en: We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a combinatorial engine' that constructs potential compounds by occupying all possible Wyckoff positions for a certain space group with  combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for electronegativities. The structures are then pre-optimized with a specially crafted universal neural-network force-field, before a final step of geometry optimization using density-functional theory is performed. In this way we unveil an unprecedented variety of two-dimensional materials, covering the whole periodic table in more than 30 different stoichiometries of form AnBm or AnBmCk. Among the found structures we find examples that can be built by decorating nearly all Platonic and Archimedean tesselations as well as their dual Laves or Catalan tilings. We also obtain a rich, and unexpected, polymorphism for some specific compounds. We further accelerate the exploration of the chemical space of two-dimensional materials by employing machine-learning-accelerated prototype search, based on the structural types discovered in the exhaustive search. In total, we obtain around 6500 compounds, not present in previous available databases of 2D materials, with an energy of less than 250 meV/atom above the convex hull of thermodynamic stability.

en: Symmetry-based computational search for novel binary and ternary 2D materials

crystal-graph-neural-networks

display_name: crystal-graph-neural-networks

high-throughput

display_name: high-throughput


$oid: OBSOLETE

en: Defect chemistry, strain, and structural, magnetic and electronic degrees of freedom constitute a rich space for the design of functional properties in transition metal oxides. Here, we show that it is possible to engineer polarity and ferroelectricity in non-polar perovskite oxides via polar defect pairs formed by anion vacancies coupled to substitutional cations. We use a self-consistent site-dependent DFT+U approach that accounts for local structural and chemical changes upon defect creation and which is crucial to reconcile predictions with the available experimental data. Our results for Fe-doped oxygen-deficient SrMnO3 show that substitutional Fe and oxygen vacancies can promote polarity due to an o -center displacement of the defect charge resulting in a net electric dipole moment, which polarizes the lattice in the defect neighborhood. The formation of these defects and the resulting polarization can be tuned by epitaxial strain, resulting in enhanced polarization also for strain values lower than the ones necessary to induce a polar phase transition in undoped SrMnO3. For high enough defect concentrations, these defect dipoles couple in a parallel fashion, thus enabling defect- and strain-based engineering of ferroelectricity in SrMnO3.

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: Interplay between polarization, strain and defect-pairs in Fe-doped  SrMnO3-δ

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

cscs

display_name: cscs

defect-pair

display_name: defect-pair

doping

display_name: doping

ferroelectricity

display_name: ferroelectricity

point-defect

display_name: point-defect

polarization

display_name: polarization

self-consistent-dftu

display_name: self-consistent-dftu

snsf

display_name: snsf

strain

display_name: strain

thin-film

display_name: thin-film


en: Organic photodetectors have promising applications in low-cost imaging, health monitoring and near infrared sensing. Recent research on organic photodetectors based on donor-acceptor systems has resulted in narrow-band, flexible and biocompatible devices, of which the best reach external photovoltaic quantum efficiencies approaching 100%. However, the high noise spectral density of these devices limits their specific detectivity to around 10^13 Jones in the visible and several orders of magnitude lower in the near-infrared, severely reducing performance. Here, we show that the shot noise, proportional to the dark current, dominates the noise spectral density, demanding a comprehensive understanding of the dark current. We demonstrate that, in addition to the intrinsic saturation current generated via charge-transfer states, dark current contains a major contribution from trap-assisted generated charges and decreases systematically with decreasing concentration of traps. By modeling the dark current of several donor-acceptor systems, we reveal the interplay between traps and charge-transfer states as source of dark current and show that traps dominate the generation processes, thus being the main limiting factor of organic photodetectors detectivity.

en: Reverse dark current in organic photodetectors and the major role of traps as source of noise

dark-current

display_name: dark-current

detectivity

display_name: detectivity

organic-photodetectors

display_name: organic-photodetectors

traps

display_name: traps


en: Graph neural networks have enjoyed great success in the prediction of material properties for both molecules and crystals. These networks typically use the atomic positions (usually expanded in a Gaussian basis) and the atomic species as input. Unfortunately, this information is in general not available when predicting new materials, for which the precise geometrical information is unknown. In this work, we circumvent this problem by predicting the thermodynamic stability of crystal structures without using the knowledge of the precise bond distances. We replace this information with embeddings of graph distances, allowing our networks to be used directly in high-throughput studies based on both composition and crystal structure prototype. Using these embeddings, we combine the newest developments in graph neural networks and apply them to the prediction of the distances to the convex hull. To train these networks, we curate a dataset of over 2 million density-functional calculations of crystals with consistent calculation parameters from various sources. The new dataset allows for the creation of a high quality convex hull and a large scale transfer learning approach. We apply the resulting model to the high-throughput search of 15 million tetragonal perovskites of composition ABCD2. As a result, we identify several thousand potentially stable compounds and demonstrate that transfer learning from the newly curated dataset reduces the required training data by 50%.

en: Crystal-graph attention networks for the prediction of stable materials

density-functional-theory

display_name: density-functional-theory

pbe

display_name: pbe


en:
Reticular chemistry provides materials designers with a practically infinite playground on different length scales. However, the space of all plausible materials for a given application is so large that it cannot be explored using a brute-force approach. One promising approach to guide the design and discovery of materials is machine learning, which typically involves learning a mapping of structures onto properties from data.
To advance the data-driven materials discovery of metal-organic frameworks (MOFs) for gas storage and separation applications we provide a dataset of diverse gas separation properties (CO2, CH4, H2, N2, O2 isotherms); H2S, H2O, Kr, Xe Henry coefficients (computed using grand canonical Monte-Carlo with classical force fields) as well as parasitic energy for carbon capture from natural gas and a coal-fired power plant (computed using a simple process model) for the relaxed structures in the QMOF dataset with their DDEC charges.

en: Gas adsorption and process performance data for MOFs

aiida

display_name: aiida

erc

display_name: erc

marvel

display_name: marvel

mof

display_name: mof

nanoporous

display_name: nanoporous


en: The formation of large polarons has been proposed as reason for the high defect tolerance, low mobility, low charge carrier trapping and low nonradiative recombination rates of lead halide perovskites. Recently, direct evidence for large-polaron formation has been reported from a 50% effective mass enhancement in angle-resolved photoemission of CsPbBr₃ over theory for the orthorhombic structure. We present in-depth band dispersion measurements of CsPbBr₃ and GW calculations which lead to almost identical effective masses at the valence band maximum of 0.203+/-0.016 m₀ in experiment and 0.217 m₀ in orthorhombic theory. We argue that the effective mass can be explained solely on the basis of electron-electron correlation and large polaron formation cannot be concluded from photoemission data.

en: Is there a polaron signature in angle-resolved photoemission of CsPbBr₃?

large-polaron

display_name: large-polaron


en: We introduce HP, an implementation of density-functional perturbation theory, designed to compute Hubbard parameters (on-site U and inter-site V) in the framework of DFT+U and DFT+U+V. The code does not require the use of computationally expensive supercells of the traditional linear-response approach; instead, unit cells are used with monochromatic perturbations that significantly reduce the computational cost of determining Hubbard parameters. HP is an open-source software distributed under the terms of the GPL as a component of Quantum ESPRESSO. As with other components, HP is optimized to run on a variety of different platforms, from laptops to massively parallel architectures, using native mathematical libraries (LAPACK and FFTW) and a hierarchy of custom parallelization layers built on top of MPI. The effectiveness of the code is showcased by computing Hubbard parameters self-consistently for the phospho-olivine LixMn0.5Fe0.5PO4 (x=0, 0.5, 1) and by highlighting the accuracy of predictions of the geometry and Li intercalation voltages.

en: HP - A code for the calculation of Hubbard parameters using density-functional perturbation theory

cscs

display_name: cscs

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

hubbard-parameters

display_name: hubbard-parameters

linear-response-theory

display_name: linear-response-theory

marvelosp

display_name: marvelosp

open-science

display_name: open-science

open-source-software

display_name: open-source-software

quantum-espresso

display_name: quantum-espresso

self-interaction-corrections

display_name: self-interaction-corrections

transition-metal-compounds

display_name: transition-metal-compounds


en: Formation of base pairs between the nucleotides of a ribonucleic acid (RNA) sequence gives rise to a complex and often highly branched RNA structure. While numerous studies have demonstrated the functional importance of the high degree of RNA branching—for instance, for its spatial compactness or interaction with other biological macromolecules—RNA branching topology remains largely unexplored. Here, we use the theory of randomly branching polymers to explore the scaling properties of RNAs by mapping their secondary structures onto planar tree graphs. Focusing on random RNA sequences of varying lengths, we determine the two scaling exponents related to their topology of branching. Our results indicate that ensembles of RNA secondary structures are characterized by annealed random branching and scale similarly to self-avoiding trees in three dimensions. We further show that the obtained scaling exponents are robust upon changes in nucleotide composition, tree topology, and folding energy parameters. Finally, in order to apply the theory of branching polymers to biological RNAs, whose length cannot be arbitrarily varied, we demonstrate how both scaling exponents can be obtained from distributions of the related topological quantities of individual RNA molecules with fixed length. In this way, we establish a framework to study the branching properties of RNA and compare them to other known classes of branched polymers. By understanding the scaling properties of RNA related to its branching structure, we aim to improve our understanding of the underlying principles and open up the possibility to design RNA sequences with desired topological properties.

en: Scaling properties of RNA as a randomly branching polymer

polymer-physics

display_name: polymer-physics

rna

display_name: rna

scaling

display_name: scaling

statistical-mechanics

display_name: statistical-mechanics


en: In a unified regenerative fuel cell (URFC) or reversible fuel cell the oxygen bifunctional catalyst must switch reversibly between the oxygen reduction reaction (ORR), fuel cell mode, and the oxygen evolution reaction (OER), electrolyzer mode. However, it is often unclear what effect alternating between ORR and OER has on the electrochemical behavior and physiochemical properties of the catalyst. Herein, operando X-ray absorption spectroscopy (XAS) is utilized to monitor the continuous and dynamic evolution of the Co, Mn, and Fe oxidation states of perovskite catalysts Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF) and La0.4Sr0.6MnO3-δ (LSM), while the potential is oscillated between reducing and oxidizing potentials with cyclic voltammetry. The results reveal the importance of investigating bifunctional catalysts by alternating between fuel cell and electrolyzer operation and highlight the limitations and challenges of bifunctional catalysts. It is shown that the requirements for ORR and OER performance are divergent and that the oxidative potentials of OER are detrimental to ORR activity. These findings are used to give guidelines for future bifunctional catalyst design. Additionally, it is demonstrated how sunlight can be used to reactivate the ORR activity of LSM after rigorous cycling.

en: Time-resolved oxidation state changes are key to elucidating the bifunctionality of perovskite catalysts for oxygen evolution and reduction

alkaline

display_name: alkaline

experimental

display_name: experimental

oer

display_name: oer

orr

display_name: orr

snsf

display_name: snsf


en: Accurate computational predictions of band gaps are of practical importance to the modeling and development of semiconductor technologies, such as (opto)electronic devices and photoelectrochemical cells. Among available electronic-structure methods, density-functional theory (DFT) with the Hubbard U correction (DFT+U) applied to band edge states is a computationally tractable approach to improve the accuracy of band gap predictions beyond that of DFT calculations based on (semi)local functionals. At variance with DFT approximations, which are not intended to describe optical band gaps and other excited-state properties, DFT+U can be interpreted as an approximate spectral-potential method when U is determined by imposing the piecewise linearity of the total energy with respect to electronic occupations in the Hubbard manifold (thus removing self-interaction errors in this subspace), thereby providing a (heuristic) justification for using DFT+U to predict band gaps. However, it is still frequent in the literature to determine the Hubbard U parameters semiempirically by tuning their values to reproduce experimental band gaps, which ultimately alters the description of other total-energy characteristics. Here, we present an extensive assessment of DFT+U band gaps computed using self-consistent ab initio U parameters obtained from density-functional perturbation theory to impose the aforementioned piecewise linearity of the total energy. The study is carried out on 20 compounds containing transition-metal or p-block (group III-IV) elements, including oxides, nitrides, sulfides, oxynitrides, and oxysulfides. By comparing DFT+U results obtained using nonorthogonalized and orthogonalized atomic orbitals as Hubbard projectors, we find that the predicted band gaps are extremely sensitive to the type of projector functions and that the orthogonalized projectors give the most accurate band gaps, in satisfactory agreement with experimental data. This work demonstrates that DFT+U may serve as a useful method for high-throughput workflows that require reliable band gap predictions at moderate computational cost.

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: Extensive benchmarking of DFT+U calculations for predicting band gaps

band-gap

display_name: band-gap

dftu

display_name: dftu

hubbard-correction

display_name: hubbard-correction

lowdin-orthogonalization

display_name: lowdin-orthogonalization

self-interaction

display_name: self-interaction


en: The dataset contains the result of 48 Nudged Elastic Band calculations of Li(2-x)VO2F diffusion barriers in the format of Atomic Simulation Environment (ASE) trajectories. The NEB was performed with VASP, using projector augmented-wave (PAW) method to describe electron-ion interaction. The disordered rock salt cells were created using a 3 x 4 x 4 supercell containing 96 atoms (in case of no vacancies). PBE is used as XC functional while a rotationally invariant Hubbard U correction was applied to the d orbital of V with a U value of 3.25 eV. See more details in the paper.

en: On-the-fly assessment of diffusion barriers of disordered transition metal oxyfluorides using local descriptors

envi

en: Environment

big-map

display_name: big-map

fet-open

display_name: fet-open

lirichfcc

display_name: lirichfcc

lithium-diffusion

display_name: lithium-diffusion

machine-learning

display_name: machine-learning

nudged-elastic-band

display_name: nudged-elastic-band


en:
Strong correlations within a symmetry-unbroken ground-state wavefunction can show up in approximate density functional theory as symmetry-broken spin-densities or total densities, which are sometimes observable. They can arise from soft modes of fluctuations (sometimes collective excitations) such as spin-density or charge-density waves at non-zero wavevector. In this sense, an approximate density functional for exchange and correlation that breaks symmetry can be more revealing (albeit less accurate) than an exact functional that does not. The examples discussed here include the stretched H2 molecule, antiferromagnetic solids, and the static charge-density wave/Wigner crystal phase of a low-density jellium. Time-dependent density functional theory is used to show quantitatively that the static charge density wave is a soft plasmon. More precisely, the frequency of a related density fluctuation drops to zero, as found from the frequency moments of the spectral function, calculated from a recent constraint-based wavevector- and frequency-dependent jellium exchange-correlation kernel.

This record contains all raw data used in this project. The second version contains better-converged data for the frequency moments. The parameters used to generate this data are included in a text file. The third version includes third moment sum rule data that is more stable (a typo in the spline interpolation was rectified, see the Gitlab commit record for more detailed information), as well as expanded correlation energy per electron data. The MCP07 analytic continuation to imaginary frequencies is also more correctly treated in this new data set.

en: Interpretations of ground-state symmetry breaking and strong correlation in wavefunction and density functional theories

density-functional-theory

display_name: density-functional-theory

exchange-correlation-kernel

display_name: exchange-correlation-kernel

jellium

display_name: jellium


en: This record contains the experimental results of the first ultrafast spectroscopic investigation of the electronic properties of SmB6, proposed to realize a Kondo topological insulator.  We employ a multi-temperature model to extract the electron-phonon coupling constant in the range 0.13-0.04, within the assumption of a strong coupling to the optical phonon modes in the range 10-19 meV.

en: SmB6 electron-phonon coupling constant from time- and angle-resolved photoelectron spectroscopy

electron-phonon-coupling

display_name: electron-phonon-coupling

electronic-bands

display_name: electronic-bands

experimental

display_name: experimental

kondo-system

display_name: kondo-system


en: This record contains data to support the findings discussed in our recent work on the synthesis and characterization of super-nonazethrene. Beginning with the early work of Clar et al. in 1955, zethrenes and their laterally-extended homologues, super-zethrenes, have been intensively studied in the solution phase, and are widely investigated as optical and charge transport materials. Super-zethrenes are also considered to exhibit an open-shell ground state. Zethrenes may thus serve as model compounds to investigate nanoscale π-magnetism. However, their synthesis is extremely challenging due to their high reactivity. In the work we report here a combined in-solution and on-surface synthesis of the hitherto largest zethrene homologue – super-nonazethrene – on Au(111). Using single-molecule scanning tunneling microscopy and spectroscopy, we show that super-nonazethrene exhibits an open-shell singlet ground state featuring a large spin polarization-driven electronic gap of 1 eV. We obtain real-space maps of the frontier molecular orbitals, and find that they correspond to singly occupied molecular orbitals. In consistence with the emergence of an open-shell ground state, high-resolution tunneling spectroscopy reveals inelastic singlet-triplet spin excitations in super-nonazethrene, characterized by a strong intramolecular magnetic exchange coupling of 51 meV. Further insights are gained by mean-field and many-body perturbation theory calculations. Given the paucity of zethrene chemistry on surfaces, our results therefore provide unprecedented access to large open-shell zethrene compounds amenable to scanning probe measurements, with potential application in molecular spintronics.

en: On-surface synthesis and characterization of super-nonazethrene

ab-initio-simulations

display_name: ab-initio-simulations

erc

display_name: erc

graphene-flagship

display_name: graphene-flagship

nanographene

display_name: nanographene

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy

zethrenes

display_name: zethrenes


en: Doping has been widely used to control the charge carrier concentration in organic semiconductors. However, in conjugated polymers, n-doping is often limited by the tradeoff between doping efficiency and charge carrier mobilities, since dopants are often randomly distributed within polymers, leading to significant structural and energetic disorder. Here, we screen a large number of polymer building block combinations and explore the possibility of designing n-type conjugated polymers with good tolerance to dopant-induced disorder. We show that a carefully designed conjugated polymer with a single dominant planar backbone conformation, high torsional barrier at each dihedral angle, and zigzag backbone curvature is highly dopable and can tolerate dopant-induced disorder. With these features, the newly designed diketopyrrolopyrrole (DPP)-based polymer can be efficiently n-doped and exhibit high n-type electrical conductivities over 120 S cm−1, much higher than the reference polymers with similar chemical structures. This work provides a new polymer design concept for highly dopable and highly conductive polymeric semiconductors.

en: Approaching disorder-tolerant semiconducting polymers

disorder-tolerant

display_name: disorder-tolerant

n-doping

display_name: n-doping

semiconducting-polymers

display_name: semiconducting-polymers


en: C2N is an ordered two-dimensional carbon nitride with a high density (1.7 × 10^14 cm−2) of 3.1 Å-sized nanopores, making it promising for high-flux gas sieving for energy-efficient He and H2 purification. Herein, we discuss the accurate calculation of potential energy surfaces for He, H2, N2, and CO2 across C2N nanopores, to characterize the gas-sieving potential of C2N. We compare the potential energy surface derived from density-functional theory calculations using five commonly used van der Waals (vdW) approximations. While all five functionals point that the C2N nanopore yields He/N2 and H2/N2 selectivities over 1000, adsorption energies and energy barriers vary remarkably depending on the approximation chosen. To make progress, we compare the calculations against the results from the adiabatic connection fluctuation dissipation theory, with random-phase approximation, known to be accurate in capturing vdW interactions. The comparison indicates that the interaction energy is less accurate with vdW density functional theory. On the other hand, more empirical corrections work reasonably well, a finding that we also confirm for another carbon nitride lattice, poly(triazine imide). Overall, we recommend these for screening carbon nitride materials for gas separation, but also comparing functionals with higher-order approaches when dealing with different materials.

en: Gas transport across carbon nitride nanopores: a comparison of van der Waals functionals against the random-phase approximation

carbon-nitrides

display_name: carbon-nitrides

cscs

display_name: cscs

epfl

display_name: epfl

marvel

display_name: marvel

nanoscale-transport

display_name: nanoscale-transport

random-phase-approximation

display_name: random-phase-approximation

snsf

display_name: snsf

van-der-waals-approximations

display_name: van-der-waals-approximations


en: Cluster expansion (CE) has gained an increasing level of popularity in recent years, and many strategies have been proposed for training and fitting the CE models to first-principles calculation results. The paper reports a new strategy for constructing a training set based on their relevance in Monte Carlo sampling for statistical analysis and reduction of the expected error. We call the new strategy a "bootstrapping uncertainty structure selection" (BUSS) scheme and compared its performance against a popular scheme where one uses a combination of random structure and ground-state search (referred to as RGS). The provided dataset contains the training sets generated using BUSS and RGS for constructing a CE model for disordered Cu2ZnSnS4 material. The files are in the format of the Atomic Simulation Environment (ASE) database (please refer to ASE documentation for more information https://wiki.fysik.dtu.dk/ase/index.html). Each `.db` file contains 100 DFT calculations, which were generated using iteration cycles. Each iteration cycle is referred to as a generation (marked with `gen` key in the database) and each database contains 10 generations where each generation consists of 10 training structures. See more details in the paper.

en: Training sets based on uncertainty estimates in the cluster-expansion method

big-map

display_name: big-map

bootstrapping

display_name: bootstrapping

cluster-expansion

display_name: cluster-expansion

energy-materials

display_name: energy-materials

machine-learning

display_name: machine-learning

monte-carlo

display_name: monte-carlo

phase-transition

display_name: phase-transition


en: We explore the transition to a charge-disproportionated insulating phase in a five-orbital cubic tight-binding model applicable to transition-metal perovskites with a formal d^4 occupation of the transition-metal cation, such as ferrates or manganites. We use dynamical mean-field theory to obtain the phase diagram as a function of the average local Coulomb repulsion U and the Hund's coupling J. The main structure of the phase diagram follows from the zero band-width (atomic) limit and represents the competition between high-spin and low-spin homogeneous and an inhomogeneous charge-disproportionated state. This results in two distinct insulating phases: the standard homogeneous Mott insulator and the inhomogeneous charge-disproportionated insulator, recently also termed Hund's insulator. We characterize the unconventional nature of this Hund's insulating state. Our results are consistent with previous studies of two- and three-orbital models applicable to isolated t2g and eg subshells, respectively, with the added complexity of the low-spin/high-spin transition. We also test the applicability of an effective two-orbital (eg-only) model with disordered S=3/2 t2g core spins. Our results show that the overall features of the phase diagram in the high-spin region are well described by this simplified two-orbital model but also that the spectral features exhibit pronounced differences compared to the full five-orbital description.

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: Charge disproportionation and Hund's insulating behavior in a five-orbital Hubbard model applicable to d^4 perovskites

charge-disproportionation

display_name: charge-disproportionation

dynamical-mean-field-theory

display_name: dynamical-mean-field-theory

metal-insulator-transition

display_name: metal-insulator-transition


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

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

envi

en: Environment

body-centered-cubic-metal

display_name: body-centered-cubic-metal

dislocation-hydrogen-interactions

display_name: dislocation-hydrogen-interactions

hydrogen-embrittlement

display_name: hydrogen-embrittlement


en: Information about the influence of basic density and lignin content on the propensity of wood to checks, simulated in a cleavage mechanical test, is scarce in the literature. Therefore, the objective of this research was to investigate the functional relationships between the cleavage strength and the basic density and lignin content of the wood. For this, two Eucalyptus grandis trees were felled at 22 years of age and specimens for the cleavage test were made. From these specimens, the basic density and lignin content were determined. The basal logs were sawn for making 45 specimens, 22 from the tree "A" and 23 from the tree "B". The production of the specimens followed the suggestions of the ASTM D143 standard (ASTM 2014), being free from any defects that could mask the test results, without distinction of heartwood and sapwood or juvenile and mature wood. The mechanical test was carried out with aid of a universal testing machine EMIC, model DL 30000. The 45 specimens tested in the cleavage test were used to determine the basic density, as described in standard NBR 11941-02 (ABNT, 2003). After determining the basic density of the 45 specimens, 20 of them, 9 from the "A" tree and 11 from the "B" tree, were randomly selected for chemical analysis and determination of the lignin content in the wood. The extractives were removed according to the ABCP M3/89 standard (ABCP, 1974). The insoluble lignin content (Klason lignin) was obtained following the methodology described by Gomide and Demuner (1986). The samples were analyzed in duplicate. The lignin content soluble in sulfuric acid was determined following the methodology proposed by Goldschimidt (1971). The total lignin content was the result of the sum of the soluble and insoluble lignin content.

en: Basic density, lignin content and cleavage strength of Eucalyptus grandis wood

basic-density

display_name: basic-density

cleavage-strength

display_name: cleavage-strength

eucalyptus-grandis

display_name: eucalyptus-grandis

experimental

display_name: experimental

lignin-content

display_name: lignin-content


en: A current goal driving alloy development is the identification of alloy compositions for high temperature applications but with the additional requirement of sufficient ductility at ambient temperatures. Multicomponent, single-phase, polycrystalline High Entropy Alloys (HEAs) have recently emerged as a new class of metal alloys, and some refractory bcc HEAs composed mainly of Nb, V, Ta, Cr, Mo, and/or W show excellent strength retention up to very high temperatures but low ductility at room temperature (RT). Here, it is postulated that the macroscopic ductility in bcc elements and alloys is determined by the intrinsic competition between brittle cleavage and ductile dislocation emission mechanisms at an atomistically sharp crack. The stress intensities K_Ic for cleavage and K_Ie for emission are evaluated within Linear Elastic Fracture Mechanics and validated by atomistic simulations on model alloys. A RT ductility criterion based on K_Ie/K_Ic for critical crack orientations is proposed based on the elemental metals and is then applied to HEAs. Agreement with experimental trends in ductility vs. composition across a range of existing HEAs is demonstrated. The analysis is then extended across large composition spaces of the Mo-Nb-Ta-V-W and Mo-Nb-Ti alloy families, identifying new compositions with the potential for RT ductility.

en: A ductility criterion for bcc high entropy alloys

bcc

display_name: bcc

fracture

display_name: fracture

high-entropy-alloys

display_name: high-entropy-alloys

intrinsic-ductility

display_name: intrinsic-ductility

snsf

display_name: snsf


en: Fast, empirical potentials are gaining increased popularity in the computational fields of materials science, physics and chemistry. With it, there is a rising demand for high-quality reference data for the training and validation of such models. In contrast to research that is mainly focused on small organic molecules, this work presents a data set of geometry-optimized bulk phase zeolite structures. Covering a majority of framework types from the Database of Zeolite Structures, this set includes over thirty thousand geometries. Calculated properties include system energies, nuclear gradients and stress tensors at each point, making the data suitable for model development, validation or referencing applications focused on periodic silica systems.

en: Zeo-1: A computational data set of zeolite structures

bulk-phase

display_name: bulk-phase

density-functional-theory

display_name: density-functional-theory

dft

display_name: dft

horizon-europe

display_name: horizon-europe

machine-learning

display_name: machine-learning

marie-curie-fellowship

display_name: marie-curie-fellowship

silica

display_name: silica

zeolite

display_name: zeolite


en: In this record we provide data to support our recent findings related to the fabrication of pentagon-fused graphene nanoribbons. Graphene nanoribbons (GNRs) have potential for applications in electronic devices. A key issue, thereby, is the fine-tuning of their electronic characteristics, which can be achieved through subtle structural modifications. These are not limited to the conventional armchair, zigzag, and cove edges, but also possible through incorporation of non-hexagonal rings. On-surface synthesis enables the fabrication and visualization of GNRs with atomically precise chemical structures, but strategies for the incorporation of non-hexagonal rings have been underexplored. In the publication, we describe the on-surface synthesis of armchair-edged GNRs with incorporated five-membered rings through the C-H activation and cyclization of benzylic methyl groups. Ortho-Tolyl-substituted dibromobianthryl was employed as the precursor monomer, and visualization of the resulting structures after annealing at 300 °C on a gold surface by high-resolution noncontact atomic force microscopy clearly revealed the formation of methylene-bridged pentagons at the GNR edges. These persisted after annealing at 340 °C, along with a few fully conjugated pentagons having singly-hydrogenated apexes. The benzylic methyl groups could also migrate or cleave-off, resulting in defects lacking the five-membered rings. Moreover, unexpected and unique structural rearrangements, including the formation of embedded heptagons, were observed. Despite the coexistence of different reaction pathways that hamper selective synthesis of a uniform structure, our results provide novel insights into on-surface reactions en route to functional, non-benzenoid carbon nanomaterials.

en: On-surface activation of benzylic C–H bonds for the synthesis of pentagon-fused graphene nanoribbons

ab-initio

display_name: ab-initio

cscs

display_name: cscs

graphene-nanoribbons

display_name: graphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis


en: Mg-Al mixed metal oxides (MMOs), derived from the decomposition of layered double hydroxides (LDHs), have been purposed as a material for CO2 capture of industrial plant emissions. In order to aid in the design and optimization of these materials for CO2 capture at 200 °C, we have used the combination of solid-state nuclear magnetic resonance (ssNMR) and density functional theory (DFT) to characterize the CO2 gas sorption products and determine the various sorption sites in the Mg-Al MMOs. Comparison of DFT cluster calculations with 13C chemical shift of the chemisorbed products indicates that mono and bi-dentate carbonate are formed at the Mg-O site with an adjacent Al substitution of an Mg atom, while bicarbonate is formed at Mg-OH sites without adjacent Al substitution. Quantitative 13C NMR shows an increase in the relative amount of strongly basic sites, where the monodentate carbonate product is formed, with increasing Al mole % in the MMO. This detailed understanding of the various basic Mg-O sites presents in the MMO material, and the formation of the carbonate, bidentate carbonate and bicarbonate chemisorbed species yields new insight into the mechanism of CO2 adsorption at 200 °C which can further aid in the design and capture capacity optimization of the materials.

en: Characterization of chemisorbed species and active adsorption sites in Mg-Al mixed metal oxides for high temperature CO2 capture

carbon-capture

display_name: carbon-capture

epfl

display_name: epfl

marvel

display_name: marvel

nmr

display_name: nmr


en: The beta-glycine dataset is created with the purpose of validating the electron machine learning potential (eMLP) on crystalline beta glycine. It contains 25,676 configurations with normal mode perturbations for the nuclei and unit cell and electric field perturbations. Energies, forces and Wannier centers are computed using density functional theory (DFT) with the PBE functional and a Plane-Wave basis set in the ab-initio quantum chemistry code QuantumESPRESSO.

en: A dataset for beta-glycine with Wannier centers

beta-glycine

display_name: beta-glycine

wannier-centers

display_name: wannier-centers


en:
We develop efficient, accurate, transferable, and interpretable machine learning force fields for Au nanoparticles, based on data gathered from Density Functional Theory calculations. We then use them to investigate the thermodynamic stability of Au nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, with respect to a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with respect to available experimental data. Furthermore, we characterize in detail the solid to liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a rigorous and data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle, and employ it to show that melting initiates at the outer layers.

The record contains all the MD simulation trajectories carried out using mapped machine learning force fields in LAMMPS, the machine learning force fields as LAMMPS pair potentials, and the ab initio training data used to construct such force fields.

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: Data-driven simulation and characterisation of gold nanoparticles melting

erc

display_name: erc

gold

display_name: gold

max

display_name: max

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nanoparticle

display_name: nanoparticle


en:
Material-specific calculations based on density functional theory play a major role in understanding and designing the properties of quantum matter. In the field of topological quantum computing there is an intense search for material systems that have the ability to realize Majorana zero modes. The ability to combine the accurate electronic structure, that is accessible from density functional theory, with superconductivity can help gaining material-specific insights and may contribute to the understanding and realization of Majorana zero modes in solid state systems. In this work we report on our implementation of the Bogoliubov-de Gennes method into the JuKKR code [https://jukkr.fz-juelich.de], an implementation of the all-electron, full-potential Korringa-Kohn-Rostoker Green function method, which allows a material-specific description of inhomogeneous superconductors and heterostructures on the basis of density functional theory. We describe the formalism and report on calculations for the s-wave superconductor Nb. We compare the properties of the superconducting state both in the bulk and for (110) thin films of different thickness. We comment on the importance of spin-orbit coupling, the effect of surface relaxations and the influence of a softening of phonon modes on the surface for the resulting superconducting gap.

This dataset contains the Fleur and KKR calculations that were done in this study using the AiiDA-Fleur and AiiDA-KKR plugins and a tutorial that introduces the practical use of AiiDA-KKR for BdG calculations.

en: Density functional Bogoliubov-de Gennes analysis of superconducting Nb and Nb(110) surfaces

envi

en: Environment

aiida-kkr

display_name: aiida-kkr

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

density-functional-theory

display_name: density-functional-theory

jukkr

display_name: jukkr

nb

display_name: nb

superconductivity

display_name: superconductivity

surface

display_name: surface


en: In this record we provide data to support our recent findings related to the fabrication of π-conjugated ladder-type polymers comprising nonbenzenoid moieties. On-surface synthesis provides a powerful approach toward the atomically precise fabrication of π-conjugated ladder polymers (CLPs). In the manuscript we report the surface-assisted synthesis of nonbenzenoid CLPs from cyclopenta-annulated anthracene monomers on Au(111) under ultrahigh vacuum conditions. Successive thermal annealing steps reveal the dehalogenative homocoupling to yield an intermediate 1D polymer and the subsequent cyclodehydrogenation to form the fully conjugated ladder polymer. Notably, neighbouring monomers may fuse in two different ways, resulting in six- and five-membered rings, respectively. The structure and electronic properties of the reaction products have been investigated via low-temperature scanning tunneling microscopy and spectroscopy, complemented by density-functional theory calculations. Our results provide perspectives for the on-surface synthesis of nonbenzenoid CLPs with the potential to be used for organic electronic devices.

en: On-surface synthesis of π-conjugated ladder-type polymers comprising nonbenzenoid moieties

ab-initio

display_name: ab-initio

cscs

display_name: cscs

nonbenzenoid

display_name: nonbenzenoid

on-surface-synthesis

display_name: on-surface-synthesis

polymers

display_name: polymers

snsf

display_name: snsf


en:
The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of the computational prediction of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential energy surface.
To test the limits of this approximation, we perform a systematic study of the role of finite-temperature and quantum nuclear fluctuations on 1H, 13C, and 15N chemical shieldings in molecular crystals -- considering the paradigmatic examples of the different polymorphs of benzene, glycine, and succinic acid.
We find the effect of quantum fluctuations to be comparable in size to the typical errors of predictions of chemical shieldings for static nuclei with respect to experimental measurements, and to improve the match between experiments and theoretical predictions, translating to more reliable assignment of the NMR spectra to the correct candidate structure.
Thanks to the use of integrated machine-learning models trained on both first-principles configurational energies and chemical shieldings, the accurate sampling of thermal and quantum fluctuations of the structures can be achieved at an affordable cost, setting a new standard for the calculations that underlie solid-state structural determination by NMR.

This archive contains the machine-learning potentials and shielding models, sample inputs, and scripts to reproduce the results discussed in the manuscript. It further contains the reference data and scripts to reconstruct the shielding models.

en: The importance of nuclear quantum effects for NMR crystallography

first-principles-gipaw-dft

display_name: first-principles-gipaw-dft

machine-learning

display_name: machine-learning

marvel

display_name: marvel

molecular-crystals

display_name: molecular-crystals

nmr-chemical-shielding

display_name: nmr-chemical-shielding

nuclear-quantum-effects

display_name: nuclear-quantum-effects


en:
Structure prediction for molecular crystals is a longstanding challenge, as often minuscule free energy differences between polymorphs are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations, which simultaneously account for all terms, have not been computationally viable.
Here we reproduce the experimental stabilities of polymorphs of prototypical compounds -- benzene, glycine, and succinic acid -- by computing rigorous first-principles Gibbs free energies, at a fraction of the cost of conventional methods. This is achieved by a bottom-up approach, which involves generating machine-learning potentials to calculate surrogate free energies and subsequently calculating true first-principles free energies using inexpensive free energy perturbations.
Accounting for all relevant physical effects is no longer a daunting task and provides the foundation for structure predictions for more complex systems of industrial importance.

This Materials Cloud archive contains first-principles training, validation, and test data for polymorphs of benzene, succinic acid, and glycine underlying the above-mentioned machine-learning potentials.
For each compound the archive provides two sets of data: the first based on DFT calculations with the semi-local PBE functional and the Tkatchenko-Scheffler dispersion correction, and the second based on DFT calculations with the hybrid PBE0 functional and the many-body dispersion correction of Tkatchenko et al. For each compound and both levels of electronic-structure theory, structure datasets in libatom extended-xyz format provide representative, thermalised configurations and the associated configurational energies, atomic forces, and stresses on the simulation cell. The configurations are extracted from a combination of classical temperature replica exchange molecular dynamics simulations and path-integral molecular dynamics for a representative set of perturbed unit cells based on the experimental structures of: forms I, II, Ihp and V' of benzene, alpha- and beta-succinic acid, and alpha-, beta-, gamma-, and delta-glycine. The detailed data provenance and dataset architecture is described in the attached README file.

en: Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine

first-principles

display_name: first-principles

hybrid-functional-dft

display_name: hybrid-functional-dft


en: In Peierls-distorted materials, photoexcitation leads to a strongly coupled transient response between structural and electronic degrees of freedom, always measured independently of each other. Here we use transient reflectivity in the extreme ultraviolet to quantify both responses in photoexcited bismuth in a single measurement. With the help of first-principles calculations based on density-functional theory (DFT) and time-dependent DFT, the real-space atomic motion and the temperature of both electrons and holes as a function of time are captured simultaneously, retrieving an anticorrelation between the A1g phonon dynamics and carrier temperature. The results reveal a coherent, bi-directional energy exchange between carriers and phonons, which is a dynamical counterpart of the static Peierls-Jones distortion, providing first-time validation of previous theoretical predictions.

en: Coherent energy exchange between carriers and phonons in Peierls-distorted bismuth unveiled by broadband XUV pulses

absorption-spectrum

display_name: absorption-spectrum

bismuth

display_name: bismuth

density-functional-theory

display_name: density-functional-theory

density-of-states

display_name: density-of-states

extreme-ultraviolet

display_name: extreme-ultraviolet

investissements-davenir

display_name: investissements-davenir

marvelosp

display_name: marvelosp

photoexcitation

display_name: photoexcitation

real-space-atomic-motion

display_name: real-space-atomic-motion

transient-reflectivity

display_name: transient-reflectivity


en: We address the long-standing question of the nature of oxygen vacancies in strontium titanate, using a combination of density functional theory and dynamical mean-field theory (DFT+DMFT) to investigate in particular the effect of vacancy-site correlations on the electronic properties. Our approach uses a minimal low-energy electronic subspace including the Ti-t2g orbitals plus an additional vacancy-centered Wannier function, and it provides an intuitive and physically transparent framework to study the effect of the local electron-electron interactions on the excess charge introduced by the oxygen vacancies. We estimate the strength of the screened interaction parameters using the constrained random phase approximation, and we find a sizable Hubbard U parameter for the vacancy orbital. Our main finding, which reconciles previous experimental and computational results, is that the ground state is either a state with double occupation of the localized defect state or a state with a singly occupied vacancy and one electron transferred to the conduction band. The balance between these two competing states is determined by the strength of the interaction both on the vacancy and the Ti sites, and on the Ti-Ti distance across the vacancy. Finally, we contrast the case of vacancy doping in SrTiO3 with doping via La substitution, and we show that the latter is well described by a simple rigid-band picture.

en: Oxygen vacancies in strontium titanate: a DFT+DMFT study

cscs

display_name: cscs

dftdmft

display_name: dftdmft

electronic-structure

display_name: electronic-structure

oxygen-vacancies

display_name: oxygen-vacancies


en: Effective catalysts for the direct conversion of methane to methanol and for methane's dry reforming to syngas are Holy Grails of catalysis research toward clean energy technologies. It has recently been discovered that Ni at low loadings on CeO2 is very reactive towards reactants CH4, H2O and CO2 and active for both of these reactions. Revealing the nature of the active sites in such systems is paramount to a rational design of improved catalysts. Here, using a combination of experimental measurements and density functional theory calculations, we show that the most active sites are cationic Ni atoms in clusters at step edges on the CeO2 surface, using the activation of CH4 as an example . We show that the size and morphology of the supported nanoparticles together with strong Ni−support bonding and charge transfer at the step edge are key to the high catalytic activity towards these methane conversions. We anticipate that this knowledge will inspire the development of more efficient catalysts for these reactions.

en: The nature of the active sites on Ni/CeO2 catalysts for methane conversions

dftuv

display_name: dftuv


en: Quantitative evaluation of the thermodynamic properties of materials—most notably their stability, as measured by the free energy—must take into account the role of thermal and zero-point energy fluctuations. While these effects can easily be estimated within a harmonic approximation, corrections arising from the anharmonic nature of the interatomic potential are often crucial and require computationally costly path integral simulations to obtain results that are essentially exact for a given potential. Consequently, different approximate frameworks for computing affordable estimates of the anharmonic free energies have been developed over the years. Understanding which of the approximations involved are justified for a given system, and therefore choosing the most suitable method, is complicated by the lack of comparative benchmarks. To facilitate this choice we assess the accuracy and efficiency of some of the most commonly used approximate methods: the independent mode framework, the vibrational self-consistent field, and self-consistent phonons. We compare the anharmonic correction to the Helmholtz free energy against reference path integral calculations. These benchmarks are performed for a diverse set of systems, ranging from simple weakly anharmonic solids to flexible molecular crystals with freely rotating units. The results suggest that, for simple solids such as allotropes of carbon, these methods yield results that are in excellent agreement with the reference calculations, at a considerably lower computational cost. For more complex molecular systems such as polymorphs of ice and paracetamol the methods do not consistently provide a reliable approximation of the anharmonic correction. Despite substantial cancellation of errors when comparing the stability of different phases, we do not observe a systematic improvement over the harmonic approximation even for relative free energies. We conclude that, at least for the classes of materials considered here, efforts toward obtaining computationally feasible anharmonic free energies should therefore be directed toward reducing the expense of path integral methods.

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: Assessment of approximate methods for anharmonic free energies

crystal-structure-prediction

display_name: crystal-structure-prediction

erc

display_name: erc

marvel

display_name: marvel

nuclear-quantum-effects

display_name: nuclear-quantum-effects

quantum-statistics

display_name: quantum-statistics

snsf

display_name: snsf


en: Chemical reactivity and sorption in zeolites are coupled to confinement and - to a lesser extent- to the acid strength of Brønsted acid sites (BAS). In presence of water the zeolite Brønsted acid sites eventually convert into hydronium ions. The gradual transition from zeolite Brønsted acid sites to hydronium ions conversion in zeolites of varying pore size is examined by ab initio molecular dynamics combined with enhanced sampling based on well-tempered metadynamics and a recently developed set of collective variables. While at low water content (1-2 water/BAS) the acidic protons prefer to be shared between zeolites and water, higher water contents (n>2) invariably lead to solvation of the protons within a localized water cluster adjacent to the BAS.  At low water loadings the standard free energy of the formed complexes is dominated by enthalpy and is associated with the acid strength of the BAS and the space around the site. Conversely, the entropy increases linearly with the concentration of waters in the pores, favors proton solvation and is independent of the pore size/shape.

en: Confinement effects and acid strength in Zeolites

envi

en: Environment

acid-base

display_name: acid-base

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics

zeolites

display_name: zeolites


en: elphbolt is a modern Fortran (2018 standard) code for efficiently solving the coupled electron-phonon Boltzmann transport equations from first principles. Using results from density functional and density functional perturbation theory as inputs, it can calculate the effect of the non-equilibrium phonons on the electronic transport (phonon drag) and non-equilibrium electrons on the phononic transport (electron drag) in a fully self-consistent manner and obeying the constraints mandated by thermodynamics. It can calculate the lattice, charge, and thermoelectric transport coefficients for the temperature gradient and electric fields, and the effect of the mutual electron-phonon drag on these transport properties. The code fully exploits the symmetries of the crystal and the transport-active window to allow the sampling of extremely fine electron and phonon wave vector meshes required for accurately capturing the drag phenomena. The coarray feature of modern Fortran, which offers native and convenient support for parallelization, is utilized. The code is compact, readable, well-documented, and extensible by design.

en: The elphbolt ab initio solver for the coupled electron-phonon Boltzmann transport equations

ab-initio

display_name: ab-initio

boltzmann-transport

display_name: boltzmann-transport

electron-drag

display_name: electron-drag

electron-phonon-coupling

display_name: electron-phonon-coupling

electron-transport

display_name: electron-transport

intersect

display_name: intersect

phonon-drag

display_name: phonon-drag

phonon-transport

display_name: phonon-transport

thermoelectricity

display_name: thermoelectricity


en: Neural network (NN) force fields can predict potential energy surfaces with high accuracy and speed compared to electronic structure methods typically used to generate their training data. However, NN predictions are well-defined only for points close to the training domains, and may exhibit poor results during extrapolation. Uncertainty quantification methods can detect geometries for which predicted errors are high, but sampling regions of high uncertainty requires a thorough exploration of the phase space, often using expensive simulations. Our work uses automatic differentiation to sample atomistic configurations by balancing thermodynamic accessibility and uncertainty quantification without using molecular dynamics simulations. This dataset provides the atomistic data used to train the NN potentials for the ammonia, alanine dipeptide, and zeolite-molecule systems. For all materials, geometries, energies, and forces are provided. The ammonia and zeolite systems were computed using density functional theory calculations, while the alanine dipeptide dataset was generated using molecular dynamics simulations with the OPLS force field.

en: Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning


en: Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help accelerate the discovery of new alloys. We present two datasets: 1) chemical composition, and 2) mechanical properties for predominantly wrought aluminum alloys. The first dataset contains 14,884 entries on aluminum alloy compositions extracted from academic literature and US patents using text processing techniques, including 550 wrought aluminum alloys which are already registered with the Aluminum Association. The second dataset contains 1,278 entries on mechanical properties for aluminum alloys, where each entry is associated with a particular wrought series designation, extracted from tables in academic literature.

en: Aluminum alloy compositions and properties extracted from a corpus of scientific manuscripts and US patents

aluminum

display_name: aluminum

chemical-composition

display_name: chemical-composition

experimental

display_name: experimental

materials-discovery

display_name: materials-discovery

mechanical-properties

display_name: mechanical-properties

metals-and-alloys

display_name: metals-and-alloys


en: Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between principal component analysis and linear regression, and can be used to conveniently reveal structure-property relations in terms of simple-to-interpret, low-dimensional maps. Here we introduce a kernelized version of PCovR and a sparsified extension, and demonstrate the performance of this approach in revealing and predicting structure-property relations in chemistry and materials science, showing a variety of examples including elemental carbon, porous silicate frameworks, organic molecules, amino acid conformers, and molecular materials.

en: Structure-property maps with kernel principal covariates regression

dimensionality-reduction

display_name: dimensionality-reduction

epfl

display_name: epfl

erc

display_name: erc

kernel-methods

display_name: kernel-methods

materials-science

display_name: materials-science

max

display_name: max

snsf

display_name: snsf


en: To study the dependence of the relative stability of surface (VA) and subsurface (VB) oxygen vacancies with the crystal facet of CeO2, the reduced (100), (110) and (111) surfaces, with two different concentrations of vacancies, were investigated by means of density functional theory (DFT+U) calculations. The results show that the trend in the near-surface vacancy formation energies for comparable vacancy spacings, i.e. (110) < (100) < (111), does not follow that in the surface stability of the facets, i.e. (111) < (110) < (100). The results also revealed that the preference of vacancies for surface or subsurface sites, as well as the preferred location of the associated Ce3+ polarons, are facet and concentration dependent. At the higher vacancy concentration, the VA is more stable than the VB at the (110) facet whereas at the (111), it is the other way around, and at the (100) facet, both the VA and the VB have similar stability. The stability of the VA vacancies, compared to that of the VB, is accentuated as the concentration decreases. Nearest neighbor polarons to the vacant sites are only observed for the less densely packed (110) and (100) facets. These findings are rationalized in terms of the packing density of the facets, the lattice relaxation effects induced by vacancy formation and the localization of the excess charge, and the repulsive Ce3+ - Ce3+ interactions.

en: Facet-dependent stability of near-surface oxygen vacancies and excess charge localization at CeO2 surfaces

cerium-oxide-surfaces

display_name: cerium-oxide-surfaces

excess-charge-localization

display_name: excess-charge-localization

facet-dependent-stability

display_name: facet-dependent-stability

lattice-relaxations

display_name: lattice-relaxations

oxygen-vacancies

display_name: oxygen-vacancies


en: We introduce turboMagnon, an implementation of the Liouville-Lanczos approach to linearized time-dependent density-functional theory, designed to simulate spin-wave spectra in solid-state materials. The code is based on the noncollinear spin-polarized framework and the self-consistent inclusion of spin-orbit coupling that allow to model complex magnetic excitations. The spin susceptibility matrix is computed using the Lanczos recursion algorithm that is implemented in two flavors - the non-Hermitian and the pseudo-Hermitian one. turboMagnon is open-source software distributed under the terms of the GPL as a component of QE. As with other components, turboMagnon is optimized to run on massively parallel architectures using native mathematical libraries (LAPACK and FFTW) and a hierarchy of custom parallelization layers built on top of MPI. The effectiveness of the code is showcased by computing magnon dispersions for the CrI₃ monolayer, and the importance of the spin-orbit coupling is discussed.

en: turboMagnon - A code for the simulation of spin-wave spectra using Liouville-Lanczos approach to time-dependent density-functional perturbation theory

cineca

display_name: cineca

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

linear-response

display_name: linear-response

liouville-lanczos-approach

display_name: liouville-lanczos-approach

magnons

display_name: magnons

marvel

display_name: marvel

quantum-espresso

display_name: quantum-espresso

spin-orbit-coupling

display_name: spin-orbit-coupling

spin-wave-spectra

display_name: spin-wave-spectra


en: The electron QM7 (eQM7) dataset is created with the purpose of training and validating polarizable (machine learning) force fields on non-equilibrium configurations of small molecules. It contains 6868 molecules with hydrogen, carbon, nitrogen and oxygen. For each molecule, 500 perturbations are constructed using normal mode sampling, torsion sampling, dimer sampling and homogeneous electric fields. Energies, forces and Foster-Boys centers are computed using density functional theory (DFT) with the PBE0 functional, Aug-cc-pVTZ basis set in the ab-initio quantum chemistry code Psi4.

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: eQM7: a dataset for small molecules with Foster-Boys centers

density-functional-theory

display_name: density-functional-theory

foster-boys-centers

display_name: foster-boys-centers

machine-learning

display_name: machine-learning


superconductivity-in-antiperovskites

en: We present a comprehensive theoretical study of conventional superconductivity in cubic antiperovskites materials with composition XYZ₃ where X and Z are metals and Y is  H, B, C, N, O, and P. Our starting point are electron-phonon calculations for 384 materials performed with density-functional perturbation theory. While 40% of the materials were dynamically unstable as they exhibited imaginary frequencies, we discovered 16 compounds with Tc higher than 5 K including antiperovskites with Y=H, N, C and O. We used these results to train interpretable machine learning models to understand and further explore this family of compounds. This lead us to predict a further 44 materials with superconducting transition temperatures above 5 K, reaching a maximum of 17.8 K for PtHBe₃. Furthermore, the models give us an understanding of the mechanism of superconductivity in anti-perovskites and highlight the importance of the density of states at the Fermi level and of the mass of the Y-atom for the strength of the phonon coupling. Finally, we study in detail a few systems, uncovering some issues with previously published theoretical data. The combination of traditional approaches with interpretable machine learning turns out to be a very efficient methodology to study and systematize whole classes of materials, and is easily extendable to other families of compounds or physical properties.

en: Superconductivity in antiperovskites

title_for_slug: superconductivity-in-antiperovskites

envi

en: Environment

anti-perovskites

display_name: anti-perovskites

electron-phonon-coupling

display_name: electron-phonon-coupling

superconductivity

display_name: superconductivity


en: Within the framework of many-body perturbation theory integrated with density functional theory (DFT), a novel defect-subspace projection GW method, the so-called p-GW, is proposed. By avoiding the periodic defect interference through open boundary self-energies, we show that the p-GW can efficiently and accurately describe quasi-particle correlated defect levels in two-dimensional (2D) monolayer MoS₂. By comparing two different defect states originating from sulfur vacancy and adatom to existing theoretical and experimental works, we show that our GW correction to the DFT defect levels is precisely modelled. Based on these findings, we expect that our method can provide genuine trap states for various 2D transition-metal dichalcogenide (TMD) monolayers, thus enabling the study of defect-induced effects on the device characteristics of these materials via realistic simulations.

en: Efficient and accurate defect level modelling in monolayer MoS₂ via GW+DFT with open boundary conditions

dft

display_name: dft

gw

display_name: gw

negf

display_name: negf


en: Nonfused ring electron acceptors (NFREAs) are interesting n-type near infrared (NIR) photoactive semiconductors with strong molecular absorption and easy synthetic route. However, the low backbone planarity and bulky substitution make NFREA less crystalline, which significantly retards charge transport and the formation of bicontinuous morphology in organic photovoltaic device. Donor and acceptor solubility in different solvents is studied, and the created solubility hysteresis can induce the formation of the highly crystalline donor polymer fibril to purify the NFREA phase, thus a better bicontinuous morphology with improved crystallinity. Based on these results, a general solubility hysteresis sequential condensation (SHSC) thin film fabrication methodology is established to produce highly uniform and smooth photoactive layer. The well-defined interpenetrating network morphology afforded a record efficiency of 19.02%, which is ~22% improvement comparing to conventional device fabrication. A high efficiency retention (Pr) value of 92.3% is achieved in 1 cm² device (17.28% efficiency).

en: Achieving 19% efficiency in nonfused ring electron acceptor solar cells via solubility control of donor and acceptor crystallisation

experimental

display_name: experimental

large-area-device

display_name: large-area-device

nonfused-ring-electron-acceptor

display_name: nonfused-ring-electron-acceptor

organic-solar-cells

display_name: organic-solar-cells

record-efficiency

display_name: record-efficiency


en:
Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, that are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), that are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes, that gather information on the relationship between neighboring atoms using "message-passing" ideas, cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and provides a coherent foundation to systematize our understanding of both atom-centered and message-passing, invariant and equivariant machine-learning schemes.

This record contains the data and code required to reproduce the results from the corresponding paper, computing message-passing inspired machine learning features built on top of density correlation. The data used in this article is a subset of other existing datasets, which can be found online:

- methane dataset: https://archive.materialscloud.org/record/2020.105
- NaCl dataset: https://github.com/dilkins/TENSOAP/tree/ea671154b3642b4ec879a4292a4dd4399ddbdea6/example/random_nacl
- QM7 and QM9 with dipole moments: https://archive.materialscloud.org/record/2020.56

en: Unified theory of atom-centered representations and message-passing machine-learning schemes

message-passing

display_name: message-passing

pasc

display_name: pasc

reproducibility

display_name: reproducibility


en: On-surface synthesis is a rapidly developing field involving chemical reactions on well-defined solid surfaces to access the synthesis of low-dimensional organic nanostructures which cannot be achieved via traditional solution chemistry. On-surface reactions critically depend on a high degree of chemoselectivity in order to achieve an optimum balance between the target structure and possible side products. In this record we provide data for the calculations that support a work that we recently published. In the published manuscript, we demonstrate the synthesis of graphene nanoribbons with a large unit cell based on steric hindrance-induced complete chemoselectivity as revealed by scanning probe microscopy measurements and density functional theory calculations. Our results disclose that combined molecule-substrate van der Waals interactions and intermolecular steric hindrance promote a selective aryl-aryl coupling, giving rise to high-quality uniform graphene nanostructures. The established coupling strategy has been used to synthesize two types of graphene nanoribbons with different edge topologies inducing a pronounced variation of the electronic energy gaps. The demonstrated chemoselectivity is representative of n-anthryl precursor molecules and may be further exploited to synthesize graphene nanoribbons with novel electronic, topological and magnetic properties with implications for electronic and spintronic applications.

en: Steering on-surface reactions through molecular steric hindrance and molecule-substrate van der Waals interactions

aiidalab

display_name: aiidalab

graphene-nanoribbons

display_name: graphene-nanoribbons

marvel

display_name: marvel

on-surface-synthesis

display_name: on-surface-synthesis

scanning-probe-microscopy

display_name: scanning-probe-microscopy

snsf

display_name: snsf


en: Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions representing transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.

en: Efficient, interpretable graph neural network representation for angle-dependent properties and its application to optical spectroscopy

aqua-complex

display_name: aqua-complex

graph-neural-network

display_name: graph-neural-network

spectroscopy

display_name: spectroscopy


en: A significant challenge for graphene nanoplatelet (GNP) suppliers is the meaningful characterisation of platelet morphology in an industrial environment. This challenge is further exacerbated to platelet surface chemistry when scalable functionalisation processes such as plasma treatment are used to modify the GNPs to improve the filler-matrix interphase in nanocomposites. The costly and complex suite of analytical equipment necessary for a complete material description makes quality control and process optimisation difficult. Raman spectroscopy is a facile and accessible characterisation technique with recent advancements unlocking fast mapping for rapid data collection. In this work we detail several big-data methodologies that extract full value out of Raman spectra so that GNP morphology and surface chemistry can be characterised. A unsupervised peak fitting and processing algorithm was used to extract crystallinity data rapidly and accurately and correlate it with laser-diffraction derived lateral size values for a commercial set of GNPs. Classical machine learning was used to find the most informative Raman features for classifying plasma functionalised GNPs. The initial material properties were found to affect which peak features are most useful for classification. In low-defect density and low specific surface area GNPs, the D peak FWHM is found the most useful, whereas the 2D/G ratio is most useful in the opposite case. Finally, a convolutional neural network was trained to discern between different GNP grades with 86% accuracy. This work demonstrates how computer vision could be deployed for rapid and accurate quality control on the factory floor.

en: Novel techniques for characterising graphene nanoplatelets using Raman spectroscopy and machine learning

experimental

display_name: experimental

graphene-nanoplatelets

display_name: graphene-nanoplatelets

machine-learning

display_name: machine-learning

raman

display_name: raman


en: We present a combined theoretical and experimental investigation of the anti-symmetric Compton profile in LiNiPO4 as a possible probe for magneto-electric toroidal moments. Understanding as well as detecting such magneto-electric multipoles is an active area of research in condensed matter physics. Our calculations, based on density functional theory, indicate an anti-symmetric Compton profile in the direction of the ty toroidal moment in momentum space, with the computed anti-symmetric profile around four orders of magnitude smaller than the total profile. The difference signal that we measure is consistent with the computed profile, but of the same order of magnitude as the statistical errors and systematic uncertainties of the experiment. Our results motivate further theoretical work to understand the factors that influence the size of the anti-symmetric Compton profile, and to identify materials exhibiting larger effects.

materialscloud

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

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

en: Materials Cloud

en: Anti-symmetric Compton scattering in LiNiPO4: Towards a direct probe of the magneto-electric multipole moment

compton-scattering

display_name: compton-scattering

density-functional-theory

display_name: density-functional-theory

erc

display_name: erc

horizon-europe

display_name: horizon-europe

magneto-electric-toroidal-moment

display_name: magneto-electric-toroidal-moment


en: In this record we provide the data to support our recent finding on the intercalation of gold chloride underneath atomically precise graphene nanoribbons (GNRs). GNRs have a wide range of electronic properties that depend sensitively on their chemical structure. Several types of GNRs have been synthesized on metal surfaces through selective surface-catalyzed reactions. The resulting GNRs are adsorbed on the metal surface, which may lead to hybridization between the GNR orbitals and those of the substrate. This makes investigation of the intrinsic electronic properties of GNRs more difficult, and also rules out capacitive gating. In the manuscript where the data presented here is discussed, we demonstrate the formation of a dielectric gold chloride adlayer that can intercalate underneath GNRs on the Au(111) surface. The intercalated gold chloride adlayer electronically decouples the GNRs from the metal and leads to a substantial hole doping of the GNRs. Our results introduce an easily accessible tool in the in situ characterization of GNRs grown on Au(111) that allows for exploration of their electronic properties in a heavily hole-doped regime.

en: Electronic decoupling and hole-doping of graphene nanoribbons on metal substrates by chloride intercalation

envi

en: Environment

dft

display_name: dft

electronic-properties

display_name: electronic-properties

stm

display_name: stm


en: Electrochemical systems are often simulated by using numerical methods based on finite element solution of differential equations. We developed a method to decrease the needed computational resources, based on the analytical solution of a part of the system: the obtained analytical equations are applied as boundary conditions to the finite element calculation. The part of the system that is analytically solved is the region of the diffuse double layer. We provide two COMSOL models: i) a 1d fully numerical calculation and ii) the same simulation performed with the semi-analytical method. For each model, we provide the .mph file (Comsol) and the .java file (Java+Comsol). We also provide examples of the resulting impedances and frequency responses of various parameters.

en: Numerical simulation of an electrochemical system and semi-analytical method

diffuse-double-layer

display_name: diffuse-double-layer

electrochemical-system

display_name: electrochemical-system

finite-element-method

display_name: finite-element-method

semi-analytical-solution

display_name: semi-analytical-solution


en: The catalytic oxidation of HCl by molecular oxygen (Deacon process) over ceria allows the recovery of molecular chlorine from omnipresent HCl waste produced in various industrial processes. In previous density functional theory (DFT) model calculations by Amrute et al. [J. Catal. 2012, 286, 287–297.], it was proposed that the most critical reaction step in this process is the displacement of tightly bound chlorine at a vacant oxygen position on the CeO2(111) surface (Clvac) toward a less strongly bound cerium on-top (Cltop) position. This step is highly endothermic by more than 2 eV. On the basis of a dedicated model study, namely the re-oxidation of a chlorinated single crystalline Clvac-CeO2−x(111)-(√3 × √3)R30° surface structure, we provide in-situ synchrotron-based spectroscopic data (high-resolution core level spectroscopy (HRCLS) and X-ray adsorption near edge structure (XANES)) for this oxygen-induced de-chlorination process. Combined with theoretical evidence from DFT calculations, the Clvac → Cltop displacement reaction is predicted to be induced by an adsorbed peroxo species (O22-), making the displacement step concerted and exothermic by only 0.6 eV with an activation barrier of only 1.04 eV. The peroxo species is shown to be important for the re-oxidation of Clvac-CeO2−x(111) and is considered essential for understanding the function of ceria in oxidation catalysis.

en: Critical step in the HCl oxidation reaction over single-crystalline CeO2−x(111): Peroxo-induced site change of strongly adsorbed surface chlorine

deacon-process

display_name: deacon-process

oxygen-induced-de-chlorination-process

display_name: oxygen-induced-de-chlorination-process

peroxo-surface-species

display_name: peroxo-surface-species

reduced-ceria

display_name: reduced-ceria


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

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

band-gap

display_name: band-gap

bond-angles

display_name: bond-angles

bond-length

display_name: bond-length

crystal-structure

display_name: crystal-structure

density-of-states

display_name: density-of-states

dftu

display_name: dftu

dftuv

display_name: dftuv

hubbard-parameters

display_name: hubbard-parameters

hubbard-projectors

display_name: hubbard-projectors

interstitial-doping

display_name: interstitial-doping

magnetic-moment

display_name: magnetic-moment

orthogonalized-atomic-orbitals

display_name: orthogonalized-atomic-orbitals

oxidation-state

display_name: oxidation-state

self-interactions

display_name: self-interactions

spin-configuration

display_name: spin-configuration

substitutional-doping

display_name: substitutional-doping


en: Magnetic topological insulators (TIs) promise a wealth of applications in spin-based technologies, relying on the novel quantum phenomena provided by their topological properties. Particularly promising is the (MnBi₂Te₄)(Bi₂Te₃)n layered family of established intrinsic magnetic TIs that can flexibly realize various magnetic orders and topological states. High tunability of this material platform is enabled by manganese–pnictogen intermixing, whose amounts and distribution patterns are controlled by synthetic conditions. Positive implication of the strong intermixing in MnSb₂Te₄ is the interlayer exchange coupling switching from antiferromagnetic to ferromagnetic, and the increasing magnetic critical temperature. On the other side, intermixing also implies atomic disorder which may be detrimental for applications. Here we employ nuclear magnetic resonance and muon spin spectroscopy, sensitive local probe techniques, to scrutinize the impact of the intermixing on the magnetic properties of (MnBi₂Te₄)(Bi₂Te₃)n and MnSb₂Te₄. Our measurements not only confirm the opposite alignment between the Mn magnetic moments on native sites and antisites in the ground state of MnSb₂Te₄, but for the first time directly show the same alignment in (MnBi₂Te₄)(Bi₂Te₃)n with n = 0, 1 and 2. Moreover, for all compounds, we find the static magnetic moment of the Mn antisite sublattice to disappear well below the intrinsic magnetic temperature, leaving a homogeneous magnetic structure undisturbed by the intermixing. Our findings provide microscopic understanding of the crucial role played by Mn–Bi intermixing in (MnBi₂Te₄)(Bi₂Te₃)n and offer pathways to optimizing the magnetic gap in its surface states. The data contained in this archive consists of both the µSR and NMR experimental data including DFT calculations for the muon and hyperfine coupling interactions, all required to reproduce the results and figures presented in this article.

en: Different ordering temperature of the Mn antisite sublattice in the intrinsic magnetic topological insulators of the MnBi₂Te₄ family

cationic-intermixing

display_name: cationic-intermixing

experimental

display_name: experimental

magnetic-topological-insulator

display_name: magnetic-topological-insulator


en: In this record we provide data to support our recent findings related to carbon nanotube based quantum dots for tunable terahertz detection. Generating and detecting radiation in the technologically relevant range of the so-called terahertz gap (0.1–10 THz) is challenging because of a lack of efficient sources and detectors. Quantum dots in carbon nanotubes have shown great potential to build sensitive terahertz detectors, usually based on photon-assisted tunneling. A recently reported mechanism combining resonant quantum dot transitions and tunneling barrier asymmetries results in a narrow linewidth photocurrent response with a large signal-to-noise ratio under weak THz radiation. That device was sensitive to one frequency, corresponding to transitions between equidistant quantized states. In this work we show, using numerical simulations together with scanning tunneling spectroscopy studies of a defect-induced metallic zigzag single-walled carbon nanotube quantum dot, that breaking simultaneously various symmetries in metallic nanotube quantum dots of arbitrary chirality strongly relaxes the selection rules in the electric dipole approximation and removes energy degeneracies. This leads to a richer set of allowed optical transitions spanning frequencies from 1 THz to several tens of THz, for a ∼10 nm quantum dot. Based on these findings, we propose a terahertz detector device based on a metallic single-walled carbon nanotube quantum dot defined by artificial defects. Depending on its length and contacts transparency, the operating regimes range from a high-resolution gate-tunable terahertz sensor to a broadband terahertz detector. Our calculations indicate that the device is largely unaffected by temperatures up to 100 K, making carbon nanotube quantum dots with broken symmetries a promising platform to design tunable terahertz detectors that could operate at liquid nitrogen temperatures.

en: Metallic carbon nanotube quantum dots with broken symmetries as a platform for tunable terahertz detection

ab-initio

display_name: ab-initio

carbon-nanotubes

display_name: carbon-nanotubes

cscs

display_name: cscs

snsf

display_name: snsf

terahertz-detector

display_name: terahertz-detector


en: Compared to isolated C₆₀³⁻ ions, characterized by a three-dimensional equipotential trough at the bottom of the lowest adiabatic potential energy surface (APES), the Jahn-Teller (JT) effect in cubic fullerides is additionally influenced by the interaction of JT distortions at C₆₀ sites with vibrational modes of the lattice. This leads to modification of JT stabilization energy and to the warping of the trough at each fullerene site, as well as to the interaction of JT distortions at different sites. Here we investigate these effects in three fcc fullerides with A = K, Rb, Cs and in Cs₃C₆₀ with bcc (A15) structure. DFT calculations of orbital vibronic coupling constants at C₆₀ sites and of phonon spectra have been done for fully ordered lattices (1 C₆₀/u.c.). Based on them the elastic response function for local JT distortions has been evaluated and the lowest APES investigated. To this end an expression for the latter as a function of trough coordinates of all sites has been derived. The results show that the JT stabilization energy slightly increases compared to an isolated C₆₀³⁻ and a warping of the trough of a few meV occurs. The interaction of JT distortions on nearest- and next-nearest-neighbor fullerene sites is of similar order of magnitude. These effects arise first of all due to the interaction of C₆₀ sites with the displacements of neighbor alkali atoms and are more pronounced in fcc fullerides than in the A15 compound. The results of this study support the picture of weakly hindered independent rotations of JT deformations at C₆₀ sites in cubic A₃C₆₀.

en: Jahn-Teller effect in the cubic fullerides A₃C₆₀

cubic-fullerides

display_name: cubic-fullerides

elastic-coupling

display_name: elastic-coupling

jahn-teller-effect

display_name: jahn-teller-effect

phonons

display_name: phonons