en: In this paper, we present a workflow that is designed to work without manual intervention to efficiently predict, by using molecular simulations, the thermodynamic data that is needed to design a carbon capture process. We developed a procedure that does not rely on fitting of the adsorption isotherms. From molecular simulations, we can obtain accurate data for both, the pure component isotherms as well as the mixture isotherms. This allowed us to make a detailed comparison of the different methods to predict the mixture isotherms. All approaches rely on an accurate description of the pure component isotherms and a model to predict the mixture isotherms. As we are interested in low CO₂ concentrations, it is essential that these models correctly predict the low pressure limit, i.e., give a correct description of the Henry regime. Among the equations that describe this limit correctly, the dual-site Langmuir (DSL) model is often used for the pure components and the extended DSL (EDSL) for the mixtures. An alternative approach, which avoids describing the pure component isotherms with a model, is to numerically integrate the pure component isotherms in the context of IAST.  In this work we compare these two methods. In addition, we show that the way these data are fitted for DSL can significantly impact the ranking of materials, in particular for capture processes with low concentration of CO₂ in the feed stream.

en: A robust framework for generating adsorption isotherms to screen materials for carbon capture

dual-site-langmuir-dsl

display_name: dual-site-langmuir-dsl

ideal-adsorbed-solution-theory-iast

display_name: ideal-adsorbed-solution-theory-iast

metal-organic-frameworks-mofs

display_name: metal-organic-frameworks-mofs

temperature-swing-adsorption-tsa

display_name: temperature-swing-adsorption-tsa


$oid: OBSOLETE

en: Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials exhibited, however, strong biases originating from underrepresented chemical elements and structural prototypes in the available data. We tackled this issue computing additional data to provide better balance across both chemical and crystal-symmetry space. Crystal-graph networks trained with this new data show unprecedented generalization accuracy, and allow for reliable, accelerated exploration of the whole space of inorganic compounds. We applied this universal network to performed machine-learning assisted high-throughput materials searches including 2500 binary and ternary prototypes and spanning about 1 billion compounds. After validation using density-functional theory, we uncover in total 19512 additional materials on the convex hull of thermodynamic stability and around 150000 compounds with a distance of less than 50 meV/atom from the hull. Here we include the DCGAT-1, DCGAT-2, and DCGAT-3 datasets used in this work.

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: Large-scale machine-learning-assisted exploration of the whole materials space

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

crystal-graph-attention-networks

display_name: crystal-graph-attention-networks

density-functional-theory

display_name: density-functional-theory

high-throughput

display_name: high-throughput


en: The urgent need for clean energy coupled with the exceptional promise of hydrogen (H) as a clean fuel is driving development of new metals resistant to hydrogen embrittlement. Experiments on new fcc high entropy alloys present a paradox: these alloys absorb more H than Ni or SS304 (austenitic 304 stainless steel) while being more resistant to embrittlement. Here, a new theory of embrittlement in fcc metals is presented based on the role of H in driving an intrinsic ductile-to-brittle transition at a crack tip. The theory quantitatively predicts the H concentration at which a transition to embrittlement occurs in good agreement with experiments for SS304, SS316L, CoCrNi, CoNiV, CoCrFeNi, and CoCrFeMnNi. The theory rationalizes why CoNiV is the alloy most resistant to embrittlement and why SS316L is more resistant than the high entropy alloys CoCrFeNi and CoCrFeMnNi, which opens a path for the computationally guided discovery of new embrittlement-resistant alloys.

en: Mechanism and prediction of hydrogen embrittlement in fcc stainless steels and high entropy alloys

fracture

display_name: fracture

high-entropy-alloys

display_name: high-entropy-alloys

hydrogen-embrittlement

display_name: hydrogen-embrittlement

snsf

display_name: snsf


en: Twinning in fcc High Entropy Alloys (HEAs) has been implicated as a possible mechanism for hardening that enables enhanced ductility. Here, a theory for the twinning stress is developed analogous to recent theories for yield stress. Specifically, the stress to move a twin dislocation, i.e an fcc partial dislocation moving along a pre-existing twin boundary, through a random multicomponent alloy is determined. A reduced elasticity theory is then introduced in which atoms interact with the twin dislocation pressure field and the twin boundary. The theory is applied to NiCoCr using results from both interatomic potentials and elasticity theory. Results are also used to predict the increased stress for the motion of (i) a single partial dislocation leaving a trailing stacking fault and (ii) adjacent partial dislocations involved in twin nucleation. Increased strength is predicted for all processes involved in the nucleation and growth of fcc twins. Comparison to single-crystal experiments at room temperature then suggests that twinning is controlled by twin nucleation, with reasonable quantitative agreement. When solute/fault interactions are neglected, the theory shows that twinning and lattice flow stresses are related. The theory also provides insight into how other dilute solute additions could suppress twinning, as found experimentally.

en: Theory of twin strengthening in fcc high entropy alloys

random-alloys

display_name: random-alloys

solute-strengthening

display_name: solute-strengthening

twinning

display_name: twinning


en: Many relationships important to civil engineering depend on surface roughness (morphology). Examples are the bond strength between concrete layers, the adhesion of a wheel to the pavement, the angle of friction in the soil in contact with a wall surface, and many other cases when we deal with a material with a surface having the characteristics of a Gaussian field. Based on scans of the natural concrete surfaces subjected to different smoothing processes, theoretical models were made. The observed features of the models were grouped into 25 categories belonging to the spherical semivariogram model. Each category is described by two parameters: range (with discrete domain 0.01, 0.04, 0.08, 0.16, 0.32) and upper limits (also with discrete domain 1, 2, 4 , 8, 16) with zero trend. For all combinations of range-limit pairs, homogeneous Gaussian random fields satisfying the spatial dependence of the category were generated in R software by using the RandomFields library. In the final step, the Blender rendering software was used. 1,000 images were taken for each category type using constant angle lighting and moving the camera over the generated surface. Users can download a set of 25,000 images to build or verify the surface classifier. The generated 25 types of surfaces are also provided.

en: Shadow-light images of simulated 25 classes of surface roughness for automatic classification

civil-engineering

display_name: civil-engineering

classification

display_name: classification

concrete

display_name: concrete

deep-learning

display_name: deep-learning

machine-learning

display_name: machine-learning

shadow-light-images

display_name: shadow-light-images

virtual-modeling

display_name: virtual-modeling


en: Pure magnesium (Mg) is an attractive metal for structural applications due to its low density, but also has low ductility and low fracture toughness. Dilute alloying of Mg with rare earth elements in small amounts improves the ductility, but the effects of alloying on fracture are not well-established. Here, the intrinsic fracture of a model Mg-3at%Y solid solution alloy is studied using a combination of anisotropic linear elastic fracture mechanics and atomistic simulations applied to a comprehensive set of crack configurations under mode I loading. The competition between brittle cleavage and ductile dislocation emission at the crack tip in Mg is improved slightly by alloying, because local fluctuations of the random solutes enable dislocation emission rather than cleavage fracture for a number of configurations where the differences in critical load for cleavage and emission are small. However, basalplane cleavage remains strongly preferred, as in pure Mg. The alloys do show higher fracture toughness for all configurations due to local solute-induced deformation phenomena at the crack tip. Thus, alloying with Y is expected to improve the fracture toughness of Mg, but the persistence of basal cleavage prevents the alloy from becoming intrinsically ductile for all orientations.

en: Intrinsic fracture behavior of Mg–Y alloys

alloy

display_name: alloy

magnesium

display_name: magnesium

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


en: We have studied the interaction of water and graphene doped with nitrogen in different configurations, namely, graphitic and pyridinic nitrogen, by means of the van der Waals density functional. We found that the local nitrogen configuration plays a key role in determining the stable water configuration, while the dispersion force is responsible for the water adsorption. With the graphitic nitrogen, water prefers to orient with its oxygen toward the surface, whereas for the pyridinic nitrogen it prefers to orient with its hydrogens toward the surface, because nitrogen is positively and negatively charged for the former and the latter, respectively. Our results have great implications for the modeling of the interface between water and nitrogen-doped graphitic systems.

en: Interaction of water with nitrogen-doped graphene

graphene

display_name: graphene

van-der-waals-density-functional

display_name: van-der-waals-density-functional

water

display_name: water


en: 'Magic'-angle twisted bilayer graphene has received a lot of interest due to its flat bands with potentially non-trivial topology that lead to intricate correlated phases. A spectrum with flat bands, however, does not require a twist between multiple sheets of van der Waals materials, but rather can be realized with the application of an appropriate periodic potential. Here, we propose the imposition of a tailored periodic potential onto a single graphene layer through local perturbations that could be created via lithography or adatom manipulation, which also results in an energy spectrum featuring flat bands. Our first-principle calculations for an appropriate decoration of graphene with adatoms indeed show the presence of flat bands in the spectrum. Furthermore, we reveal the topological nature of the flat bands through a symmetry-indicator analysis. This non-trivial topology manifests itself in corner-localized states with a filling anomaly as we show using a tight-binding model. Our proposal of a single decorated graphene sheet provides a new versatile route to study correlated phases in topologically non-trivial, flat band structures.

en: Flat bands with fragile topology through superlattice engineering on single-layer graphene

marvel

display_name: marvel

topological-materials

display_name: topological-materials

vasp

display_name: vasp


en: A statistical-mechanics analysis is used to create analytic estimates for the short-range order (SRO) parameters in FCC and BCC solid solution alloys as a function of composition and temperature. As in the classic quasi-chemical method, the analysis assumes pair interactions among atoms at different distances and partitions the entire crystal into a set of identical independent clusters that are then treated like molecules in a gas.  This enables analytic development of the probabilities that govern SRO.  For binary and ternary alloys, increasingly complex clusters are considered that systematically extend the range of accuracy of the analytic model relative to reference Monte Carlo simulations.  The results enable fast assessment of likely SRO using estimated inputs for atom-atom interaction energies. In this record we provide our analytical solution as well as the simulation data supporting our theoretical prediction of the short-range order parameter in both FCC and BCC alloys.

en: Analytical models of short-range order in FCC and BCC alloys

high-entropy-alloys

display_name: high-entropy-alloys

short-range-order

display_name: short-range-order

snsf

display_name: snsf

solid-solutions

display_name: solid-solutions

statistical-mechanics

display_name: statistical-mechanics


en: The heterogeneously catalyzed HCl oxidation reaction (Deacon reaction) over ceria leads under typical reaction conditions to a reduction and surface chlorination of CeO2. The reduced single crystalline CeO2-x(111) model surface stabilizes various ordered surface structures, e.g. (√7 × √7)R19.1°, (3 × 3), or (4 × 4), depending on the concentration of oxygen vacancies (VO). Saturating these phases with HCl at room temperature, followed by annealing to the process temperature of 700 K, leads in all cases to a uniformly covering (√3 × √3)R30° overlayer structure with identical Cl coverage and identical adsorption geometry. Low energy electron diffraction (LEED) fingerprinting, density functional theory (DFT) calculations and X-ray photoelectron spectroscopy (XPS) evidence that Cl adsorbs into the O-vacancy at the surface (Clvac) with a high adsorption energy (>2 eV). From thermal desorption spectroscopy (TDS) and XPS of Cl 2p the adsorption energy of Clvac and the water formation is found to dependent sensitively on the degree of bulk-reduction x of CeO2-x(111). Chlorine desorption in TDS shifts from 1175 K to 1320 K when the the reduction degree x is increased from CeO1.8(111) (x = 0.2) to CeO1.6(111) (x = 0.4). In order to rationalize why the formation of (√3 × √3)R30°-Clvac structure on CeO2-x(111) is independent of the original reduction degree x of CeO2 x(111), efficient diffusion of surface and bulk oxygen vacancies (VO) is required.

materialscloud

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

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

en: Materials Cloud

en: Dynamic response of oxygen vacancies on the Deacon reaction over reduced single crystalline CeO₂-x(111) surfaces

chlorine-adsorption

display_name: chlorine-adsorption

deacon-process

display_name: deacon-process

hcl-oxidation

display_name: hcl-oxidation

leed-fingerprinting

display_name: leed-fingerprinting

oxygen-vacancy-mobility

display_name: oxygen-vacancy-mobility

reduced-ceria

display_name: reduced-ceria


infrared-spectra-in-amorphous-alumina

en: We present a combined study based on experimental measurements of infrared (IR) dielectric function and first-principles calculations of IR spectra and vibrational density of states (VDOS) of amorphous alumina (am-Al₂O₃). In particular, we show that the main features of the imaginary part of the dielectric function ε₂(ω) at ~380 and 630 cm-¹ are related to the motions of threefold coordinated oxygen atoms, which are the vast majority of oxygen atoms in am-Al₂O₃. Our analysis (involving three model structures) provides an alternative point of view with respect to an earlier suggested assignment of the vibrational modes, which relates them to the stretching and bending vibrational modes of AlOₙ (n = 4, 5, and 6) polyhedra. Our assignment is based on the additive decomposition of the VDOS and ε₂(ω) spectra, which shows that: (i) the band at ~380 cm-¹ features oxygen motions occurring in a direction normal to the plane defined by the three nearest-neighbor aluminum atoms, i.e. out-of-plane motions of oxygen atoms; (ii) Al-O stretching vibrations (i.e. in-plane motions of oxygen atoms) appear at frequencies above ~500 cm-¹, which characterize the vibrational modes underlying the band at ~630 cm-¹.

en: Infrared spectra in amorphous alumina

title_for_slug: infrared-spectra-in-amorphous-alumina

envi

en: Environment

dft

display_name: dft

infrared

display_name: infrared

vdos

display_name: vdos


en: Determining ab-initio potential dependent energetics are critical to investigating mechanisms for electrochemical reactions. While methodology for evaluating reaction thermodynamics is established, simulation techniques for the corresponding kinetics is still a major challenge owing to a lack of potential control, finite cell size effects or computational expense. In this work, we develop a model which allows for computing electrochemical activation energies from just a handful of Density Functional Theory (DFT) calculations. The sole input into the model are the atom centered forces obtained from DFT calculations performed on a homogeneous grid composed of varying field-strengths. We show that the activation energies as a function of the potential obtained from our model are consistent for different super-cell sizes and proton concentrations for a range of electrochemical reactions. This record contains output files from all the DFT calculations needed to reproduce the figures in the manuscript.

en: Force-based method to determine the potential dependence in electrochemical barriers

electrochemical-barriers

display_name: electrochemical-barriers

electrochemical-kinetics

display_name: electrochemical-kinetics

proton-electron-transfer-reactions

display_name: proton-electron-transfer-reactions


en: The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)heating of batteries. The reason for this lies in the complexity of the calculations: on one hand, the diffusion of ionic charge carriers makes lattice methods formally unsuitable due to the lack of equilibrium atomic positions needed for normal-mode expansion. On the other hand, the prohibitive cost of large-scale molecular dynamics (MD) simulations of heat transport in large systems at ab initio levels has hindered the use of MD-based methods. In this work, we leverage recently developed machine-learning potentials targeting different ab initio functionals (PBEsol, r2SCAN, PBE0) and a state-of-the-art formulation of the Green-Kubo theory of heat transport in multicomponent systems to compute the thermal conductivity of a promising solid-state-electrolyte, Li3PS4, in all its polymorphs (α, β and γ). By comparing MD estimates with lattice methods on the low-temperature, non-diffusive γ-Li3PS4, we highlight strong anharmonicities and negligible nuclear quantum effects, hence further justifying MD-based methods even for non-diffusive phases. Finally, for the ion-conducting α and β phases, where the multicomponent Green-Kubo MD approach is mandatory, our simulations indicate a weak temperature dependence of the thermal conductivity, a glass-like behavior due to the effective local disorder characterizing these Li-diffusing phases.

en: Thermal transport of Li₃PS₄ solid electrolytes with ab initio accuracy

epfl

display_name: epfl

lab-cosmo

display_name: lab-cosmo

machine-learning

display_name: machine-learning

marvel

display_name: marvel

max

display_name: max

solid-state-electrolytes

display_name: solid-state-electrolytes

swissuniversities

display_name: swissuniversities

thermal-transport

display_name: thermal-transport


en: Body-centered-cubic (BCC) high entropy alloys (HEAs) can show exceptionally high strength up to high temperatures. Mechanistic theories are needed to guide alloy discovery within the immense multicomponent HEA compositional space. Here, two new theories for strengthening as controlled by screw and edge dislocations, respectively, are applied to predict the yield stresses of a range of BCC alloys over a wide range of temperatures. Results show that the screw theory, with one fitting parameter, can capture experiments in many dilute and non-dilute alloys while the parameter-free edge theory agrees with experiments in non-dilute alloys having a sufficiently large misfit parameter. These results indicate a transition in single-phase alloy strengthening from traditional screw dominance to edge dominance with increasing misfit that is enabled in complex non-dilute alloys. These results point to the use of the edge theory to guide design of high-temperature alloys in the non-dilute range.

en: Screw vs. edge dislocation strengthening in body-centered-cubic high entropy alloys and implications for guided alloy design

alloy-discovery

display_name: alloy-discovery

bcc

display_name: bcc

high-entropy-alloys

display_name: high-entropy-alloys

solute-strengthening

display_name: solute-strengthening


en: We present a method, based on the classical Green-Kubo theory of linear response, to compute the heat conductivity of extended systems, leveraging energy-density, rather than energy-current, fluctuations, thus avoiding the need to devise an analytical expression for the macroscopic energy flux. The implementation of this method requires the evaluation of the long-wavelength and low-frequency limits of a suitably defined correlation function, which we perform using a combination of recently-introduced cepstral-analysis and Bayesian extrapolation techniques. Our methodology is demonstrated against standard current-based Green-Kubo results for liquid argon and water, and solid amorphous Silica, and compared with a recently proposed similar technique, which utilizes mass-density, instead of energy-density, fluctuations.

en: Heat conductivity from energy-density fluctuations

energy-density

display_name: energy-density

equilibrium-molecular-dynamics

display_name: equilibrium-molecular-dynamics

heat-conductivity

display_name: heat-conductivity


en:
The behavior of adsorbate-induced surface transformation can be clearly understood given the mechanical aspects of such phenomenon are well described at the atomic level. In this study, we provide the atomic-level description on the formation of Cu clusters on the Cu(111) surface by performing set of molecular dynamics simulations driven by machine-learning force-field. The machine learning technique called Gaussian Process (GP), as implemented in FLARE v1.1.2 (https://github.com/mir-group/flare/tree/1.1.2) was used to construct the machine-learning force-field. The dynamics simulations were performed using LAMMPS v29Sept2021 (https://github.com/lammps/lammps/tree/stable_29Sep2021_update2). This archive contains some supplementary data including the validation structures and also the MGP potential used to drive the MD simulations in LAMMPS. Additionally, the GP potential (before mapping) containing the database of atomic environment is also made available.

The simulations at 450 K–550 K show clusters are formed within a hundred of ns when the Cu surface is exposed with CO. On the other hand, no cluster is formed within the same time interval on the clean Cu surface even at 550 K, which signifies the importance of CO exposure to the surface transformation. The effect of temperature to the formation of clusters is also investigated. The CO-decorated Cu clusters ranging from dimer to hexamer are detected within a hundred of ns at 450 K. Lowering the temperature to 350 K does not result in the formation of clusters within a hundred ns due to the scarce detachments of adatom, while raising the temperature to 550 K results in the formation of more clusters, ranging from dimer to heptamer, but with shorter lifetimes. The clusters can be formed directly through instantaneous detachment of a group of step-atoms or indirectly by aggregation of wandering Cu monomers and smaller clusters on the surface terrace. The preference to the indirect mechanism is indicated by the higher frequency of its occurrence. More details on the procedures and results of the research can be found in the paper.

en: Machine learning molecular dynamics simulation of CO-driven formation of Cu clusters on the Cu(111) surface

carbon-monoxide

display_name: carbon-monoxide

cu-surface

display_name: cu-surface

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

surface-reconstruction

display_name: surface-reconstruction


en: The Random-Phase approximation (RPA) provides an appealing framework for semi-local density functional theory. In its current formulation, it is cost-effective and has a better scaling behaviour compared to other wavefunction based correlation methods. To broaden the application field for RPA, it is necessary to have first order properties available. RPA nuclear gradients allow for structure optimizations and data sampling for machine learning applications. We report on an efficient implementation of RPA nuclear gradients for massively parallel computers. We apply the implementation to two polymorphs of the benzene crystal obtaining very good cohesive and relative energies. Different correction and extrapolation schemes are investigated for further improvement of the results and in order to estimate error bars.

en: Massively parallel implementation of gradients within the Random Phase Approximation: Application to the polymorphs of benzene

benzene

display_name: benzene

density-functional-theory

display_name: density-functional-theory

high-performance-computing

display_name: high-performance-computing

nuclear-gradients

display_name: nuclear-gradients

pasc

display_name: pasc

polymorphs

display_name: polymorphs

random-phase-approximation

display_name: random-phase-approximation


en: The time-honored Allen-Feldman theory of heat transport in glasses is generally assumed to predict a finite value for the thermal conductivity, even if it neglects the anharmonic broadening of vibrational normal modes. We demonstrate that the harmonic approximation predicts that the bulk lattice thermal conductivity of harmonic solids inevitably diverges at any temperature, irrespective of configurational disorder, and that its ability to represent the heat-transport properties observed experimentally in most glasses is implicitly due to finite-size effects. Our theoretical analysis is thoroughly benchmarked against careful numerical simulations. Our findings thus reveal that a proper account of anharmonic effects is indispensable to predict a finite value for the bulk thermal conductivity in any solid material, be it crystalline or glassy. This record contains data and scripts to support the findings of the manuscript and ensure their reproducibility.

materialscloud

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

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

en: Materials Cloud

en: Unearthing the foundational role of anharmonicity in heat transport in glasses

amorphous-solids

display_name: amorphous-solids

glass

display_name: glass

max

display_name: max

thermal-transport

display_name: thermal-transport


en: Two-dimensional superconductors attract great interest both for their fundamental physics and for their potential applications, especially in the rapidly growing field of quantum computing.  In this, we predict a remarkably high superconducting critical temperature of 21 K in the easily exfoliable, topologically nontrivial 2D semimetal W2N3. Furthermore we study how strain and doping can affect its superconducting properties.

en: Prediction of phonon-mediated superconductivity with high critical temperature in the two-dimensional topological semimetal W2N3

envi

en: Environment

electron-phonon-coupling

display_name: electron-phonon-coupling

marie-curie-fellowship

display_name: marie-curie-fellowship

prace

display_name: prace

snsf

display_name: snsf

superconductivity

display_name: superconductivity


en: Atomically precise graphene nanoflakes called nanographenes have emerged as a promising platform to realize carbon magnetism. Their ground state spin configuration can be anticipated by Ovchinnikov-Lieb rules based on the mismatch of π electrons from two sublattices. While rational geometrical design achieves specific spin configurations, further direct control over the π electrons offers a desirable extension for efficient spin manipulations and potential quantum device operations. To this end,  in a recent publication, we applied a site-specific dehydrogenation using a scanning tunneling microscope tip to nanographenes deposited on a Au(111) substrate, which showed the capability of precisely tailoring the underlying π-electron system and therefore efficiently manipulating their magnetism. Through first-principles calculations and tight-binding meanfield-Hubbard modeling, we demonstrated that the dehydrogenation-induced Au—C bond formation along with the resulting hybridization between frontier π orbitals and Au substrate states effectively eliminate the unpaired π electron. Our results establish an efficient technique for controlling the magnetism of nanographenes. This record contains data that support the scientific results discussed in our manuscript.

en: Tailoring magnetism of graphene nanoflakes via tip-controlled dehydrogenation

cscs

display_name: cscs

dft

display_name: dft

magnetic-nanographene

display_name: magnetic-nanographene

on-surface-synthesis

display_name: on-surface-synthesis

stm

display_name: stm


en:
Among the family of topological superconductors derived from Bi2Se3, Cux(PbSe)5(Bi2Se3)6 is unique in its surface termination of a single quintuple layer (QL) of the topological insulator (TI) Bi2Se3 on an ordinary insulator PbSe. Here, we report a combined scanning tunneling microscopy (STM) and density functional theory (DFT) characterization of the cleaved surface of the parent compound (PbSe)5(Bi2Se3)6 (PSBS). Interestingly, the potential disorder due to the random distribution of native defects is only Γ ∼ 4 meV, among the smallest reported for TIs. Performing high-resolution quasiparticle interference imaging (QPI) near the Fermi energy (E−EF = −1 eV to 0.6 eV) we reconstruct the dispersion relation of the dominant spectral feature and our ab initio calculations show that this surface feature originates from two bands with Rashba-like splitting due to strong spin-orbit coupling and inversion symmetry breaking. Moreover, a small hexagonal distortion of the calculated Fermi surface is seen in the full momentum space distribution of the measured scattering data. Interestingly, the scattering pattern transforms into a flower-like shape with suppressed intensity along the ΓK direction, at lower energies. However, this change is not due to the forbidden backscattering in the topological surface state in Bi2Se3 but the threefold symmetry of the scattering potential itself.

This dataset contains the experimental and theoretical data of this work.

en: Quasiparticle interference on the surface of Bi2Se3 terminated (PbSe)5(Bi2Se3)6

experimental

display_name: experimental

fleur

display_name: fleur

jukkr

display_name: jukkr

qpi

display_name: qpi

quasiparticle-interference

display_name: quasiparticle-interference

topological-materials

display_name: topological-materials

topological-superconductor

display_name: topological-superconductor


en: We present Molecular Vibration Explorer, a freely accessible online database and interactive tool for exploring vibrational spectra and tensorial light-vibration coupling strength of a large collection of thiolated molecules. The `Gold' version of the database gathers the results from density functional theory calculations on 2'800 commercially available thiol compounds linked to a gold atom, with the main motivation to screen the best molecules for THz and mid-infrared to visible upconversion. Additionally, the `Thiol' version of the database contains results for 1'900 unbound thiolated compounds.They both provide access to a comprehensive set of computed spectroscopic parameters for all vibrational modes of all molecules in the database. Infrared absorption, Raman scattering and vibrational sum- and difference frequency generation cross sections can be simultaneously investigated by the user. Molecules can be screened for various parameters in custom frequency ranges, such as large Raman cross-section under specific molecular orientation, or large orientation-averaged sum-frequency generation (SFG) efficiency. The user can select polarization vectors for the electromagnetic fields, set the orientation of the molecule and customize parameters for plotting the corresponding IR, Raman and sum-frequency spectra.

en: Molecular vibration explorer: an online database and toolbox for surface-enhanced frequency conversion, infrared and Raman spectroscopy

density-functional-theory

display_name: density-functional-theory

light-vibration-couplings

display_name: light-vibration-couplings

molecular-optomechanical-conversion

display_name: molecular-optomechanical-conversion

vibrational-properties

display_name: vibrational-properties


bound-by-three-body-interactions

en:
Stable bound quantum states are ubiquitous in nature. Mostly, they result from the interaction of only pairs of particles, so called two-body interactions, even when large complex many-particle structures are formed. We show that three-particle bound states occur in a generic, experimentally accessible solid state system: antiferromagnetic spin ladders, related to high-temperature superconductors. Strikingly, this binding is induced by genuine three-particle interactions; without them there is no bound state. We compute the dynamic exchange structure factor required for the experimental detection of the predicted state by resonant inelastic x-ray scattering for realistic material parameters. Our work enables us to quantify these elusive interactions and unambiguously establishes their effect on the dynamics of the quantum many-particle state.
The data provided here contains the Green function and the weights of the dynamic exchange structure factor and the corresponding Lanczos coefficients.

en: Bound by three-body interactions

title_for_slug: bound-by-three-body-interactions

bound-state

display_name: bound-state

cut

display_name: cut

rixs

display_name: rixs

spin-ladder

display_name: spin-ladder

theoretical

display_name: theoretical

triplon

display_name: triplon


en: We investigate the structural, electronic, vibrational, power, and transport properties of the β allotrope of Ga2O3 from first principles. We find phonon frequencies and elastic constants that reproduce the correct band ordering, in agreement with experiment. We use the Boltzmann transport equation to compute the intrinsic electron and hole drift mobility and obtain room-temperature values of 258 and 1.2 cm2/Vs, respectively, as well as 6300 and 13 cm2/Vs at 100 K. Through a spectral decomposition of the scattering contribution to the inverse mobility, we find that multiple longitudinal-optical modes of Bu symmetry are responsible for the electron mobility of β-Ga2O3 but that many acoustic modes also contribute, making it essential to include all scattering processes in the calculations. Using the von Hippel low-energy criterion, we computed the breakdown field to be 5.8 MV/cm at room temperature, yielding a Baliga figure of merit of 1250 with respect to silicon, ideal for high-power electronics. This work presents a general framework to predictively investigate novel high-power electronic materials.

en: Structural, electronic, elastic, power, and transport properties of β-Ga2O3 from first principles

ab-initio

display_name: ab-initio

breakdown-field

display_name: breakdown-field

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

marie-curie-fellowship

display_name: marie-curie-fellowship

prace

display_name: prace

transport

display_name: transport


en: Osmium complexes with osmium in different oxidation states (II, III, IV, VI) have been reported to exhibit antiproliferative activity in cancer cell lines. Herein, we demonstrate unexplored opportunities offered by 187Os nuclear forward scattering (NFS) and nuclear inelastic scattering (NIS) of synchrotron radiation for characterization of hyperfine interactions and lattice dynamics in a benchmark Os(VI) complex, K₂[OsO₂(OH)₄]. The isomer shift (𝛿 = 3.3(1) mm/s) relative to [OsIVCl₆]2– and quadrupole splitting (𝚫EQ = 12.0(2) mm/s) were determined with NFS. The Lamb-Mössbauer factor (0.80(4)) is estimated, the density of phonon states (DOS) is extracted, and a thermodynamics characterization was carried out using the NIS data combined with first principles calculations. Overall, this study provides evidence that 187Os nuclear resonance scattering is a reliable technique for the investigation of hyperfine interactions and Os specific vibrations in osmium(VI) species, and is thus applicable for such measurements in osmium complexes of other oxidation states, including those with anticancer activity such as Os(III) and Os(IV).

en: 187Os nuclear resonance scattering to explore hyperfine interactions and lattice dynamics for biological applications

hyperfine-interactions

display_name: hyperfine-interactions

lattice-dynamics

display_name: lattice-dynamics

nuclear-resonance-scattering

display_name: nuclear-resonance-scattering


en:
A recently developed bottom-up synthesis strategy enables the fabrication of graphene nanoribbons with well-defined width and non-trivial edge structures from dedicated molecular precursors. Here we discuss the synthesis and properties of zigzag nanoribbons (ZGNRs) modified with periodic cove-cape-cove units along their edges. Contrary to pristine ZGNRs, which show antiferromagnetic correlation of their edge states, the edge-modified ZGNRs exhibit a finite single particle band gap without localized edge states. We report the on-surface synthesis of such edge-modified ZGNRs and discuss tunneling conductance dI/dV spectra and dI/dV spatial maps that reveal a noticeable localization of electronic states at the cape units and the
opening of a band gap without presence of edge states of magnetic origin. A thorough ab initio investigation of the electronic structure identifies the conditions under which antiferromagnetically coupled, edge-localized states reappear in the electronic structure. Further modifications of the ribbon structure are proposed that lead to an enhancement of such features, which could find application in nanoelectronics and spintronics.
The record contains input files to reproduce the calculations discussed in the manuscript and the raw data of the experimental images 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: Materials Cloud

en: Graphene nanoribbons with mixed cove-cape-zigzag edge structure

density-functional-theory

display_name: density-functional-theory

electronic-structure

display_name: electronic-structure

graphene-nanoribbons

display_name: graphene-nanoribbons

surface-chemistry

display_name: surface-chemistry


en: Recent theory proposes that edge dislocations in random body-centered cubic (BCC) high entropy alloys have high barriers for motion, conveying high strengths up to high temperatures. Here, the energy barriers for edge motion are computed for two model alloys, NbTaV and MoNbTaW as represented by interatomic potentials, using the Nudged Elastic Band method and compared to theoretical predictions. The average magnitude of the barriers and the average spacing of the barriers along the glide direction agree well with the analytical theory, with no adjustable parameters. The evolution of the barriers versus applied stress is modeled, and the mean strength is in reasonable agreement with the predicted zero-temperature strength. These findings validate the analytic theory. A reduced analytic model based on solute misfit volumes is then applied to Hf-Mo-Nb-Ta-Ti-Zr and Mo-Nb-Ta-Ti-V-W alloys, rationalizing the observed significant strength increases at room temperature and 1000 ∘C upon addition of solutes with large misfit into a base alloy. The analytic theory for edge motion is thus a powerful validated tool for guiding alloy selection.

en: High energy barriers for edge dislocation motion in body-centered cubic high entropy alloys

envi

en: Environment

atomistic-models

display_name: atomistic-models

high-entropy-alloys

display_name: high-entropy-alloys

snsf

display_name: snsf

solute-strengthening

display_name: solute-strengthening


en: Superelasticity, being a reversible nonlinear strain response to stress stimuli beyond the linear elastic regime, is always associated with phase transformations in its host materials, mostly metals or polymers. Theoretical rationale indicates that inorganic materials with covalent/ionic bonding normally have large energy barriers for reversible structural transitions and thus host less opportunity to achieve superelasticity. Here, we demonstrate a directional tensile superelasticity in ceramic crystal GeSe through an unconventional reversible shuffle twinning mechanism instead of martensitic phase transition. We observed, with in-situ mechanical transmission electron microscopy, an evolution in stress‒strain curve from the linear elastic behavior to a nonlinear superelastic plateau, and confirmed that such superelasticity appears simultaneously together with the generation of stripy-shaped twin domains along  orientation. Theoretical calculations revealed that the shuffle twinning process from "Z-shaped" to "anti-Z-shaped" bond-configuration leads to the release of elastic potential energy, being responsible for the emergence of tensile superelasticity therein. Note that such a highly-directional superelasticity prefers to emerge at angles near the zigzag direction owing to the anisotropic Young's modulus and Poisson's ratio in GeSe, and has never been reported in superelastic materials. Our observation provides a novel strategy to exploit tensile superelasticity and nonlinear mechanics for advanced mechanical and flexible electronics.

en: A directional tensile superelasticity in ceramic crystal via reversible shuffle twinning

in-situ-tem

display_name: in-situ-tem

shuffle-twinning

display_name: shuffle-twinning

superelasticity

display_name: superelasticity


en: The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational material design. Given its usefulness and relative simplicity, it has been one of the first electronic properties used as target for machine-learning approaches going beyond interatomic potentials. A subtle but important point, well-appreciated in the condensed matter community but usually overlooked in the construction of data-driven models, is that for bulk configurations the absolute energy reference of single-particle energy levels is ill-defined. Only energy differences matter, and quantities derived from the DOS are typically independent on the absolute alignment. We introduce an adaptive scheme that optimizes the energy reference of each structure as part of the training process, and show that it consistently improves the quality of ML models compared to traditional choices of energy reference, for different classes of materials and different model architectures. On a practical level, we trace the improved performance to the ability of this self-aligning scheme to match the most prominent features in the DOS. More broadly, we believe that this work highlights the importance of incorporating insights on the nature of the physical target into the definition of the architecture and of the appropriate figures of merit for machine-learning models, that translate in better transferability and overall performance.

en: Adaptive energy reference for machine-learning models of the electronic density of states

density-of-states

display_name: density-of-states

epfl

display_name: epfl

erc

display_name: erc

machine-learning

display_name: machine-learning

marvel

display_name: marvel


en: Understanding the structure of glassy materials represents a tremendous challenge for both experiments and computations. Despite decades of scientific research, for instance, the structural origin of the density anomaly in silica glasses is still not well understood. Atomistic simulations based on molecular dynamics (MD) produce atomically resolved structure, but extracting insights about the role of disorder in the density anomaly is challenging. Here, we propose to quantify the topological differences between structural arrangements from MD trajectories using a graph-theoretical approach, such that structural differences in silica glasses that exhibit density anomaly can be captured. To balance the accuracy and speed of the MD simulations, we utilized force matching potentials to generate the silica glass structures. This approach involves casting all-atom glass configurations as networks, and subsequently applying a graph-similarity metric (D-measure). Calculated D-measure values are then taken as the topological distances between two configurations. By measuring the topological distances of configurations in silica glass simulated structures across a range of temperatures, distinct structural features could be observed at temperatures higher than the fictive temperature. In addition to quantifying structural changes in the full simulation box, we compared topological distances between local atomic environments in the glass and crystalline silica phases. Evidence from this approach suggests that more coesite-like local structures, which are less symmetric, emerge in silica glasses when density is at a minimum during the heating process.

en: Graph theory-based structural analysis on density anomaly of silica glass

force-matching-potential

display_name: force-matching-potential

graph-theory

display_name: graph-theory

molecular-dynamics

display_name: molecular-dynamics

silica-glass

display_name: silica-glass


en:
Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic polarization. We demonstrate the development and application of an integrated machine learning (ML) model that describes on the same footing structural, energetic and functional properties of barium titanate (BaTiO₃), a prototypical ferroelectric. The model uses ab initio calculations as reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio modeling. The ML model allows us to thoroughly probe the static and dynamical behavior of BaTiO₃ across its phase diagram, without the need to introduce a coarse-grained description of the ferroelectric transition. Furthermore, we apply the polarization model to calculate dielectric response properties of the material in a fully ab-initio manner.
This archive provides all the relevant data and input files that were used to fit the ML interatomic potential and the polarization model used in this work, along with the relevant Density-Functional Theory calculations that were used for the training set construction and the validation of the ML model. Furthermore, it provides input files and first few snapshots of all the molecular dynamics trajectories needed to investigate the thermodynamics and dielectric properties of BaTiO₃.

en: Thermodynamics and dielectric response of BaTiO₃ by data-driven modeling

dielectric-response

display_name: dielectric-response

ferroelectricity

display_name: ferroelectricity

max

display_name: max

samsung-institute-of-technology-sait

display_name: samsung-institute-of-technology-sait

swissuniversities

display_name: swissuniversities

thermodynamics

display_name: thermodynamics


en: Metal halide perovskites (MHPs) demonstrate an exceptional combination of properties. Rapid progress has extended their application beyond solar cells, light-emitting diodes, photodetectors, and lasers to include memristors, artificial synapse devices, and pressure induced emission. In particular, the vacancy-ordered double perovskite Cs₂TiBr₆ has been identified as a promising material. The effective characterization of MHPs requires accurate and efficient methods for the calculation of electronic structure. Koopmans compliant (KC) functionals are an accurate and computationally efficient alternative to many-body perturbation theory using the GW approximation but have yet only been validated on a small number of simple materials. In this work, KC functionals were applied to the more complex case of Cs₂TiBr₆ and gave a zero-temperature fundamental gap of 4.28 eV, in close agreement with the value of 4.44 eV obtained using the accurate, but more computationally expensive, evGW₀ approach. The temperature-dependent renormalization of the bandgap has also been investigated and found to be significant. Agreement with the experimental optical bandgaps of 1.76–2.0 eV would also require the inclusion of exciton binding energy.

en: Accurate and efficient computation of the fundamental bandgap of the vacancy-ordered double perovskite Cs₂TiBr₆

koopmans-functionals

display_name: koopmans-functionals

metal-halide-perovskites

display_name: metal-halide-perovskites

quasi-particle-self-consistent-gw

display_name: quasi-particle-self-consistent-gw


en: The Fe(CoB)/MgO interface is vital to spintronics as it exhibits the tunneling magnetoresistance (TMR) effect and interfacial perpendicular magnetic anisotropy (PMA) simultaneously. To further enhance TMR and PMA for the development of high-density magnetoresistive random access memory, it is essential to clarify the behavior of Fe atoms interfaced with MgO. This study reveals that the spin and orbital magnetic moments of Fe are enhanced at the Fe/MgO interface. The enhancement in the orbital magnetic moment is much more significant than that predicted by the standard density functional theory. Theoretical calculations based on the orbital polarization correction reproduce this enhancement, the origin of which is attributed to the electron-electron correlation resulting from electron localization at the Fe/MgO interface. The present findings highlight the importance of electro--electron correlation at ferromagnet/oxide interfaces, which has often been disregarded in spintronics, and provide a new perspective in materials design.

en: Electron correlation enhances orbital polarization at a ferromagnetic metal/insulator interface

experimental

display_name: experimental

femgo

display_name: femgo

interface

display_name: interface

magnetic-anisotropy

display_name: magnetic-anisotropy

spintronics

display_name: spintronics


en: With the recent advances in machine learning for quantum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules. Existing generative models mostly use a string- or graph-based representation, but the precise three-dimensional coordinates of the atoms are usually not encoded. First attempts in this direction have been proposed, where autoregressive or GAN-based models generate atom coordinates. Those either lack a latent space in the autoregressive setting, such that a smooth exploration of the compound space is not possible, or cannot generalize to varying chemical compositions. We propose a new approach to efficiently generate molecular structures that are not restricted to a fixed size or composition. Our model is based on the variational autoencoder which learns a translation-, rotation-, and permutation-invariant low-dimensional representation of molecules. Our experiments yield a mean reconstruction error below 0.05 Angstrom, outperforming the current state-of-the-art methods by a factor of four, and which is even lower than the spatial quantization error of most chemical descriptors. The compositional and structural validity of newly generated molecules has been confirmed by quantum chemical methods in a set of experiments.

materialscloud

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

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

en: Materials Cloud

en: 3DMolNet: a generative network for molecular structures

generative-model

display_name: generative-model

molecules-discovery

display_name: molecules-discovery

snsf

display_name: snsf

three-dimensional-structures

display_name: three-dimensional-structures


en: The precise introduction of nonplanar pores in the backbone of graphene nanoribbon represents a great challenge. In a recent work, we explore a synthetic strategy toward the preparation of nonplanar porous graphene nanoribbon from a predesigned dibromohexabenzotetracene monomer bearing four cove-edges. Successive thermal annealing steps of the monomers indicate that the dehalogenative aryl-aryl homocoupling yields a twisted polymer precursor on a gold surface and the subsequent cyclodehydrogenation leads to a defective porous graphene nanoribbon containing nonplanar [14]annulene pores and five-membered rings as characterized by scanning tunneling microscopy and noncontact atomic force microscopy. Although the C–C bonds producing [14]annulene pores are not achieved with high yield, our results provide new synthetic perspectives for the on-surface growth of nonplanar porous graphene nanoribbons. The record contains data to support the results of our work

en: On-surface synthesis of porous graphene nanoribbons containing nonplanar [14]annulene pores

envi

en: Environment

dft

display_name: dft

graphene-nanoribbons

display_name: graphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis

stm

display_name: stm


en: We present an ab initio modeling framework to simulate Majorana transport in 2D semiconducting materials, paving the way for topological qubits based on 2D nanoribbons. By combining density-functional-theory and quantum transport calculations, we show that the signature of Majorana bound states (MBSs) can be found in 2D material systems as zero-energy modes with peaks in the local density-of-states. The influence of spin-orbit coupling and external magnetic fields on Majorana transport is studied for two relevant 2D materials, WSe2 and PbI2. To illustrate the capabilities of the proposed ab initio platform, a device structure capable of hosting MBSs is created from a PbI2 nanoribbon and carefully investigated. These results are compared to InSb nanowires and used to provide design guidelines for 2D topological qubits.

en: Ab initio modeling framework for Majorana transport in 2D materials: towards topological quantum computing

ab-initio

display_name: ab-initio

majorana

display_name: majorana

quantum-transport

display_name: quantum-transport


en: Electrochemical reduction of CO(2) to value-added chemicals and fuels is a promising strategy to sustain pressing renewable energy demands and address climate change issues. Direct observation of reaction intermediates during the CO(2) reduction reaction will contribute to mechanistic understandings and thus promote the design of catalysts with the desired activity, selectivity, and stability. Herein, we combined in situ electrochemical shell-isolated nanoparticle-enhanced Raman spectroscopy and ab initio molecular dynamics calculations to investigate the CORR process on Cu single-crystal surfaces in various electrolytes. Competing redox pathways and coexistent intermediates of CO adsorption dimerization, oxidation, and hydrogenation, as well as Cu-Oad/Cu-OHad species at Cu-electrolyte interfaces, were simultaneously identified using in situ spectroscopy and further confirmed with isotope-labeling experiments. With AIMD simulations, we report accurate vibrational frequency assignments of these intermediates based on the calculated vibrational density of states and reveal the corresponding species in the electrochemical CO redox landscape on Cu surfaces. Our findings provide direct insights into key intermediates during the reduction of CO(2) and offer a full-spectroscopic tool (40–4,000 cm⁻¹) for future mechanistic studies.

en: In situ spectroelectrochemical probing of CO redox landscape on copper single-crystal surfaces

aimd

display_name: aimd

electrocatalysis

display_name: electrocatalysis

in-situ-raman

display_name: in-situ-raman


en: The prediction of material properties through electronic-structure simulations based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods aiming to solve similar problems is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use any one for a given task. Leveraging recent advances in managing reproducible scientific workflows, we demonstrate how developing common interfaces for workflows that automatically compute material properties can tackle the challenge mentioned above, greatly simplifying interoperability and cross-verification. We introduce design rules for reproducible and reusable code-agnostic workflow interfaces to compute well-defined material properties, which we implement for eleven different quantum engines and use to compute three different material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer's expertise of the quantum engine directly available to non-experts. Full provenance and reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure. All workflows are made available as open-source and come pre-installed with the Quantum Mobile virtual machine, making their use straightforward. This entry contains all data and scripts to reproduce the figures of the corresponding scientific paper.

en: Common workflows for computing material properties using different quantum engines

aiida

display_name: aiida

automated

display_name: automated

common-interfaces

display_name: common-interfaces

intersect

display_name: intersect

marvelosp

display_name: marvelosp

max

display_name: max

verification

display_name: verification

workflows

display_name: workflows


en: Charge neutrality and their expected itinerant nature makes excitons potential transmitters of information. However, exciton mobility remains inaccessible to traditional optical experiments that only create and detect excitons with negligible momentum. Here, using angle-resolved photoemission spectroscopy, we detect dispersing excitons in quasi-one-dimensional metallic trichalcogenide, TaSe₃. The low density of conduction electrons and low dimensionality in TaSe₃ combined with a polaronic renormalization of the conduction band and the poorly screened interaction between these polarons and photo-induced valence holes leads to various excitonic bound states that we interpret as intrachain and interchain excitons, and possibly trions. The thresholds for the formation of a photo-hole together with an exciton appear as side valence bands with dispersions nearly parallel to the main valence band, but shifted to lower excitation energies. The energy separation between side and main valence bands can be controlled by surface doping, enabling the tuning of certain exciton properties. This document contains the raw data of this project.

en: Multiple mobile excitons manifested as sidebands in quasi-one-dimensional metallic TaSe₃

arpes

display_name: arpes

dispersive-bands-from-excitons

display_name: dispersive-bands-from-excitons

interchain-excitons-and-trions

display_name: interchain-excitons-and-trions

mobile-excitons-in-metal

display_name: mobile-excitons-in-metal


en: The design of open-shell carbon-based nanomaterials is at the vanguard of materials science, steered by their beneficial magnetic properties like weaker spin–orbit coupling than that of transition metal atoms and larger spin delocalization, which are of potential relevance for future spintronics and quantum technologies. A key parameter in magnetic materials is the magnetic exchange coupling (MEC) between unpaired spins, which should be large enough to allow device operation at practical temperatures. In a recent work, we theoretically and experimentally explore three distinct families of nanographenes (NGs) (A, B, and C) featuring majority zigzag peripheries. Through many-body calculations, we identify a transition from a closed-shell ground state to an open-shell ground state upon an increase of the molecular size. Our predictions indicate that the largest MEC for open-shell NGs occurs in proximity to the transition between closed-shell and open-shell states. Such predictions are corroborated by the on-surface syntheses and structural, electronic, and magnetic characterizations of three NGs (A[3,5], B[4,5], and C[4,3]), which are the smallest open-shell systems in their respective chemical families and are thus located the closest to the transition boundary. Notably, two of the NGs (B[4,5] and C[4,3]) feature record values of MEC (close to 200 meV) measured on the Au(111) surface. Our strategy for maximizing the MEC provides perspectives for designing carbon nanomaterials with robust magnetic ground states. This record contains the data for the simulations discussed in our manuscript.

en: Steering large magnetic exchange coupling in nanographenes near the closed-shell to open-shell transition

dft

display_name: dft

nanographenes

display_name: nanographenes

open-shell

display_name: open-shell

snsf

display_name: snsf

spectroscopy

display_name: spectroscopy


en: Landau-Lifshitz-Gilbert (LLG) spin-dynamics calculations based on the extended Heisenberg Hamiltonian is an important tool in computational materials science involving magnetic materials. LLG simulations allow to bridge the gap from expensive quantum mechanical calculations with small unit cells to large supercells where the collective behavior of millions of spins can be studied. In this work we present the AiiDA-Spirit plugin that connects the spin-dynamics code Spirit to the AiiDA framework. AiiDA provides a Python interface that facilitates performing high-throughput calculations while automatically augmenting the calculations with metadata describing the data provenance between calculations in a directed acyclic graph. The AiiDA-Spirit interface thus provides an easy way for high-throughput spin-dynamics calculations. The interface to the AiiDA infrastructure furthermore has the advantage that input parameters for the extended Heisenberg model can be extracted from high-throughput first-principles calculations including a proper treatment of the data provenance that ensures reproducibility of the calculation results in accordance to the FAIR principles. We describe the layout of the AiiDA-Spirit plugin and demonstrate its capabilities using selected examples for LLG spin-dynamics and Monte Carlo calculations. Furthermore, the integration with first-principles calculations through AiiDA is demonstrated at the example of gamma-Fe, where the complex spin-spiral ground state is investigated.

en: The AiiDA-Spirit plugin for automated spin-dynamics simulations and multi-scale modelling based on first-principles calculations

density-functional-theory

display_name: density-functional-theory

landau-lifshitz-gilbert-equation

display_name: landau-lifshitz-gilbert-equation

llg

display_name: llg

monte-carlo

display_name: monte-carlo

skyrmion

display_name: skyrmion

spin-dynamics

display_name: spin-dynamics


en:
A semi-automated protocol largely based on AiiDA has been developed and exploited to explore the yet-unsolved crystal structure of the recently-discovered AsTe₃ material with potential thermoelectric applications due to its ultralow thermal conductivity. The workflow involves different steps of increasing precision and computational cost, in a sequential approach to generate, refine and evaluate model AsTe₃ structures based on supercells of elemental-Te from the stand point of As/Te chemical disorder. Our studies shed light on the structural complexity that has prevented, to this day, a direct structure determination of the recently-discovered crystalline AsTe₃ material, despite the new synthesis protocol that has led to its obtention in a pure and fully crystalline phase, by congruent crystallization from the parent AsTe₃ glass. Structural models of crystalline AsTe₃ reveal a composition based on an intergrowth of subnanometric As₂Te₃ and Te domains with potentially defected interfaces that intimately interfere with the electronic properties of this material.
This repository contains data generated in the course of the structure-construction-and-evaluation protocol for subsets of AsTe₃ structural models obtained at different steps of the workflow, with the full provenance included, in the form of an AiiDA archive. Scripts and workchains developped specifically for this study are also privided, along with the atomic structures of the most relevant AsTe₃ models identified, in common file formats for researchers not familiar with AiiDA databases.

materialscloud

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

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

en: Materials Cloud

en: Insights into the structural complexity and local disorder of crystalline AsTe₃ from semi-automated first-principles modelling

automation

display_name: automation

chemical-disorder

display_name: chemical-disorder

structural-complexity

display_name: structural-complexity

structural-models

display_name: structural-models

thermoelectrics

display_name: thermoelectrics


en: Singlet fission (SF) is a promising multiexciton-generating process. Its demanding energy splitting criterion - that the S1 energy must be at least twice that of T1 - has limited the range of materials capable of SF. We propose heteroatom oxidation as a robust strategy to achieve sufficient S1/T1 splitting, and demonstrate the potential of this approach for intramolecular SF.

en: Heteroatom oxidation controls singlet-triplet energy splitting in singlet fission building blocks

envi

en: Environment

conjugated-polymers

display_name: conjugated-polymers

donor-acceptor-systems

display_name: donor-acceptor-systems

epfl

display_name: epfl

excited-states

display_name: excited-states

singlet-fission

display_name: singlet-fission


en: The efficient activation of methane and simultaneous water dissociation are crucial in many catalytic reactions on oxide-supported transition metal catalysts. On very low-loaded Ni/CeO₂ surfaces, methane easily fully decomposes, CH₄ -> C + 4H,  and water dissociates, H₂O -> OH + H. However, in important reactions such as the direct oxidation of methane to methanol (MTM), where complex interplay exists between reactants (CH₄, O₂), it is desirable to avoid the complete dehydrogenation of methane to carbon.  Remarkably, the barrier for the activation of C-H bonds in CHx (x= 1-3) species on Ni/CeO₂ surfaces can be manipulated by adding Cu, forming bimetallic NiCu clusters, whereas the ease for cleavage of O-H bonds in water, is not affected by ensemble effects, as obtained from density functional theory-based calculations. CH4 activation occurs only on Ni sites and H₂O activation on both Ni and Cu sites. The MTM reaction pathway for the example of the Ni₃Cu₁/CeO₂ model catalyst predict higher selectivity and a lower activation barrier for methanol production, compared with that for Ni₄-CeO₂. These findings point toward a possible strategy to design active and stable catalysts which can be employed for methane activation and conversions.

en: Tuning selectivity in the direct conversion of methane to methanol: bimetallic synergistic effects on the cleavage of C-H and O-H bonds over NiCu/CeO₂ catalysts

bimetallic

display_name: bimetallic

ceria

display_name: ceria

methane-activation

display_name: methane-activation

water-activation

display_name: water-activation


en: The properties of molecules and materials containing light nuclei are affected by their quantum mechanical nature. Accurate modeling of these quantum nuclear effects requires computationally demanding path integral techniques. Considerable success has been achieved in reducing the cost of such simulations by using generalized Langevin dynamics to induce frequency-dependent fluctuations. Path integral generalized Langevin equation methods, however, have this far been limited to the study of static, thermodynamic properties due to the large perturbation to the system's dynamics induced by the aggressive thermostatting. Here, we introduce a post-processing scheme, based on analytical estimates of the dynamical perturbation induced by the generalized Langevin dynamics, which makes it possible to recover meaningful time correlation properties from a thermostatted trajectory. We show that this approach yields spectroscopic observables for model and realistic systems that have an accuracy comparable to much more demanding approximate quantum dynamics techniques based on full path integral simulations.

en: Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats

erc

display_name: erc

marvel

display_name: marvel

molecular-dynamics

display_name: molecular-dynamics

nuclear-quantum-effects

display_name: nuclear-quantum-effects

vibrational-spectra

display_name: vibrational-spectra


en:
Why are materials with specific characteristics more abundant than others?
This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space. We highlight here the anomalous abundance of  inorganic compounds whose primitive unit cell contains a number of atoms that is a multiple of four. This occurrence - named here the 'rule of four' - has to our knowledge not previously been reported or studied. Here, we first highlight the rule's existence, especially notable when restricting oneself to experimentally known compounds, and explore its possible relationship with established descriptors of  crystal structures, from symmetries to energies. We then investigate this relative abundance by looking at structural descriptors, both of global (packing configurations) and local (the smooth overlap of atomic positions) nature. Contrary to intuition, the overabundance does not correlate with low-energy or high-symmetry structures; in fact, structures which obey the 'rule of four' are characterized by low symmetries and loosely packed arrangements maximizing the free volume. We are able to correlate this abundance with local structural symmetries, and visualize the results using a hybrid supervised-unsupervised machine learning method.

en: The rule of four: anomalous stoichiometries of inorganic compounds

classification

display_name: classification

inorganic-databases

display_name: inorganic-databases

local-symmetries

display_name: local-symmetries

machine-learning

display_name: machine-learning

soap-vectors

display_name: soap-vectors


en: We present a self-consistent method based on first-principles calculations to determine the magnetic ground state of materials, regardless of their dimensionality. Our methodology is founded on satisfying the stability conditions derived from the linear spin wave theory (LSWT) by optimizing the magnetic structure iteratively. We demonstrate the effectiveness of our method by successfully predicting the experimental magnetic structures of NiO, FePS₃, FeP, MnF₂, FeCl₂, and CuO. In each case, we compared our results with available experimental data and existing theoretical calculations reported in the literature. Finally, we discuss the validity of the method and the possible extensions.

en: Systematic determination of a material's magnetic ground state from first principles

dftu

display_name: dftu

linear-spin-wave-theory

display_name: linear-spin-wave-theory

magnetic-structure

display_name: magnetic-structure


en: Polyelectrolyte complexes (PECs), formed via the self-assembly of oppositely charged polysaccharides, are highly valued for their biocompatibility, biodegradability, and hydrophilicity, offering significant potential for biotechnological applications. However, the complex nature and lack of insight at a molecular level into polyelectrolytes conformation and aggregation often hinders the possibility of achieving an optimal control of PEC systems, limiting their practical applications. To address this problem, an in-depth investigation of PECs microscopic structural organization is required. In this work, for the first time, a hybrid approach that combines experimental techniques with atomistic molecular dynamics simulations is used to elucidate, at a molecular level, the mechanisms underlying the aggregation and structural organization of complexes formed by gellan and chitosan, i.e. PECs commonly used in food technology. This combined analysis reveals a two-step complexation process: gellan initially self-assembles into a double-helix structure, subsequently surrounded and stabilized by chitosan via electrostatic interactions. Furthermore, these results show that complexation preserves the individual conformation and intrinsic functionality of both polyelectrolytes, thereby ensuring the efficacy of the PECs in biotechnological applications.

en: Unveiling the self-assembly process of gellan-chitosan complexes through a combination of atomistic simulations and experiments

chitosan

display_name: chitosan

gellan

display_name: gellan

gels

display_name: gels

polyelectrolyte-complexes

display_name: polyelectrolyte-complexes

polysaccharides

display_name: polysaccharides


en: Doping and compositing are two universal design strategies used to engineer the electronic state of a material and mitigate its disadvantages. These two strategies have been extensively applied to the design of efficient electrocatalysts for water splitting. Using cobalt oxide (CoO) as a model catalyst, we prove that the oxygen evolution reaction (OER) performance could be progressively improved, first by Fe-doping to form Fe-CoO solid solution, and further by the addition of CeO2 to produce a Fe-CoO/CeO2 composite. X-ray adsorption spectroscopy (XAS) reveals that distinct electronic interactions are induced by the processes of doping and compositing. Fe-doping of CoO can break down the structural symmetry in the pristine material, changing the electronic structure of both Co and O species at the surface and decreasing the flat-band potential (Vfb). In comparison, subsequent compositing of Fe-CoO with CeO2 induces negligible electronic changes in the as-synthesized Fe-CoO (as seen in ex-situ characterizations), but significantly modifies the oxidative transformations of both Co and Fe under OER conditions. Our spectroscopic investigations reveal that Fe-doping and CeO2 compositing play different roles in modifying the electronic properties of CoO in its pristine state and during OER catalysis, respectively, in return, providing useful guidance for the design of more efficient electrocatalysts using these two strategies.

en: Spectroscopic investigations of complex electronic interactions by elemental doping and material compositing of cobalt oxide for enhanced oxygen evolution reaction activity

compositing

display_name: compositing

doping

display_name: doping

electronic-interaction

display_name: electronic-interaction

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

x-ray-adsorption-spectroscopy

display_name: x-ray-adsorption-spectroscopy


en: This database contains phosphorus and sulfur K-edge X-ray absorption near-edge structure (XANES) of delithiated Lithium-Phosphorus-Sulfur compounds. The structures were generated by a computational delithiation procedure of β-Li3PS4 with density functional theory calculations, where we systematically enumerated distinct lithium/vacancy orderings in the super cells with the general composition Li12-xP4S16 (i.e., four Li3PS4 formula units and x from 0 to 12). The database contains a total of 2227 P K-edge and 8885 S K-edge XANES spectra of symmetrically inequivalent absorbing P and S sites. The XANES spectra were calculated using the excited electron and core hole method as implemented in The Vienna Ab initio Simulation Package (VASP) 6.2.1. Details of the structure generation procedure as well as the VASP simulations can be found in the associated manuscript (see reference below).

materialscloud

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

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

en: Materials Cloud

en: Atomic insights into the oxidative degradation mechanisms of sulfide solid electrolytes

lithium-ion-batteries

display_name: lithium-ion-batteries

lithium-phosphorus-sulfide

display_name: lithium-phosphorus-sulfide

solid-electrolyte

display_name: solid-electrolyte

vasp

display_name: vasp

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


en:
Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by weak binding energy and, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors.
To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.

en: Machine-learning accelerated identification of exfoliable two-dimensional materials

envi

en: Environment

binding-energy

display_name: binding-energy

crystal-structure

display_name: crystal-structure

exfoliation

display_name: exfoliation

online-tool

display_name: online-tool

two-dimensional

display_name: two-dimensional


en: Raman spectra play an important role in characterizing two-dimensional materials, as they provide a direct link between the atomic structure and the spectral features. In this work, we present an  automatic computational workflow for Raman spectra using all-electron density functional perturbation theory. Utilizing this workflow, we have successfully completed the Raman spectra calculation for 3504 different two-dimensional materials, with the resultant data saved in a data repository.

en: High-throughput computation of ab initio Raman spectra for two-dimensional materials

fhi-aims

display_name: fhi-aims

raman-spectra

display_name: raman-spectra

two-dimensional-material

display_name: two-dimensional-material


en: Ab initio random structure searching (AIRSS) has been performed for carbon at 10GPa, a pressure at which diamond is expected to be the thermodynamic ground state. A small (~10K structures) and larger (~100K structures) set are provided. To demonstrate a search in a ternary composition space ~100K AIRSS structures for the C+N+H+O system are provided. All structures are generated at random using the buildcell tool of the AIRSS suite, and relaxed to a nearby local minimum in the DFT-PBE total energy using the CASTEP code. Computational details are provided in the REM block of the .res structure files.

en: AIRSS data for carbon at 10GPa and the C+N+H+O system at 1GPa

ab-initio-random-structure-searching

display_name: ab-initio-random-structure-searching

carbon

display_name: carbon

dft

display_name: dft


en:
In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to materials science. While most current MLPs are based on environment-dependent atomic energies, the limitations of this locality approximation can be overcome, e.g., in fourth-generation MLPs, which incorporate long-range electrostatic interactions based on an equilibrated global charge distribution. Apart from the considered interactions,  the quality of MLPs crucially depends on the information available about the system, i.e., the descriptors.
In this work we show that including — in addition to structural information — the electrostatic potential arising from the charge distribution in the atomic environments significantly improves the quality and transferability of the potentials. Moreover, the extended descriptor allows to overcome current limitations of two- and three-body based feature vectors regarding artificially degenerate atomic environments.
The capabilities of such an electrostatically embedded fourth-generation high-dimensional neural network potential (ee4G-HDNNP), which is further augmented by pairwise interactions, are demonstrated for NaCl as a benchmark system. Employing a data set containing only neutral and negatively charged NaCl clusters, even small energy differences between different cluster geometries can be resolved, and the potential shows an impressive transferability to positively charged clusters as well as the melt.

en: Accurate fourth-generation machine learning potentials by electrostatic embedding

electrostatics

display_name: electrostatics

machine-learning-potentials

display_name: machine-learning-potentials


en: We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on the known material Cr2Ge2Te6, using density functional theory (DFT) calculations and determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability.

en: Data-driven studies of magnetic two-dimensional materials

machine-learning

display_name: machine-learning

magnetic-materials

display_name: magnetic-materials

two-dimensional-materials

display_name: two-dimensional-materials


en: The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system's modes that most slowly approach equilibrium. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on-the-fly probability enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a mini-protein and the study of materials crystallization.

en: Deep learning the slow modes for rare events sampling

collective-variables

display_name: collective-variables

enhanced-sampling

display_name: enhanced-sampling

molecular-dynamics

display_name: molecular-dynamics

protein-folding

display_name: protein-folding

silicon-crystallization

display_name: silicon-crystallization


en: Being Wannierizable is not the end of the story for topological insulators. We introduce a family of topological insulators that would be considered trivial in the paradigm set by the tenfold way, topological quantum chemistry, and the method of symmetry-based indicators. Despite having a symmetric, exponentially localized Wannier representation, each Wannier function cannot be completely localized to a single primitive unit cell in the bulk. Such multicellular topology is shown to be neither stable nor fragile, but delicate; i.e., the topology can be nullified by adding trivial bands to either valence or conduction band.

en: Multicellularity of delicate topological insulators

delicate-topology

display_name: delicate-topology

erc

display_name: erc

hopf-insulator

display_name: hopf-insulator

multicellular-wannier-functions

display_name: multicellular-wannier-functions

returning-thouless-pump

display_name: returning-thouless-pump

snsf

display_name: snsf

topological-insulators

display_name: topological-insulators


en: Photochemical reactions of contaminants in snow and ice can be important sources and sinks for various organic and inorganic compounds. Snow contaminants can be found in the bulk ice matrix, in internal liquid-like regions (LLRs), or in quasi-liquid layers (QLLs) at the air-ice interface, where they can readily exchange with the firn air. Some studies have reported that direct photochemical reactions occur faster in LLRs and QLLs than in aqueous solution, while others have found similar rates. Here, we measure the photodegradation rate constants of the three dimethoxybenzene isomers under varying experimental conditions, including in aqueous solution, in LLRs, and at the air-ice interface of nature-identical snow. Relative to aqueous solution, we find modest photodegradation enhancements (3- and 6-fold) in LLRs for two of the isomers, and larger enhancements (15- to 30-fold) at the air-ice interface for all three isomers. We use computational modeling to assess the impact of light absorbance changes on photodegradation rate enhancements at the interface. We find small (2–5 nm) bathochromic (red) absorbance shifts at the interface relative to in solution, which increases light absorption, but this factor only accounts for less than 50 % of the measured rate constant enhancements. The major factor responsible for photodegradation rate enhancements at the air-ice interface appears to be more efficient photodecay: estimated dimethoxybenzene quantum yields are 6- to 24-fold larger at the interface compared to in aqueous solution and account for the majority (51–96 %) of the observed enhancements. Using a hypothetical model compound with an assumed Gaussian-shaped absorbance peak, we find that a shift in the peak to higher or lower wavelengths can have a minor to substantial impact on photodecay rate constants, depending on the original location of the peak and the magnitude of the shift. Changes in other peak properties at the air-ice interface, such as peak width and height (i.e., molar absorptivity) can also impact rates of light absorption and direct photodecay.

materialscloud

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

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

en: Materials Cloud

en: Enhanced photodegradation of dimethoxybenzene isomers in/on ice compared to in aqueous solution

absorption-spectra

display_name: absorption-spectra

dimethoxybenzene

display_name: dimethoxybenzene

ice

display_name: ice


en: We propose Landau levels as a probe for the topological character of electronic bands in two-dimensional moiré superlattices. We consider two configurations of twisted double bilayer graphene (TDBG) that have very similar band structures, but show different valley Chern numbers of the flat bands. These differences between the AB-AB and AB-BA configurations of TDBG clearly manifest as different Landau level sequences in the Hofstadter butterfly spectra calculated using the tight-binding model. The Landau level sequences are explained from the point of view of the distribution of orbital magnetization in momentum space that is governed by the rotational C2 and time-reversal T symmetries. Our results can be readily extended to other twisted graphene multilayers and h-BN/graphene heterostructures thus establishing the Hofstadter butterfly spectra as a powerful tool for detecting the nontrivial valley band topology.

en: Landau levels as a probe for band topology in graphene moiré superlattices

envi

en: Environment

band-topology

display_name: band-topology

landau-level

display_name: landau-level

moire-superlattice

display_name: moire-superlattice

twisted-double-bilayer-graphene

display_name: twisted-double-bilayer-graphene


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

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

database

display_name: database

dft

display_name: dft

electronic-bands

display_name: electronic-bands

electronic-properties

display_name: electronic-properties

high-throughput

display_name: high-throughput

magnetic-properties

display_name: magnetic-properties

marvel

display_name: marvel

monolayer

display_name: monolayer

phonons

display_name: phonons

two-dimensional

display_name: two-dimensional

vibrational-properties

display_name: vibrational-properties