en: The chemical versatility of carbon imparts manifold properties to organic compounds, where magnetism remains one of the most desirable but elusive. Polycyclic aromatic hydrocarbons, also referred to as nanographenes, show a critical dependence of electronic structure on the topologies of the edges and the π-electron network, which makes them model systems with which to engineer unconventional properties including magnetism. In 1972, Erich Clar envisioned a bow-tie-shaped nanographene, C38H18, where topological frustration in the π-electron network renders it impossible to assign a classical Kekulé structure without leaving unpaired electrons, driving the system into a magnetically non-trivial ground state. In this record we include data needed to support our recent work where we demonstrate the experimental realization and in-depth characterization of this emblematic nanographene, known as Clar's goblet. Scanning tunnelling microscopy and spin excitation spectroscopy of individual molecules on a gold surface reveal a robust antiferromagnetic order with an exchange-coupling strength of 23 meV, exceeding the Landauer limit of minimum energy dissipation at room temperature. Through atomic manipulation, we realize switching of magnetic ground states in molecules with quenched spins. Our results provide direct evidence of carbon magnetism in a hitherto unrealized class of nanographenes, and prove a long-predicted paradigm where topological frustration entails unconventional magnetism, with implications for room-temperature carbon-based spintronics.

en: Topological frustration induces unconventional magnetism in a nanographene

ab-initio

display_name: ab-initio

clar-goblet

display_name: clar-goblet

erc

display_name: erc

kondo

display_name: kondo

nanographene

display_name: nanographene


$oid: OBSOLETE

en: The vast chemical space of metal and ligand combinations in Transition Metal Complexes (TMCs) gives rise to a rich variety of electronic excited states with local and non-local character such as intra-ligand (IL), metal-centered (MC), metal-to-ligand (MLCT) or ligand-to-metal charge-transfer (LMCT) states. Those features are equally found in Metal Organic Frameworks (MOFs), defined as modular materials built from metal-nodes connected through organic-ligands. Because of the electronic and structural complexity of MOFs, the computational description of their excited states is a formidable challenge for which two different approaches have been usually followed: the solid state and the molecular perspective. The first consists in analysing the frontier electronic bands and crystal orbitals of the electronic ground state (GS) in periodic boundary conditions, while the latter points to an accurate computation of the excited states in representative clusters at the molecular level. Herein, we apply both approaches to evaluate the optical absorption properties of three experimentally reported Ti(IV) mononuclear MOFs with in silico metal substitutions with Zn(II), Cd(II), Fe(II), Ru(II) and Zr(IV) ions, thus covering d10, d6 and d0 electronic configurations of 1st and 2nd row TMCs in MOFs. Our analysis captures the main electronic features attributed to these systems while we discuss the main advantages and drawbacks of both approximations.

en: Optical absorption properties of metal-organic frameworks: solid state versus molecular perspective

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

epfl

display_name: epfl

mof

display_name: mof

optical-absorption

display_name: optical-absorption


en:
One proposed mechanism of implant fouling is attributed to the nonspecific adsorption of non-collagenous bone matrix proteins (NCPs) onto a newly implanted interface. With the goal of capturing the fundamental mechanistic and thermodynamic forces that govern changes in these NCP recognition domains as a function of γ-carboxyglutamic acid (Gla) post-translational modification and surface chemistry, we probe the adsorption process of the most commonly occurring NCP, osteocalcin, onto a mineral and metal oxide surface. Here, we apply two enhanced sampling methods to independently probe the effects of post-translational modification and peptide structure on adsorption. First, well-tempered metadynamics was used to capture the binding of acetyl and N-methylamide capped glutamic acid and Gla single amino acids onto crystalline hydroxyapatite and titania model surfaces at physiological pH. Following this, parallel tempering metadynamics in the well-tempered ensemble (PTMetaD-WTE) was used to study adsorption of the α-1 domain of osteocalcin onto hydroxyapatite and titania. Simulations were performed for the α-1 domain of osteocalcin in both its fully decarboxylated (dOC) and fully carboxylated (OC) form. Our simulations find that increased charge density due to carboxylation results in increased interactions at the interface, and stronger adsorption of the single amino acids to both surfaces. Interestingly, the role of Gla in promoting compact and helical structure in the α-1 domain resulted in disparate binding modes at the two surfaces, which is attributed to differences in interfacial water behavior. Overall, this work provides a benchmark for understanding the mechanisms that drive adsorption of Gla-containing mineralizing proteins onto different surface chemistries.

This record contains files necessary to reproduce enhanced sampling well-tempered metadynamics (wtMTD) and parallel tempering metadynamics in the well-tempered ensemble (PT-MetaD-WTE) simulations in PLUMED using GROMACS 2018.3.

en: Impact of glutamate carboxylation in the adsorption of the alpha-1 domain of osteocalcin to hydroxyapatite and titania

biomimetics

display_name: biomimetics

biomineralization

display_name: biomineralization

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics


en: High-strength metal alloys achieve their performance via careful control of the nucleation, growth, and kinetics of precipitation. Alloy mechanical properties are then controlled by atomic scale phenomena such as shearing of the precipitates by dislocations. Atomistic modeling to understand the operative mechanisms requires length and time scales far larger than those accessible by first-principles methods. Here, a family of Behler-Parinello neural-network potentials (NNPs) for the Al-Mg-Si system is developed to enable quantitative studies of Al-6xxx alloys. The NNP is trained on metallurgically-important quantities computed by first principles density functional theory (DFT) leading to high fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, Al stacking fault energies, antisite defect energies, and other quantities. A preliminary examination of early-stage clustering kinetics and energetics in Al-6xxx is then made, showing the formation of low-energy Mg-Si structures and the trapping of vacancies in these clusters. The Generalized Stacking Fault Energy surfaces (GSFE) for the three prevalent β" precipitate compositions in peak-aged Al-6xxx are then computed with the NNP, and are validated by DFT computations at key points. The NNP thus shows significant transferability across structures, making it a powerful approach for chemically-accurate simulations of metallurgical phenomena in Al-Mg-Si alloys.

en: Machine learning for metallurgy: a neural network potential for Al-Mg-Si

aluminum

display_name: aluminum

machine-learning

display_name: machine-learning

metallurgy

display_name: metallurgy


en: In this record, we provide the data supporting our recent results on the synthesis of nitrogen-substituted undecacene analogs. Heteroatom substitution in acenes allows to tailor their remarkable electronic properties, expected to include spin-polarization and magnetism for larger members of the acene family. Here, we present a strategy for the on-surface synthesis of three undecacene analogs substituted with four nitrogen atoms on an Au(111) substrate, by employing specifically designed diethano-bridged precursors. A similarly designed precursor is used to synthesize the pristine undecacene molecule. In the publication where the results are discussed, the experimental features of scanning probe microscopy are compared with ab initio simulations, to demonstrate that the ground state of the synthesized tetraazaundecacene has considerable open-shell character on Au(111). Additionally, we demonstrate that electronegative nitrogen atoms induce a considerable shift in energy level alignment compared to the pristine undecacene, and that the introduction of hydro-aza groups causes local anti-aromaticity in the synthesized compounds. Our work provides access to the precise fabrication of nitrogen-substituted acene and its analogs, potential building-blocks of organic electronics and spintronics, and a rich playground to explore pi-electron correlation.

en: On-surface synthesis and characterization of nitrogen-substituted undecacenes

aromaticity

display_name: aromaticity

cscs

display_name: cscs

experimental

display_name: experimental

heteroacenes

display_name: heteroacenes

on-surface-synthesis

display_name: on-surface-synthesis

open-shell

display_name: open-shell

snsf

display_name: snsf


en: By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are databases with over 500,000 predicted structures. This raises the question of whether a new experimental or predicted structure adds new information. For MOF-chemists the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we show how machine learning can be used to quantify similarities of MOFs. This quantification allows us to use techniques from ecology to analyse the chemical diversity of these materials in terms of diversity metrics. In particular, we show that this diversity analysis can identify biases in the databases, and how such bias can lead to incorrect conclusions. This formalism provides us with a simple and powerful practical guideline to see whether a set of new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.

en: Understanding the diversity of the metal-organic framework ecosystem

chemical-space

display_name: chemical-space

diversity

display_name: diversity

metal-organic-frameworks

display_name: metal-organic-frameworks


en: In this record we provide data to support our recent findings for the fabrication of Unsaturated Carbon Nanostructures with Regularly Fused Pentagon–Heptagon Pairs. Multiple fused pentagon–heptagon pairs are frequently found as defects at the grain boundaries of the hexagonal graphene lattice and are suggested to have a fundamental influence on graphene-related materials. However, the construction of sp2-carbon skeletons with multiple regularly fused pentagon–heptagon pairs is challenging. In this work, we found that the pentagon–heptagon skeleton of azulene was rearranged during the thermal reaction of an azulene-incorporated organometallic polymer on Au(111). The resulting sp2-carbon frameworks were characterized by high-resolution scanning probe microscopy techniques and feature novel polycyclic architectures composed of multiple regularly fused pentagon–heptagon pairs. Moreover, the calculated analysis of its aromaticity revealed a peculiar polar electronic structure.

en: On-surface synthesis of unsaturated carbon nanostructures with regularly fused pentagon-heptagon pairs

carbon-nanostructures

display_name: carbon-nanostructures

open-shell-systems

display_name: open-shell-systems

polymers

display_name: polymers


en: The correlation between electronic and crystal structures of 1T -TiSe2 in the charge-density wave (CDW) state is studied by x-ray diffraction in order to clarify basic properties in the CDW state, transport properties, and chirality. Three families of reflections are used to probe atomic displacements and the orbital asymmetry in Se. Two distinct onset temperatures are found: TCDW and a lower T∗ indicative for an onset of Se out-of-plane atomic displacements. T∗ coincides with a DC resistivity maximum and the onset of the proposed gyrotropic (chiral) electronic structure. However, no indication for chirality is found. The relation between the atomic displacements and the transport properties is discussed in terms of Ti 3d and Se 4p states that only weakly couple to the CDW order.

en: Correlation between electronic and structural orders in 1T-TiSe2

charge-density-wave

display_name: charge-density-wave

crystal-symmetry

display_name: crystal-symmetry

electrical-conductivity

display_name: electrical-conductivity

marvel

display_name: marvel

paul-scherrer-institute

display_name: paul-scherrer-institute

resonant-elastic-x-ray-scattering

display_name: resonant-elastic-x-ray-scattering

structural-order-parameter

display_name: structural-order-parameter

transition-metal-dichalcogenide

display_name: transition-metal-dichalcogenide

x-ray-diffraction

display_name: x-ray-diffraction


en: We investigate the effect of charge self-consistency (CSC) in density-functional theory plus dynamical mean-field theory calculations compared to simpler "one-shot" calculations for materials where interaction effects lead to a strong redistribution of electronic charges between different orbitals or between different sites. We focus on two systems close to a metal-insulator transition (MIT), for which the importance of CSC is currently not well understood. Specifically, we analyze the strain-related orbital polarization in the correlated metal CaVO3 and the spontaneous electronic charge disproportionation in the rare-earth nickelate LuNiO3. In both cases, we find that the CSC treatment reduces the charge redistribution compared to cheaper one-shot calculations. However, while the MIT in CaVO3 is only slightly shifted due to the reduced orbital polarization, the effect of the site polarization on the MIT in LuNiO3 is more subtle. Furthermore, we highlight the role of the double-counting correction in CSC calculations containing different inequivalent sites.

en: Effect of charge self-consistency in DFT+DMFT calculations for complex transition metal oxides

dftdmft

display_name: dftdmft

dynamical-mean-field-theory

display_name: dynamical-mean-field-theory

electronic-properties-and-materials

display_name: electronic-properties-and-materials

electronic-structure

display_name: electronic-structure


en: One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.

en: The role of water in host-guest interaction

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).

enhanced-sampling

display_name: enhanced-sampling

erc

display_name: erc

host-guest

display_name: host-guest

ligand-binding

display_name: ligand-binding

molecular-dynamics

display_name: molecular-dynamics

neural-network

display_name: neural-network

snsf

display_name: snsf

water

display_name: water


en: The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been proposed, ranging from simple linear models to kernel regression and highly nonlinear neural networks. Here we apply two widely different approaches to the same, challenging problem - the sampling of the conformational landscape of polypeptides at finite temperature. We develop a Local Kernel Regression (LKR) coupled with a supervised sparsity method and compare it with a more established approach based on Behler-Parrinello type Neural Networks. In the context of the LKR, we discuss how the supervised selection of the reference pool of environments is crucial to achieve accurate potential energy surfaces at a competitive computational cost and leverage the locality of the model to infer which chemical environments are poorly described by the DFTB baseline. We then discuss the relative merits of the two frameworks and perform Hamiltonian-reservoir replica-exchange Monte Carlo sampling and metadynamics simulations, respectively, to demonstrate that both frameworks can achieve converged and transferable sampling of the conformational landscape of complex and flexible biomolecules with comparable accuracy and computational cost.

en: Local kernel regression and neural network approaches to the conformational landscapes of oligopeptides

materialscloud

en: Materials Cloud

envi

en: Environment

behler-parrinello-neural-network

display_name: behler-parrinello-neural-network

epfl

display_name: epfl

hamiltonian-replica-exchange

display_name: hamiltonian-replica-exchange

local-kernel-regression

display_name: local-kernel-regression

machine-learning

display_name: machine-learning

oligopeptides

display_name: oligopeptides

supervised-sparsity

display_name: supervised-sparsity


en: This work presents extensive first-principles (Car-Parrinello) molecular dynamics simulations of the solid-state electrolyte Li10GeP2O12 (LGPO) in a tetragonal phase -not synthesized so far- that is isostructural to the highly Li-ion conductive tetragonal phase of the sulfide analogue Li10GeP2S12 (LGPS). We provide comparative simulations of the experimentally known orthorhombic phase of LGPO (that we call here LISICON, from the family of superionic conductors to which LGPO belongs) and of the two experimentally known phases of LGPS, quasi-orthorhombic (called thio-LISICON) and tetragonal. We extract diffusion coefficients from fixed-cell simulations in the canonical ensemble and we study dynamical stability from variable-cell simulations in the isobaric-isothermal ensemble. The main outcome of this work is that, according to these simulations, although tetragonal LGPO is less stable than its orthorhombic allotrope, it exhibits a much higher conductivity, comparable to that presently estimated for both phases of LGPS. These results indicate that hypothetical tetragonal LGPO, if synthesized, could make a very attractive Li-ion conductor.

en: High Li-ion conductivity in tetragonal LGPO: a comparative first-principles study against known LISICON and LGPS phases

big-map

display_name: big-map

cscs

display_name: cscs

dynamical-stability

display_name: dynamical-stability

first-principles-molecular-dynamics

display_name: first-principles-molecular-dynamics

ionic-conductivity

display_name: ionic-conductivity

ionic-transport

display_name: ionic-transport

lgpo

display_name: lgpo

lgps

display_name: lgps

lisicon

display_name: lisicon

solid-state-electrolytes

display_name: solid-state-electrolytes


en:
Results of an ab initio structure search on the H+C+N+O quaternary space at 500GPa.

The solar system's outer planets, and many of their moons, are dominated by matter from the H–C–N–O chemical space, based on solar system abundances of hydrogen and the planetary ices H2O, CH4 , and NH3 . In the planetary interiors, these ices will experience extreme pressure conditions, around 5 Mbar at the Neptune mantle–core boundary, and it is expected that they undergo phase transitions, decompose, and form entirely new compounds. While temperature will dictate the formation of compounds, ground- state density functional theory allows us to probe the chemical effects resulting from pressure alone. These structural developments in turn determine the planets' interior structures, thermal evolution, and magnetic field generation, among others. Despite its importance, the H–C–N–O system has not been surveyed systematically to explore which compounds emerge at high-pressure conditions, and what governs their stability. Here, we report on and analyze an unbiased crystal structure search among H–C–N–O compounds between 1 and 5 Mbar. We demonstrate that simple chemical rules drive stability in this composition space, which explains why the simplest possible quaternary mixture HCNO—isoelectronic to diamond—emerges as a stable compound and discuss dominant decomposition products of planetary ice mixtures.

en: Rules of formation of H–C–N–O compounds at high pressure and the fates of planetary ices

ab-initio-random-structure-searching

display_name: ab-initio-random-structure-searching

h-c-n-o-chemistry

display_name: h-c-n-o-chemistry

high-pressure

display_name: high-pressure

planetary-ices

display_name: planetary-ices


en: A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences. In pursuit of this goal, neural message passing has lead to a paradigm shift by describing many-body correlations of atoms through iteratively passing messages along an atomistic graph. This propagation of information, however, makes parallel computation difficult and limits the length scales that can be studied. Strictly local descriptor-based methods, on the other hand, can scale to large systems but do not currently match the high accuracy observed with message passing approaches. This work introduces Allegro, a strictly local equivariant deep learning interatomic potential that simultaneously exhibits excellent accuracy and scalability of parallel computation. Allegro learns many-body functions of atomic coordinates using a series of tensor products of learned equivariant representations, but without relying on message passing. Allegro obtains improvements over state-of-the-art methods on the QM9 and revised MD-17 data sets. A single tensor product layer is shown to outperform existing deep message passing neural networks and transformers on the QM9 benchmark. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular dynamics simulations based on Allegro recover structural and kinetic properties of an amorphous phosphate electrolyte in excellent agreement with first principles calculations. Finally, we demonstrate the parallel scaling of Allegro with a dynamics simulation of 100 million atoms.

en: Learning local equivariant representations for large-scale atomistic dynamics

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en: We present a first-principles investigation of the structural, electronic, and magnetic properties of pyrolusite (β-MnO2) using conventional and extended Hubbard-corrected density-functional theory (DFT+U and DFT+U+V). The onsite U and intersite V Hubbard parameters are computed using linear-response theory in the framework of density-functional perturbation theory. We show that while the inclusion of the onsite U is crucial to describe the localized nature of the Mn(3d) states, the intersite V is key to capture accurately the strong hybridization between neighboring Mn(3d) and O(2p) states. In this framework, we stabilize the simplified collinear antiferromagnetic (AFM) ordering (suggested by the Goodenough-Kanamori rule) that is commonly used as an approximation to the experimentally-observed noncollinear screw-type spiral magnetic ordering. A detailed investigation of the ferromagnetic and of other three collinear AFM spin configurations is also presented. The findings from Hubbard-corrected DFT are discussed using two kinds of Hubbard manifolds -- nonorthogonalized and orthogonalized atomic orbitals -- showing that special attention must be given to the choice of the Hubbard projectors, with orthogonalized manifolds providing more accurate results than nonorthogonalized ones within DFT+U+V. This work paves the way for future studies of complex transition-metal compounds containing strongly localized electrons in the presence of pronounced covalent interactions.

en: Importance of intersite Hubbard interactions in β-MnO2: A first-principles DFT+U+V study

band-gap

display_name: band-gap

crystal-structure

display_name: crystal-structure

density-of-states

display_name: density-of-states

dftu

display_name: dftu

dftuv

display_name: dftuv

goodenough-kanamori-rule

display_name: goodenough-kanamori-rule

hubbard-parameters

display_name: hubbard-parameters

hubbard-projectors

display_name: hubbard-projectors

magnetic-moment

display_name: magnetic-moment

marvelosp

display_name: marvelosp

nonorthogonalized-atomic-orbitals

display_name: nonorthogonalized-atomic-orbitals

orthogonalized-atomic-orbitals

display_name: orthogonalized-atomic-orbitals

self-interactions

display_name: self-interactions

snsf

display_name: snsf

spin-configuration

display_name: spin-configuration


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

density-functional-theory

display_name: density-functional-theory

high-throughput

display_name: high-throughput

machine-learning

display_name: machine-learning


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.

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

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

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

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).

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

materialscloud

en: Materials Cloud

pbe

display_name: pbe


en: The paper reports on a theoretical description of work function of TiN, which is one of the most used materials for the realization of electrodes and gates in CMOS devices. Indeed, although the work function is a fundamental quantity in quantum mechanics and also in device physics, as it allows the understanding of band alignment at heterostructures and gap states formation at the metal/semiconductor interface, the role of defects and contaminants is rarely taken into account. Here, by using first principles simulations, we present an extensive study of the work function dependence on nitrogen vacancies and surface oxidation for different TiN surface orientations. The results complement and explain a number of existent experimental data, and provide a useful tool to tailoring transport properties of TiN electrodes in device simulations.

en: Controlling the TiN electrode work function at the atomistic level: a first principles investigation

envi

en: Environment

defects

display_name: defects

dft

display_name: dft

electrodes

display_name: electrodes

gates

display_name: gates

intersect

display_name: intersect

oxidation

display_name: oxidation

titanium-nitride

display_name: titanium-nitride


en: In the present record we provide the theoretical calculations used in the article: G. Gatti et al., Radial Spin Texture of the Weyl Fermions in Chiral Tellurium, Phys. Rev. Lett. 125, 216402. It consist of a detailed analysis of the electronic structure and spin textures at different points of the Brillouin zone. We provide the band structure and spin expectation values along different high-symmetry lines, Fermi surface contour plots in the ΓMLA plane, and spin texture in small sphere around two points of the Brillouin zone. The data is provided as the output format of the Quantum Espresso package. We also include the pseudopotential used in these calculations.

en: Radial spin texture of the Weyl fermions in chiral tellurium

chiral-symmetry

display_name: chiral-symmetry

cscs

display_name: cscs

electronic-structure

display_name: electronic-structure

erc

display_name: erc

snsf

display_name: snsf

spin-texture

display_name: spin-texture

topological-materials

display_name: topological-materials


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

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


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: Sustainability combined with high energy density prospects makes Fe-based oxides attractive as cathodes for calcium rechargeable batteries. This work presents a DFT evaluation of the CaFe2+nO4+n (0 < n < 3) family, for which both the average intercalation voltage and the theoretical specific capacity decrease with the increasing n value. The term n = 1/4, Ca4Fe9O17, meets the most appealing characteristics: a calculated average voltage of 4.16 V, a theoretical specific capacity of 230 mA h g−1 and the lowest energy barrier for Ca migration so far predicted for an existing oxide (0.72 eV). To overcome the previously reported synthesis difficulties, we employed a novel synthesis procedure in sealed quartz tubes followed by quenching in water. The XRD and SAED patterns of the prepared Ca4Fe9O17 powder reveal a certain degree of stacking defects along the c axis. Attempts to deinsert Ca ions from Ca4Fe9O17 by chemical means (NO2BF4 in ACN) and in electrochemical Ca cells were unsuccessful, although some hints of oxidation are observed in Li cells with the LP30 electrolyte. The suitability of Ca4Fe9O17 as a Ca cathode is pending further studies utilizing Ca-electrolytes with high anodic stability.

en: Appraisal of calcium ferrites as cathodes for calcium rechargeable batteries: DFT, synthesis, characterization and electrochemistry of Ca4Fe9O17

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).

batteries

display_name: batteries

calcium

display_name: calcium

cathode-material

display_name: cathode-material

ferrites

display_name: ferrites


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

materialscloud

en: Materials Cloud

electron-phonon-coupling

display_name: electron-phonon-coupling

electronic-bands

display_name: electronic-bands

kondo-system

display_name: kondo-system

snsf

display_name: snsf


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

envi

en: Environment

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: 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.

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

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

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).

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

materialscloud

en: Materials Cloud

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: Ice templated anisotropic foams based on cellulose nanocrystals (CNC) with densities ranging between 25 to 130 kg.m-3 were prepared from aqueous CNC dispersions. The thermal conductivities perpendicular to the columnar macropores direction increased in a non-monotonous way with the increasing CNC foam density while the thermal conductivity reached a minimum value for the CNC foam with the highest nanoporosity. Theoretical calculations including the solid and gas conduction within the foams as well as the thermal conductivity of water showed that phonon scattering at the solid-solid interfaces is responsible for reaching low thermal conductivity values. The foam wall nanoporosity, the particle alignment, the macropores orientation and the foam wall thickness seem to have a minimal effect on the thermal conductivity but can explain the deviations between the theoretical estimates and the experimental data. To this end the identification of the important influencing factors and the great importance of phonon scattering paves the way to design new fossil-free nanofibrillar foams with superinsulating properties.

en: Effect of density, phonon scattering and nanoporosity on the thermal conductivity of anisotropic cellulose nanocrystal foams

envi

en: Environment

cellulose-nanocrystals

display_name: cellulose-nanocrystals

cellulose-nanomaterials

display_name: cellulose-nanomaterials

foams

display_name: foams

moisture-uptake

display_name: moisture-uptake

thermal-conductivity

display_name: thermal-conductivity


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

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning

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.

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

aiida-kkr

display_name: aiida-kkr

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

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

materialscloud

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

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

ab-initio

display_name: ab-initio

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:
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

materialscloud

en: Materials Cloud

first-principles

display_name: first-principles

hybrid-functional-dft

display_name: hybrid-functional-dft

marvel

display_name: marvel

molecular-crystals

display_name: molecular-crystals


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

envi

en: Environment

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: Molecular dynamics (MD) simulations are performed to obtain mode I and II fracture energies and cohesive laws for bulk epoxy and interfaces formed between epoxy and single-layer graphene (SLG), multilayer graphene (MLG), and multilayer graphene oxide (MLGO). The elastic moduli and ultimate tensile and shear strengths of epoxy--graphene interfaces are calculated from uniaxial tension and simple shear loadings. The results show that Young's modulus and the ultimate tensile strength increase relative to bulk epoxy, whereas the shear modulus and ultimate shear strength are reduced. Failure of epoxy--graphene interfaces in tension occurs due to the formation of voids in the epoxy. Failure in shear is due to tangential slipping at the interface. Under mixed mode conditions, the shear modulus and shear strength decrease with increasing tensile load. The critical energy release rate G_c for the studied epoxy--SLG/MLG/MLGO systems are obtained using a continuum fracture mechanics approach and are found to be significantly lower than for bulk epoxy. All of the results are combined to define mode I and II cohesive laws for bulk epoxy and epoxy--SLG/MLG/MLGO interfaces that can be used in theoretical models and numerical methods, such as finite elements, that employ cohesive zones.

en: Molecular dynamics based cohesive law for epoxy-graphene interfaces

cohesive-law

display_name: cohesive-law

graphene-reinforced-epoxy-composite

display_name: graphene-reinforced-epoxy-composite

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


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: Magneto-electric multipoles, which are odd under both space-inversion 𝓘 and time-reversal 𝓣 symmetries, are fundamental in understanding and characterizing magneto-electric materials. However, the detection of these magneto-electric multipoles is often not straightforward as they remain "hidden" in conventional experiments in part since many magneto-electrics exhibit combined 𝓘𝓣 symmetry. In the present work, we show that the anti-symmetric Compton profile is a unique signature for all the magneto-electric multipoles, since the asymmetric magnetization density of the magneto-electric multipoles couples to space via spin-orbit coupling, resulting in an anti-symmetric Compton profile. We develop the key physics of the anti-symmetric Compton scattering using symmetry analysis and demonstrate it using explicit first-principles calculations for two well-known representative materials with magneto-electric multipoles, insulating LiNiPO₄ and metallic Mn₂Au. Our work emphasizes the crucial roles of the orientation of the spin moments, the spin-orbit coupling, and the band structure in generating the anti-symmetric Compton profile in magneto-electric materials.

en: Revealing hidden magneto-electric multipoles using Compton scattering

compton-scattering

display_name: compton-scattering

erc

display_name: erc

horizon-europe

display_name: horizon-europe

magneto-electric-multipoles

display_name: magneto-electric-multipoles


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.

en: Assessment of approximate methods for anharmonic free energies

crystal-structure-prediction

display_name: crystal-structure-prediction

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

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).

acid-base

display_name: acid-base

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics

zeolites

display_name: zeolites


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

materialscloud

en: Materials Cloud

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

envi

en: Environment

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


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.

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

anti-perovskites

display_name: anti-perovskites

electron-phonon-coupling

display_name: electron-phonon-coupling

superconductivity

display_name: superconductivity