115 Li-containing bulk solids with up to 32 atoms in the unit cell. Our analysis of the Hubbard parameters calculated reveals a significant correlation of the onsite U values on the oxidation state and coordination environment of the atom on which the Hubbard manifold is centered, while intersite V values exhibit a general decay with increasing interatomic distance. We find, e.g., that the numerical values of U for the 3d orbitals of Fe and Mn can vary up to 3 eV and 6 eV, respectively; their distribution is characterized by typical shifts of about 0.5 eV and 1.0 eV upon change in oxidation state, or local coordination environment. For the intersite V a narrower spread is found, with values ranging between 0.2 eV and 1.6 eV when considering transition metal and oxygen interactions. This framework paves the way for the exploration of redox materials chemistry and high-throughput screening of d and f compounds across diverse research areas, including the discovery and design of novel energy storage materials, as well as other technologically-relevant applications.

en: First-principles Hubbard parameters with automated and reproducible workflows

aiida

display_name: aiida

dftu

display_name: dftu

dftuv

display_name: dftuv

hubbard

display_name: hubbard

oxidation-state

display_name: oxidation-state


en: Comprehending water dynamics is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While ab-initio molecular dynamics (AIMD) accurately portray water's structure, computing its dynamic properties over nanosecond timescales proves cost-prohibitive. This study employs machine learning potentials (MLPs) to accurately determine the dynamical properties of liquid water with ab-initio accuracy. Our findings reveal diversity in the calculated diffusion coefficient (D) and viscosity of water (η) across different methodologies. Specifically, while the GGA, meta-GGA, and hybrid functional methods struggle to predict dynamic properties under ambient conditions, whereas methods on the higher level of Jacob's ladder of DFT approximation perform significantly better. Intriguingly, we discovered that all D and η adhere to the established Stokes-Einstein (SE) relation for all the ab-initio water. The diversity observed among different methods can be attributed to distinct structural entropy, affirming the applicability of excess entropy scaling relations across all functionals. The correlation between D and η provides valuable insights for identifying the ideal temperature to accurately replicate liquid water's dynamic properties. Furthermore, our findings can validate the rationale behind employing artificially high temperatures in the simulation of water via AIMD. These outcomes not only pave the path toward designing better functionals for water but also underscore the significance of water's many-body characteristics.

en: The dynamic diversity and invariance of ab-initio water

aimd

display_name: aimd

density-functional-theory

display_name: density-functional-theory

diffusion

display_name: diffusion

dynamical-properties

display_name: dynamical-properties

machine-learning-potential

display_name: machine-learning-potential

water

display_name: water


en: On-surface synthesis relies on carefully designed molecular precursors that are thermally activated to afford desired, covalently coupled architectures. In a recent publication, we studied the reactions of vinyl groups on poly-para-phenylene and provided a comprehensive description of all the reaction steps taking place on the Au(111) surface under ultrahigh vacuum conditions. We find that vinyl groups successfully cyclize with the phenylene rings in the ortho positions, forming a dimethyl-dihydroindenofluorene as the repeating unit, which can be further dehydrogenated to a dimethylene-dihydroindenofluorene structure. Interestingly, the obtained polymer can be transformed cleanly into thermodynamically stable polybenzo[k]tetraphene at higher temperature, involving a previously elusive pentagon-to-hexagon transformation via ring opening and rearrangement on a metal surface. Our insights into the reaction cascade unveil fundamental chemical processes involving vinyl groups on surfaces. Because the formation of specific products is highly temperature-dependent, this innovative approach offers a valuable tool for fabricating complex, low-dimensional nanostructures with high precision and yield. This record contains the data that support the scientific results in the publication.

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: On-surface cyclization of vinyl groups on poly-para-phenylene involving an unusual pentagon to hexagon transformation

cscs

display_name: cscs

dft

display_name: dft

on-surface-synthesis

display_name: on-surface-synthesis

spm

display_name: spm

vinyl-cyclization

display_name: vinyl-cyclization


en:
The dynamics of (few) electrons dissolved in an ionic fluid—as when a small amount of metal is added to a solution while upholding its electronic insulation—manifests interesting properties that can be ascribed to nontrivial topological features of particle transport (e.g., Thouless' pumps). In the adiabatic regime, the charge distribution and the dynamics of these dissolved electrons are uniquely determined by the nuclear configuration. Yet, their localization into effective potential wells and their diffusivity are dictated by how the self-interaction is modeled. In this article, we investigate the role of self-interaction in the description of localization and transport properties of dissolved electrons in non-stoichiometric molten salts. Although the account for the exact (Fock) exchange strongly localizes the dissolved electrons, decreasing their tunneling probability and diffusivity, we show that the dynamics of the ions and of the dissolved electrons are largely uncorrelated, irrespective of the degree to which the electron self-interaction is treated, and in accordance with topological arguments.
This record contains input and analysis scripts to reproduce the findings of this article.

materialscloud

en: Materials Cloud

en: Self-interaction and transport of solvated electrons in molten salts

ionic-transport

display_name: ionic-transport

marie-curie-fellowship

display_name: marie-curie-fellowship

max

display_name: max

molten-salts

display_name: molten-salts

topological-materials

display_name: topological-materials

transport

display_name: transport


en: Emerging two-dimensional (2D) materials bring unprecedented opportunities for electronic applications. The design of high-performance devices requires an accurate prediction of carrier mobility in 2D materials, which can be obtained using state-of-the-art ab initio calculations. However, various factors impact the computational accuracy, leading to contradictory estimations for the mobility. In this work, targeting accurate and efficient ab initio calculations, transport properties in III-V monolayers are reported using the Boltzmann transport equation, and the influences of pseudopotential, quadrupole correction, Berry connection, and spin-orbit coupling (SOC) on mobilities are systematically investigated. Our findings are as follows: (1) The inclusion of semi-core states in pseudopotentials is important to obtain accurate calculations. (2) The variations induced by dynamical quadrupole and Berry connection when treating long range fields can be respectively 40% and 10%. (3) The impact of SOC can reach up to 100% for materials with multi-peak bands. Importantly, although SOC notably modifies the electronic wavefunctions, it negligibly impacts the dynamical matrices and scattering potential variations. As a result, the combination of fully-relativistic electron calculation and scalar-relativistic phonon calculation can strike a good balance between accuracy and cost. This work compares computational methodologies, providing guidelines for accurate and efficient calculations of mobilities in 2D semiconductors.

en: Guidelines for accurate and efficient calculations of mobilities in two-dimensional semiconductors

envi

en: Environment

electron-phonon-coupling

display_name: electron-phonon-coupling


en: An excellent single-atomic photocatalyst, Ti@C₄N₃, is theoretically found to effectively convert CO₂ to C₂H₆ by density functional theory (DFT) calculations and non-adiabatic molecular dynamics (NAMD) simulations. The Ti@C₄N₃ photocatalyst has remarkable stability both thermally, chemically, and mechanically. Electronically, it has strong absorption properties, suitable band positions, and a long photogenerated electron lifetime, allowing photogenerated electrons to migrate to the surface. Notably, the high-valence active site effectively activates two CO₂ through dual activation: Under light irradiation, the weakly adsorbed CO₂ undergoes photo-induced activation by the photoelectron of conduction band minimum (CBM); without light, the high Lewis acidity of the Ti site induces CO₂ activation through back-donating π-bond. Contrast simulation results uncovered that dual activation of CO₂ is attributed to the thermal and photonic synergy. Furthermore, two activated CO₂ species under light easily couple to form oxalate with the barrier of 0.19 eV, and further reduced to C₂H₆ with a low activation energy of 1.09 eV.

en: Dual activation and C-C coupling on single atom catalyst for CO₂ photoreduction

c-c-coupling

display_name: c-c-coupling

dual-activation

display_name: dual-activation

single-atom-catalyst

display_name: single-atom-catalyst


en:
Passivation oxide formation is the key for corrosion control of metal alloys.  The kinetics of competing oxide formation and dissolution determines alloy corrosion behaviors in aqueous solution. Despite the important role of the multi-component oxide evolution, little has been known on the kinetics from the atomistic level.  We have built a computational framework that enables simulations of competing kinetic processes in multi-component oxides from first principles. The effects of applied voltage, pH and temperature on oxide growth, dissolution and reprecipitation can all be captured in this model. Combining with our experimental measurements on Alloy 22 and a Ni80%-Cr20% model alloy, we identified three voltage regimes with distinct oxide thicknesses and compositions.
The oxide energetics of various stoichiometries are calculated by the density functional theory (DFT). Then the obtained data are used to train a surrogate lattice Hamiltonian with the cluster expansion (CE) method. Finally, kinetic Monte Carlo (KMC) simulations are run with the cation hopping barriers calculated on-the-fly based on the local environments from the combination of the above Hamiltonian and the linear Brønsted−Evans−Polanyi (BEP) relation.

en: Atomic-scale understanding of oxide growth and dissolution kinetics of Ni-Cr alloys

cluster-expansion

display_name: cluster-expansion

kinetics

display_name: kinetics

ni-cr-oxide

display_name: ni-cr-oxide

vasp

display_name: vasp


en: Hopf et al. first reported the high-temperature 6π-electrocyclization of cis-hexa-1,3-diene-5-yne to benzene in 1969. Subsequent studies using this cyclization have been limited by its very high reaction barrier. Here, we show that the reaction barrier for two model systems, (E)-1,3,4,6-tetraphenyl-3-hexen-1,5-diyne (1a) and (E)-3,4-bis(4-iodophenyl)-1,6-diphenyl-3-hexen-1,5-diyne 1b, is decreased by nearly half on a Au(111) surface. In recent work, we have used scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM) to monitor the Hopf cyclization of enediynes 1a,b on Au(111). Enediyne 1a undergoes two sequential, quantitative Hopf cyclizations, first to naphthalene derivative 2, and finally to chrysene 3. Density functional theory (DFT) calculations reveal that a gold atom from the Au(111) surface is involved in all steps of this reaction, and that it is crucial to lowering the reaction barrier. Our findings have important implications for the synthesis of novel graphene nanoribbons. Ullman coupling of enediyne 1b at 20 ˚C on Au(111), followed by a series of Hopf cyclizations and aromatization reactions at higher temperatures, produces nanoribbons 12, and eventually 13 upon further heating. These results show for the first time that graphene nanoribbons can be synthesized on-surface using the Hopf cyclization mechanism. This record contains all simulation data that support our scientific work.

en: Dramatic acceleration of the Hopf cyclization on gold(111): from enediynes to unusual graphene nanoribbons

cscs

display_name: cscs

dft

display_name: dft

nc-afm

display_name: nc-afm

on-surface-synthesis

display_name: on-surface-synthesis

snsf

display_name: snsf

stm

display_name: stm


en: Achieving fine control over the dispersion of supported platinum nanoparticles (Pt) is a promising avenue to enhance their catalytic activity and selectivity. Experimental observations suggest that exposing ceria-supported Pt nanoparticles to O₂ at 500 °C promote their dispersion into smaller particles and eventually single atoms. In the associated paper we have combined several approaches and types of models in a consistent atomistic framework to evaluate the relative stability of ceria-supported Pt as a function of the degree of oxidation of Pt and of the particle size, ranging from single atoms to nanoparticles of 1.5 nm of diameter.

en: The role of oxidizing conditions in the dispersion of supported platinum nanoparticles explored by ab initio modeling

cerium-oxide

display_name: cerium-oxide

dftu

display_name: dftu

platinum

display_name: platinum


en: The use of perovskite oxides as bifunctional catalysts for oxygen electrocatalysis is a promising strategy for developing high-performance unified regenerative fuel cells (URFCs), thanks to their highly tunable structure. This work explores the exsolution of well-dispersed nanoparticles containing reduced Pt species from La0.6Sr0.4Fe0.95Pt0.05O3-δ (LSFPt) as an effective approach to design bifunctional catalysts for oxygen electrocatalysis. A 10 h reduction treatment at 500 °C in 5% H2/Ar promotes the exsolution of ~1.2 nm Pt-containing nanoparticles on the surface, increasing oxygen deficiency without compromising the perovskite structure. The resulting catalyst exhibits significantly improved activity for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) in alkaline media, with over 2.5-fold performance improvement compared to the as-prepared LSFPt. Electrochemical impedance spectroscopy (EIS), combined with distribution of relaxation times (DRT) deconvolution analysis, revealed a notable decrease in medium-frequency resistance, indicating enhanced charge transfer at the electrode surface for OER and ORR. Post-mortem O K-edge spectra obtained by soft X-ray absorption spectroscopy in total electron yield mode further suggest an increased surface restructuring in the reduced LSFPt, attributed to the synergistic presence of surface defects and exsolved nanoparticles containing reduced Pt species. This one-step reduction protocol offers a promising strategy for designing efficient bifunctional electrocatalysts.

en: Harnessing bifunctional oxygen electrocatalysis: Exsolved Pt nanoparticles from La0.6Sr0.4Fe0.95Pt0.05O3-δ

bifunctional-electrocatalyst

display_name: bifunctional-electrocatalyst

distribution-of-relaxation-times-drt

display_name: distribution-of-relaxation-times-drt

oxygen-evolution-reaction-oer

display_name: oxygen-evolution-reaction-oer

oxygen-reduction-reaction-orr

display_name: oxygen-reduction-reaction-orr

perovskite

display_name: perovskite

pt-exsolution

display_name: pt-exsolution

unified-regenerative-fuel-cells

display_name: unified-regenerative-fuel-cells


en: Hydrogen boride (HB) sheet is a newly developed two-dimensional boron-based nanosheet. To explore its potential in electronic and catalytic applications, it's crucial to assess its chemical stability with common substances. In this study, we examine its stability in water, a substance frequently encountered in ambient conditions and various applications. Focusing on the HB sheet's edge, we utilized a nanoribbon model to observe water-edge interactions. This record includes the crystal structures optimized through first-principles calculations, which were used to analyze the properties and interactions between water and the HB sheet. Additionally, input files for system calculations are provided to facilitate reproducibility of the study's results.

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: Interplay of hydrogen boride sheets with water: An insight into edge stability

boron

display_name: boron

density-functional-theory

display_name: density-functional-theory

hydrogen-boride-sheet

display_name: hydrogen-boride-sheet

water

display_name: water


en: This study proposes an eco-friendly approach to zeolite agglomeration for petroleum sorbents. The novelty lies in integrating agglomeration and deagglomeration within a single high-pressure grinding roll (HPGR) system, enhancing sorption capacity by creating a secondary porosity network. This eliminates energy-intensive calcination, making it a sustainable alternative to wet granulation. We examine the impact of binder and water dosages on sorption capacity, mechanical resistance, and textural properties of roll-compacted zeolite agglomerates. Feed materials were characterized using N₂ adsorption, XRD, XRF, particle size distribution, and SEM. Structural and functional properties were assessed via mercury intrusion porosimetry, petroleum sorption efficiency (Westinghouse test), sorption capacity, gravitational drop tests, and SEM. All sorbents (0.5–1 mm) met the 50 wt.% oil absorbency threshold for petroleum spill cleanup in Poland. The fabricated zeolite agglomerates exhibited superior sorption capacities compared to zeolite powder, Na-P1, and commercial sorbents. The optimal feed composition yielded sorbents with the best properties and versatile performance. An analysis of sorption characteristics and pore size distribution showed that a higher proportion of 10–100 µm pores improved efficiency. This study addresses the knowledge gap in zeolite powder agglomeration and demonstrates the effectiveness of integrating agglomeration and deagglomeration in a high-pressure roller press.

materialscloud

en: Materials Cloud

en: Sustainable approach to zeolite agglomeration for petroleum spill management - Agglomeration and deagglomeration integrated in one HPGR system

binder-powder

display_name: binder-powder

natural-zeolite-powder

display_name: natural-zeolite-powder

xrd-data

display_name: xrd-data


en: We investigate the superconducting properties of molybdenum disulphide (MoS₂) monolayer across a broad doping range, successfully recreating the so far unresolved superconducting dome. Our first-principles findings reveal several dynamically stable phases across the doping-dependent phase diagram. We observe a doping-induced increase in the superconducting transition temperature Tc, followed by a reduction in Tc due to the formation of charge density waves (CDWs), polaronic distortions, and structural transition from the H to the 1T′ phase. Our work reconciles various experimental observations of CDWs in MoS₂ with its doping-dependent superconducting dome structure, which occurs due to the 1×1 H to 2×2 CDW phase transition.

en: Understanding the origin of superconducting dome in electron-doped MoS₂ monolayer

envi

en: Environment

charge-density-wave

display_name: charge-density-wave

first-principles

display_name: first-principles

lattice-model

display_name: lattice-model

migdal-eliashberg

display_name: migdal-eliashberg

nonadiabaticity

display_name: nonadiabaticity

polarons

display_name: polarons

prace

display_name: prace

superconductivity

display_name: superconductivity


en:
In this entry is a database of 324,426 hypothetical Metal-Organic Frameworks (MOFs) that were used in a study to screen potential carbon dioxide scrubbers.  Using a method to assemble these materials with topological blueprints, we only selected materials that could be accurately represented with the MEPO-QEq charge generation method. By ensuring that the electrostatic potential is accurately represented in these materials, screening for CO2 adsorption properties would result very few false positives/negatives. The atom-centered charges reported in the CIF file for each MOF were derived from the MEPO-QEq method, which can be found under the '_atom_type_partial_charge' column in each CIF file.

The relevant data for each MOF is reported in accompanying .csv files. Post-combustion flue gas was simulated at a temperature of both 298K and 0.15 bar CO2, and 313K and 0.15 bar CO2. Mixture adsorption was simulated with the conditions 298K and 0.15:0.85 CO2/N2 with a total pressure of 1 bar. The data file reports working capacities, which is the difference of adsorption of CO2 between two thermodynamic state points. The adsorption state point(s) are mentioned above, and two desorption values were simulated; 0.1 bar CO2 at 363K (vacuum swing adsorption) and 0.7 bar CO2 at 413K (temperature swing adsorption). The data presented in the main manuscript correspond to vacuum swing conditions.

Over 8,000 materials were selected for more refined simulations, including re-defining partial atomic charges with the REPEAT method, and more detailed simulations to obtain common chemical patterns surrounding CO2 binding sites (adsorbaphores). There is an additional .csv file with these refined calculations that accompany this entry titled 'top_MOFs_screening_data.csv'.

en: Data-driven design of metal-organic frameworks for wet flue gas CO2 capture

adsorbaphore

display_name: adsorbaphore

gas-adsorption

display_name: gas-adsorption

high-throughput-screening

display_name: high-throughput-screening

nanoporous-materials

display_name: nanoporous-materials


en: Materials that efficiently promote the thermodynamically uphill water-splitting reaction under solar illumination are essential for generating carbon-free ("green") hydrogen. Mapping out the combinatorial space of potential photocatalysts for this reaction can be expedited using data-intensive materials exploration. The calculated band gaps and band alignments can serve as key indicators and metrics to computationally screen photoactive materials. Ternary main-group metal sulfides containing p- and s-block elements represent a promising, albeit underexplored, class of photocatalysts. Here, we computationally screen 86 candidate ternary main-group metal sulfides containing p- and s-block elements. By validating electronic structure predictions against experimental band gaps and band edges for synthetically accessible materials, we propose eight potential photocatalysts. Using computed Pourbaix diagrams, we further narrowed the candidate pool to four materials based on the predicted aqueous stability. We then synthesized and characterized these four materials and experimentally screened them for photoresponsiveness under photocatalytically relevant conditions. We also characterized their experimental band gaps and band edge positions and compared them with computational predictions. Based on the experimental screening protocols, we identify MgIn₂S₄ and BaSn₂S₅ as photoresponsive materials with sufficient aqueous stability to be considered in greater depth as potential photocatalysts for overall water-splitting. This record contains the computational predictions for the four candidates discussed in our manuscript.

en: Data-intensive exploration of the photoelectrochemical responses of main-group metal sulfides

photocatalysts

display_name: photocatalysts

sulfides

display_name: sulfides

water-splitting

display_name: water-splitting


en: Within many-body perturbation theory, we calculate band offsets for a set of epitaxial interfaces, including AlP/GaP, AlAs/GaAs, Ge/AlAs, Ge/GaAs, Ge/ZnSe, Si/GaP, ZnSe/GaAs, and CaF2/Si. We consider various quasiparticle self-consistent 𝐺𝑊 schemes with or without including vertex functions. In particular, we consider two types of effective vertex functions complying with the Ward identity in the long range, one of which additionally carries a short-range part, which has been found to improve ionization potentials. The obtained band offsets correspond to model interface structures that match the experimental lattice parameters of the bulk components. Strain, zero-phonon renormalization, and spin-orbit coupling effects are properly accounted for. For the band offsets of the semiconductor-semiconductor interfaces, all the self-consistent 𝐺𝑊 schemes yield similar mean absolute errors on the order of 0.2 eV. In the case of the CaF2/Si interface, the calculated band offsets show large indetermination spanning an interval up to 1 eV, the discrepancy with respect to experiment being correlated with the error by which the band gap of the insulator is described. Through 𝐺𝑊 calculations for selected interface models, we further assess the effect of self-consistently updating the charge density. Our result support the practice of relying on semilocal or hybrid-functional schemes for determining the line-up potential.

en: Band alignments through quasiparticle self-consistent 𝐺𝑊 with efficient vertex corrections

band-alignments

display_name: band-alignments

epitaxial-interfaces

display_name: epitaxial-interfaces

gw-calculations

display_name: gw-calculations


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


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

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: 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.

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: 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.

materialscloud

en: Materials Cloud

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

envi

en: Environment

energy-landscape

display_name: energy-landscape

first-principles

display_name: first-principles

hubbard-corrections

display_name: hubbard-corrections

marvel

display_name: marvel

pbe

display_name: pbe

pbeu

display_name: pbeu

quantum-espresso

display_name: quantum-espresso

romeodft

display_name: romeodft

two-dimensional-magnetic-materials

display_name: two-dimensional-magnetic-materials


en:
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: 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

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

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: 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.

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: 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.

materialscloud

en: Materials Cloud

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: 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

envi

en: Environment

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:
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: 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

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.

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: 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.

materialscloud

en: Materials Cloud

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: 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

envi

en: Environment

defect

display_name: defect

dft

display_name: dft

oxide

display_name: oxide

time-dependent-dft

display_name: time-dependent-dft


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

ab-initio

display_name: ab-initio

anharmonicity

display_name: anharmonicity

electron-phonon-coupling

display_name: electron-phonon-coupling

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

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

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.

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).

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.

materialscloud

en: Materials Cloud

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