en: Weyl semimetals in three-dimensional crystals provide the paradigm example of topologically protected band nodes. It is usually taken for granted that a pair of colliding Weyl points annihilate whenever they carry opposite chiral charges. In stark contrast, here we report that Weyl points in systems symmetric under the composition of time-reversal with a π-rotation are characterized by a non-Abelian topological invariant. The topological charges of the Weyl points are transformed via braid phase factors which arise upon exchange inside symmetric planes of the reciprocal momentum space. We elucidate this process with an elementary two-dimensional tight-binding model implementable in cold-atoms setups and in photonic systems. In three dimensions, interplay of the non-Abelian topology with point-group symmetry is shown to enable topological phase transitions in which pairs of Weyl points may scatter or convert into nodal-line rings. By combining our theoretical arguments with first-principles calculations, we predict that Weyl points occurring near the Fermi level of zirconium telluride (ZrTe) carry non-trivial values of the non-Abelian charge, and that uniaxial compression strain drives a non-trivial conversion of the Weyl points into nodal lines.

en: Non-Abelian reciprocal braiding of Weyl points and its manifestation in ZrTe

non-abelian-braiding

display_name: non-abelian-braiding

snsf

display_name: snsf

taas

display_name: taas

weyl-semimetal

display_name: weyl-semimetal

zrte

display_name: zrte


$oid: OBSOLETE

en: Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective training set. In this work, we implement and train a MLP to obtain an accurate description of the potential energy surface and property predictions for organic compounds, as both single molecules and in the condensed phase. We devise a dual descriptor, based on the atomic cluster expansion (ACE), that couples an information-rich short-range description with a coarser long-range description that captures weak intermolecular interactions. We employ uncertainty-guided active learning for the training set generation, creating a dataset that is comparatively small for the breadth of application and consists of alcohols, alkanes, and an adipate. Utilizing that MLP, we calculate densities of those systems of varying chain lengths as a function of temperature, obtaining a discrepancy of less than 4% compared with experiment. Vibrational frequencies calculated with the MLP have a root mean square error of less than 1 THz compared to DFT. The heat capacities of condensed systems are within 11% of experimental findings, which is strong evidence that the dual descriptor provides an accurate framework for the prediction of both short-range intramolecular and long-range intermolecular interactions.

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

en: A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

diffusion

display_name: diffusion

heat-capacity

display_name: heat-capacity

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics

radial-distribution-functions

display_name: radial-distribution-functions

training-data

display_name: training-data

vasp

display_name: vasp


en: High power SiC MOSFET technologies are critical for energy saving in, e.g., distribution of electrical power. They suffer, however, from low near-interface mobility, the origin of which has not yet been conclusively determined. Here, we present unique concerting evidence for the presence of interface defects in the form of carbon clusters at native thermally processed oxides of SiC. These clusters, with a diameter of 2–5 nm, are HF-etch resistant and possess a mixture of graphitic (sp2) and amorphous (sp3 mixed in sp2) carbon bonds different from the normal sp3 carbon present in 4H-SiC. The nucleation of such defects during thermal oxidation as well as their atomic structure is elucidated by state-of-the-art atomistic and electronic structure calculations. In addition, our property prediction techniques show the impact of the simulated carbon accumulates on the electronic structure at the interface.

en: Evidence for carbon clusters present near thermal gate oxides affecting the electronic band structure in SiC-MOSFET

microfabrication

display_name: microfabrication

mosfet

display_name: mosfet


en: Density functional theory calculations were used to obtain the generalized stacking fault energy curves in six BCC metals: Cr, Mo, Nb, Ta, V, and W. Among them, antiferromagnetism was considered only in Cr. Results based on non-magnetic Cr are denoted as Cr-NM.

en: Generalized stacking fault energy curves for six body-centered cubic refractory metals

body-centered-cubic-refractory-metals

display_name: body-centered-cubic-refractory-metals

density-functional-theory

display_name: density-functional-theory

generalized-stacking-fault-energies

display_name: generalized-stacking-fault-energies


en: Unbiased molecular dynamics simulation was used to simulate systems containing hevein (HEV32) domain and mono-, di- or trisaccharide of GlcNAc. Carbohydrate molecules were placed outside the binding site. Three of six simulations led to formation of a carbohydrate-protein complex. Trajectories are available without water and are sampled every 100 ps. This study is one of the first application of docking by dynamics concept on carbohydrate-protein interactions.

en: Atomistic simulation of carbohydrate-protein complex formation: Hevein-32 domain

carbohydrate-protein-interactions

display_name: carbohydrate-protein-interactions

docking-by-dynamics

display_name: docking-by-dynamics

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


en: Electron energy bands, which are studied to explain electronic and optical properties of crystalline solids, often exhibit degeneracies called band-structure nodes. Here, we introduce non-Abelian topological charges that characterize line nodes inside the momentum space of -symmetric crystalline metals with weak spin-orbit coupling. We show that these are quaternion charges, similar to those describing disclinations in biaxial nematics. Starting from two-band considerations, we develop the complete many-band description of nodes in the presence of and mirror symmetries, which allows us to investigate the topological stability of nodal chains in metals. The non-Abelian charges put strict constraints on the possible nodal line configurations. Our analysis goes beyond the standard approach to band topology and implies the existence of 1D topological phases not present in existing classifications.

en: Supplementary Data for "Non-Abelian band topology in noninteracting metals"

band-topology

display_name: band-topology

marvel

display_name: marvel

model-hamiltonian

display_name: model-hamiltonian

non-abelian

display_name: non-abelian

pt-symmetric-crystalline-metals

display_name: pt-symmetric-crystalline-metals

topological-materials

display_name: topological-materials

wanniertools

display_name: wanniertools

weak-spin-orbit-coupling

display_name: weak-spin-orbit-coupling


en: Metal-organic frameworks (MOFs) consist of metal nodes that are connected by organic linkers. They are thus highly chemically tunable materials given the broad range of potential linkers and nodes that can be chosen for their synthesis. Their tunability has recently sparked interest for the development of new MOF photo-catalysts for energy-related applications such as hydrogen (H2) evolution and CO2 reduction. The sheer amount of potentially synthesizable MOFs requires to define descriptors that allow to predict their performance with this aim. Herein we propose a systematic computational protocol to determine two energy-based descriptors that are directly related to the performance of a MOF as a photocatalyst. These descriptors assess the UV-vis light absorption capability and the band energy alignment with respect to redox processes and/or co-catalysts energy levels. High-throughput screening based on cost-effective computations of these features is envisioned to aid the discovery of new promising photoactive systems.

en: Energy-based Descriptors for Photo-Catalytically Active Metal-Organic Frameworks Discovery

mof

display_name: mof

photocatalysis

display_name: photocatalysis

screening

display_name: screening


en: The lighting of the future is expected to be light weight, flexible, highly efficient, non-expensive and fabricated in an environmentally friendly way. Organic light emitting diodes meet all these requirements and can be fabricated using inexpensive and roll-to-roll compatible printing techniques. They however often use low work function, highly reactive metals, such as barium and calcium to facilitate electron injection, deposited using expensive and non-continuous vacuum techniques. Efficient and stable alternatives can be found in the aliphatic amines, polyethylenimine and polyethylenimine(ethoxylated), which shift the work function of aluminum favorably for electron injection. This work demonstrates ultrasonic spray coating of polyethylenimine and polyethylenimine(ethoxylated) as electron injection and transport layer for OLEDs, reducing the work function of the aluminum cathode by 0.355 eV allowing a luminous efficacy comparable to that of the OLEDs using calcium/aluminum electrodes. Slightly higher luminous results are noted for the OLEDs with spin coated PEIE, indicating that the surface morphology and thickness of the polyethylenimine(ethoxylated) layer are crucial factors: ultrasonic spray coated polyethylenimine(ethoxylated) layers have an increased overall thickness and surface roughness. This study confirms the versatility of ultrasonic spray coating and the suitability of polyethylenimine(ethoxylated) as excellent electron injection and transport layer for OLEDs and paves the way towards fully spray coated OLEDs. The produced OLEDs were measured using a Keithley 2401 source meter (Keithley Instruments, Cleveland, Ohio, USA) to obtain the current and voltage characteristics and by using an absolute calibrated integrating sphere spectrometer from Avantes (Apeldoorn, The Netherlands) to acquire the luminous flux. The luminous efficacy was then calculated by dividing the luminous flux by the used electrical power. This data is presented here.

en: Ultrasonic spray coating of polyethylenimine (ethoxylated) as electron injection and transport layer for organic light emitting diodes

electron-injection-and-transport-layer

display_name: electron-injection-and-transport-layer

experimental

display_name: experimental

organic-light-emitting-diodes

display_name: organic-light-emitting-diodes

polyethylenimine-ethoxylated

display_name: polyethylenimine-ethoxylated

printing-technique

display_name: printing-technique

ultrasonic-spray-coating

display_name: ultrasonic-spray-coating


en: The energy level alignment across solvated molecule/semiconductor interfaces is a crucial property for the correct functioning of dye-sensitized photo-electrodes, where, following the absorption of solar light, a cascade of interfacial hole/electron transfer processes has to efficiently take place. In light of the difficulty of performing X-ray photoelectron spectroscopy measurements at the molecule/solvent/metal-oxide interface, being able to accurately predict the level alignment by first-principles calculations on realistic structural models would represent an important step toward the optimization of the device. In this respect dye/NiO surfaces, employed in p-type dye-sensitized solar cells, are undoubtedly challenging for ab initio methods and, also for this reason, much less investigated than the n-type dye/TiO2 counterpart. Here we consider the C343-sensitized NiO surface in water and combine ab initio Molecular Dynamics (AIMD) simulations with GW (G0W0) calculations, performed along the MD trajectory, to reliably describe the structure and energetics of the interface when explicit solvation and finite temperature effects are accounted for. We show that the differential perturbative correction on the NiO and molecule states obtained at GW level is mandatory to recover the correct (physical) interfacial energetics, allowing hole transfer from the semiconductor valence band to the HOMO of the dye. Moreover, the calculated average driving force quantitatively agrees with the experimental estimate.

en: Structure and energetics of dye-sensitized NiO interfaces in water from ab-initio MD and large-scale GW calculations

ab-initio

display_name: ab-initio

energy-level-alignment

display_name: energy-level-alignment

gw

display_name: gw

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nio

display_name: nio

p-type-dssc

display_name: p-type-dssc


en: There is much current work on metallic glasses (MGs). The field is making rapid advances and has opened up questions of fundamental scientific interest. Metallic glasses are known to suffer from poor formability. Among other methods of improving the mechanical properties of MGs, introducing deformable crystalline phases into MGs is beneficial for enhancing the plastic compliance of MGs. The definition of the principal phase transformations (on heating and on cooling) underlying the feasibility of such a method is the focus of the deposited in situ high-energy XRD data carried out at Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany. This archive entry contains the temporal evolution of the equilibrium and metastable phases on flash-annealing (heating and cooling) and during containerless solidification via electromagnetic levitation with an unprecedented timescale of ~4 ms.

materialscloud

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

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

en: Materials Cloud

en: In situ high-energy X-ray diffraction of a CuZr-based metallic glass

containerless-solidification

display_name: containerless-solidification

electromagnetic-levitation

display_name: electromagnetic-levitation

metallic-glasses

display_name: metallic-glasses

phase-transformation-mechanisms

display_name: phase-transformation-mechanisms

synchrotron-xrd

display_name: synchrotron-xrd


en: The renormalization of electronic eigenenergies due to electron-phonon interactions (temperature dependence and zero-point motion effect) is important in many materials. We address it in the adiabatic harmonic approximation, based on first principles (e.g., density-functional theory), from different points of view: directly from atomic position fluctuations or, alternatively, from Janak's theorem generalized to the case where the Helmholtz free energy, including the vibrational entropy, is used. We prove their equivalence, based on the usual form of Janak's theorem and on the dynamical equation. We then also place the Allen-Heine-Cardona (AHC) theory of the renormalization in a first-principles context. The AHC theory relies on the rigid-ion approximation, and naturally leads to a self-energy (Fan) contribution and a Debye-Waller contribution. Such a splitting can also be done for the complete harmonic adiabatic expression, in which the rigid-ion approximation is not required. A numerical study within the density-functional perturbation theory framework allows us to compare the AHC theory with frozen-phonon calculations, with or without the rigid-ion approximation. For the two different numerical approaches without non-rigid-ion terms, the agreement is better than 7 μeV in the case of diamond, which represent an agreement to five significant digits. The magnitude of the non-rigid-ion terms in this case is also presented, distinguishing specific phonon modes contributions to different electronic eigenenergies.

en: Temperature dependence of electronic eigenenergies in the adiabatic harmonic approximation

envi

en: Environment

adiabatic-harmonic-approximation

display_name: adiabatic-harmonic-approximation

allen-heine-cardona-theory

display_name: allen-heine-cardona-theory

ceci

display_name: ceci

diamond

display_name: diamond

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

frqnt

display_name: frqnt

frs-fnrs

display_name: frs-fnrs

temperature-dependence

display_name: temperature-dependence

verification-and-validation

display_name: verification-and-validation

zero-point-motion-renormalization

display_name: zero-point-motion-renormalization


en: Fractionalization is a phenomenon in which strong interactions in a quantum system drive the emergence of excitations with quantum numbers that are absent in the building blocks. Outstanding examples are excitations with charge e/3 in the fractional quantum Hall effect, solitons in one-dimensional conducting polymers and Majorana states in topological superconductors. Fractionalization is also predicted to manifest itself in low-dimensional quantum magnets, such as one-dimensional antiferromagnetic S = 1 chains. The fundamental features of this system are gapped excitations in the bulk and, remarkably, S = 1/2 edge states at the chain termini, leading to a four-fold degenerate ground state that reflects the underlying symmetry-protected topological order. This record contains data to support the result in a recent publication of ours where we use on-surface synthesis to fabricate one-dimensional spin chains that contain the S = 1 polycyclic aromatic hydrocarbon triangulene as the building block. Using scanning tunneling microscopy and spectroscopy at 4.5 K, we probe length-dependent magnetic excitations at the atomic scale in both open-ended and cyclic spin chains, and directly observe gapped spin excitations and fractional edge states therein. Exact diagonalization calculations provide conclusive evidence that the spin chains are described by the S = 1 bilinear-biquadratic Hamiltonian in the Haldane symmetry-protected topological phase. Our results open a bottom-up approach to study strongly correlated phases in purely organic materials, with the potential for the realization of measurement-based quantum computation.

en: Observation of fractional edge excitations in nanographene spin chains

cscs

display_name: cscs

erc

display_name: erc

fractionalization

display_name: fractionalization

graphene-flagship

display_name: graphene-flagship

nanographene

display_name: nanographene

snsf

display_name: snsf

spin-chains

display_name: spin-chains


en: Artificial muscle materials promise incredible applications in actuators, robotics and medical apparatus, yet the ability to mimic the full characteristics of skeletal muscles into synthetic materials remains a huge challenge. Herein, inspired by the dynamic sacrificial bonds in biomaterials and the self-strengthening of skeletal muscles by physical exercise, high performance artificial muscle material is prepared by rearrangement of sacrificial coordination bonds in the polyolefin elastomer via a repetitive mechanical training process. Biomass lignin is incorporated as a green reinforcer for the construction of interfacial coordination bonds. The prepared artificial muscle material exhibits high actuation strain (>40%), high actuation stress (1.5 MPa) which can lift more than 10000 times its own weight with 30% strain, characteristics of excellent self-strengthening by mechanical training, strain-adaptive stiffening, and heat/electric programmable actuation performance. In this work, we show a facile strategyfor the fabrication of intelligent materials using easily available raw materials.

en: Biomimetic high performance artificial muscle built on sacrificial coordination network and mechanical training process

artificial-muscle-materials

display_name: artificial-muscle-materials

lignin

display_name: lignin

mechanical-training

display_name: mechanical-training

programmable-actuation

display_name: programmable-actuation

sacrificial-coordination-bonds

display_name: sacrificial-coordination-bonds

self-strengthening

display_name: self-strengthening

strain-adaptive-stiffening

display_name: strain-adaptive-stiffening


en: The on-top pair density [Π(r)] is a local quantum chemical property, which reflects the probability of two electrons of any spin to occupy the same position in space. Simplest quantity related to the two-particles density matrix, the on-top pair density is a powerful indicator of electron correlation effects and, as such, it has been extensively used to combine density functional theory and multireference wavefunction theory. The widespread application of Π(r) is currently hindered by the need for post-Hartree-Fock or multireference computations for its accurate evaluation. In this work, we propose the construction of a machine learning model capable of predicting the CASSCF-quality on-top pair density of a molecule only from its structure and composition. Our model, trained on the GDB11-AD-3165 database, is able to predict with minimal error the on-top pair density of organic molecules bypassing completely the need for ab-initio computations. The accuracy of the regression is demonstrated using the on-top ratio as a visual metric of electron correlation effects and bond-breaking in real-space. In addition, we report the construction of a specialized basis set, built to fit the on-top pair density in a single, atom-centered expansion. This basis, cornerstone of the regression, could be potentially used also in the same spirit of the resolution-of-the-identity approximation for the electron density.

en: Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation

epfl

display_name: epfl

machine-learning

display_name: machine-learning

on-top-pair-density

display_name: on-top-pair-density

strong-electron-correlation

display_name: strong-electron-correlation


en: Hematite as promising photoanode for solar water splitting suffers from severe bulk and surface charge recombination. This work describes that a protonation−annealing treatment can effectively suppress both bulk and surface charge recombination in hematite. Protons/electrons are electrochemically incorporated into hematite under 0.2 VRHE followed by annealing at 120 °C. The photocurrent density increases from ∼0.9 to 1.8 mA cm−2 at 1.23 VRHE under 1 sun, and further to 2.7 mA cm−2 after loading cobalt phosphate, stabilizing at round 2.4 mA cm−2. A cathodic shift of the onset potential of photocurrent is also observed. H2O2 oxidation, impedance spectroscopy, and Mott−Schottky measurements show that the protonation suppresses bulk recombination and enhances donor density, but introducing more surface recombination. The annealing reduces surface recombination, while preserving relatively high bulk charge separation efficiency. Different from previous reports on the electrochemically reduced hematite, this work demonstrates that the performance improvement should be ascribed to the proton incorporation instead of the formation of Fe3O4 or metal Fe. This facile treatment by protonation and annealing could be applied in other semiconductors to promote the development of high performing photoelectrodes.

en: Suppressed charge recombination in hematite photoanode via protonation and annealing

hematite-photoanode

display_name: hematite-photoanode

marvel

display_name: marvel

photoelectrochemistry

display_name: photoelectrochemistry

solar-water-splitting

display_name: solar-water-splitting


en: We performed calculations of the electronic band structure and the Fermi surface as well as measured the longitudinal resistivity ρxx(T,H), Hall resistivity ρxy(T,H), and quantum oscillations of the magnetization as a function of temperature at various magnetic fields for MoO2 with a monoclinic crystal structure. The band structure calculations show that MoO2 is a nodal-line semimetal when the spin-orbit coupling is ignored. It was found that a large magnetoresistance reaching 5.03 × 10^4% at 2 K and 9 T, its nearly quadratic field dependence, and a field-induced up-turn behavior of ρxx(T), the characteristics common for many topologically nontrivial as well as trivial semimetals, emerge also in MoO2. The observed properties are attributed to a perfect charge-carrier compensation, evidenced by both calculations relying on the Fermi surface topology and the Hall resistivity measurements. Both the observation of negative magnetoresistance for the magnetic field along the current direction and the nonzero Berry phase in de Haas–van Alphen measurements indicate that pairs of Weyl points appear in MoO2, which may be due to the crystal symmetry breaking. These results highlight MoO2 as a platform for studying the topological properties of oxides.

en: Large magnetoresistance and nonzero Berry phase in the nodal-line semimetal MoO2

berry-phase

display_name: berry-phase

cscs

display_name: cscs

magnetoresistance

display_name: magnetoresistance


en: Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme—the "zeolite sorting hat"—that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the zeolite sorting hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the zeolite sorting hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the zeolite sorting hat. Finally, we analyze the behavior of the zeolite sorting hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si-O distances around 3.2–3.4 Å as the key discriminatory factor.

en: Ranking the synthesizability of hypothetical zeolites with the sorting hat

erc

display_name: erc

zeolites

display_name: zeolites


en:
Data includes the the ab initio molecular dynamic simulation of Li7P3S11 that was used to measure the performance of the GNNFF. The data is divided into training and testing sets.

Brief descirption of the work: Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

materialscloud

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

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

en: Materials Cloud

en: Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture

density-functional-theory

display_name: density-functional-theory

molecular-dynamics

display_name: molecular-dynamics


en:
Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is used to assess the success of a synthesis.
However, predicting the colour of a material based on its structure is challenging.
In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale, using chemically meaningful reasoning.
In the process of developing the model, we also uncover inadequacies in current reporting mechanisms.
For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards.
To remedy this, we suggest and implement an alternative way of reporting colour that is more suitable for a data-driven approach to materials science.

en: A data-driven perspective on the colours of metal-organic frameworks

envi

en: Environment

color

display_name: color

lsmo

display_name: lsmo

ml

display_name: ml


en: The design of novel cathode materials for Li-ion batteries requires accurate first-principles predictions of structural, electronic, and magnetic properties as well as intercalation voltages in compounds containing transition-metal (TM) elements. For such systems, density-functional theory (DFT) with standard (semi-)local exchange-correlation functionals is of limited use as it often fails due to strong self-interaction (delocalization) errors that are especially large for the partially filled d shells of the TMs. Here, we perform the first comparative study of the phospho-olivine cathode materials LixMnPO4, LixFePO4, and mixed-TM LixMn1/2Fe1/2PO4 (x=0, 1/4, 1/2, 3/4, 1) using four electronic structure methods: DFT, DFT+U, DFT+U+V, and HSE06. We show that DFT+U+V outperforms the other three methods, provided that the onsite U and intersite V Hubbard parameters are determined from first-principles and self-consistently with respect to the structural parameters by means of density-functional perturbation theory (linear response). In particular, we demonstrate that DFT+U+V is the only method that correctly predicts the digital change in oxidation states of the TM ions in all compounds for the mixed-valence phases occurring at intermediate Li concentrations, leading to voltages in remarkable agreement with experiments. We thus show that the inclusion of intersite Hubbard interactions is essential for the accurate prediction of thermodynamic quantities when electronic localization occurs while in the presence of inter-atomic orbital hybridization. At variance with the other methods, DFT+U+V alone is capable to describe such localization-hybridization interplay, and thus opens the door for the study of more complex cathode materials as well as for a reliable exploration of the chemical space of compounds for Li-ion batteries.

en: Accurate electronic properties and intercalation voltages of olivine-type Li-ion cathode materials from extended Hubbard functionals

dftu

display_name: dftu

dftuv

display_name: dftuv

extended-hubbard-functionals

display_name: extended-hubbard-functionals

first-principles-calculations

display_name: first-principles-calculations

formation-energy

display_name: formation-energy

hybrid-functionals

display_name: hybrid-functionals

intercalation-voltages

display_name: intercalation-voltages

li-ion-batteries

display_name: li-ion-batteries

marvelosp

display_name: marvelosp

phospho-olivine-cathode-materials

display_name: phospho-olivine-cathode-materials


en: Two-dimensional materials are excellent lubricants with inherent advantages. However, superlubricity has only been reported in a few of them. It is a regret that other promising 2D materials with different physical properties cannot be discovered and applied in production so that energy consumption can be greatly reduced. Here we carry out high-throughput calculations for 1475 two-dimensional materials and screen for low-friction ones. To set a standard, we propose, for the first time, a geometry-independent frictional figure of merit based on the condition for stick-slip transition and our theory of Moiré friction. For the efficient calculation of this figure of merit, an innovative approach is developed based on an improved registry index model. Through the calculation, 340 materials are found to have a figure of merit lower than 10-3. Eventually, a small set of 21 materials with a figure of merit lower than 10-4 are screened out within them. These materials can provide diversified choices for applications. In addition, the efficient computational approach demonstrated in this work can be used to study other stacking-dependent properties.

en: Screening of low-friction two-dimensional materials from high-throughput calculations using frictional figure of merit

high-throughput-calculation

display_name: high-throughput-calculation

low-friction-materials

display_name: low-friction-materials

moire-patterns

display_name: moire-patterns

tribology

display_name: tribology

two-dimensional-materials

display_name: two-dimensional-materials


en: The process of catalytically oxidizing carbon monoxide (CO) remains a critical issue across various industrial sectors. However, it continues to be a challenge to achieve effective CO oxidation at low temperatures using non-noble metal catalysts. This study addresses these gaps by investigating the effects of dilute gallium (Ga) doping on the catalytic performance of flower-like ceria (CeO₂) microspheres. By using a modified hydrothermal synthesis method, we prepared the Ga-doped CeO₂ microspheres and characterized their morphology, surface area, and evidence of oxygen vacancy through various experimental techniques as well as computational simulation method. Our findings disclosed that the incorporation of Ga significantly enhances the catalytic performance of CeO₂, with the optimal doping level (2 mol% Ga) achieving a 90% CO conversion temperature (T90) of 388.9 °C, obviously lower than that of pristine CeO₂ (488.5 °C). This work demonstrates that dilute Ga doping effectively improves the catalytic properties of CeO₂-based materials, offering a potential strategy for developing effective CO oxidation catalysts.

en: Enhanced catalytic performance towards CO oxidation of broccoli-like Ceria microsphere by dilute Ga doping

catalytic-enhancement

display_name: catalytic-enhancement

ceria-microspheres

display_name: ceria-microspheres

co-oxidation

display_name: co-oxidation

gallium-doping

display_name: gallium-doping

hydrothermal-synthesis

display_name: hydrothermal-synthesis

oxygen-vacancy

display_name: oxygen-vacancy


en: Materials for high-temperature environments are actively being investigated for deployment in aerospace and nuclear applications. This study uses computational approaches to unravel the crystallography, and thermodynamics of a promising class of refractory alloys containing aluminum. Accurate first-principles calculations, cluster expansion models, and statistical mechanics techniques are employed to rigorously analyze precipitation in a prototypical senary Al-Nb-Ta-Ti-V-Zr alloy. Finite-temperature calculations reveal a strong tendency for aluminum to segregate to a single sublattice at elevated temperatures. Precipitate and matrix compositions computed with our ab-initio model are in excellent agreement with previous experimental measurements (Soni et al., 2020). Surprisingly, conventional B2-like orderings are found to be both thermodynamically and mechanically unstable in this alloy system. Complex anti-site defects are essential to forming a stable ordered precipitate. Our calculations reveal that the instability of B2 compounds can be related to a simple electron counting rule across all binary alloys formed by elements in groups 4,5, and 6. The results of this study provide viable routes toward designing high-temperature materials for deployment in extreme environments.

en: First-principles thermodynamics of precipitation in aluminum-containing refractory alloys

ab-initio

display_name: ab-initio

alloy-theory

display_name: alloy-theory

cluster-expansion

display_name: cluster-expansion

phase-stability

display_name: phase-stability

precipitation

display_name: precipitation


en: Progress in the development of rechargeable Ca-ion batteries demands the discovery of potential cathode materials. Transition metal oxides are interesting candidates due to their theoretical high energy densities, but with the drawback of a low Ca mobility. Previous computational/experimental investigations associate the electrochemical inactivity of various oxides (CaMO3-perovskite, CaMn2O4-post-spinel and CaV2O5) to high energy barriers for Ca migration. The introduction of oxygen and/or Ca vacancies in ternary transition metal oxides is a likely way to reshape the local topology and hence improve the Ca diffusivity. In this work, the energy barriers for Ca migration are calculated and discussed for (i) oxygen-deficient perovskites within the related Ca2Fe2O5-brownmillerite and Ca2Mn2O5 structures, and (ii) tunnel CaMn4O8, a derivative of the CaMn2O4-marokite with Ca vacancies.

en: DFT investigation of Ca mobility in reduced-perovskite and oxidized-marokite oxides

calcium-batteries

display_name: calcium-batteries

marokite

display_name: marokite

perovskite

display_name: perovskite

post-spinel

display_name: post-spinel


en: Photoelectrochemical solar water splitting is a promising approach to convert solar energy into sustainable hydrogen fuel using semiconductor electrodes. Due to their visible light absorption properties, oxynitrides have shown to be attractive photocatalysts for this application. In this study, the influence of the preparation method of CaNbO2N particles on their morphological and optical properties, and thereby their photoelectrochemical performance, is investigated. The best performing CaNbO2N photoanode is produced by ammonolysis of Nb enriched calcium niobium oxide. The enhanced photoactivity arises from an enlarged surface area and superior visible light absorption properties. The photoactivity of this photoanode was further enhanced by photodeposition of Co-Pi co-catalyst and by atomic layer deposition of an Al2O3 overlayer. A photocurrent density of 70 mA at 1.23 V vs RHE was achieved. The observed enhancement of the photoelectrochemical performance after Co-Pi/Al2O3 deposition is the combined effect of the improved kinetics of oxygen evolution due to the Co-Pi co-catalyst and the reduced surface recombination of the photogenerated carriers at the Al2O3 surface layer.

en: Improved photoelectrochemical water splitting of CaNbO2N photoanodes by Co-Pi photodeposition and surface passivation

computational-screening

display_name: computational-screening

marvel

display_name: marvel

photoelectrochemical-water-splitting

display_name: photoelectrochemical-water-splitting

surface-passivation

display_name: surface-passivation


en: The magnetic and electronic nature of the γ-Al2O3/SrTiO3 spinel/perovskite interface is explored by means of x-ray absorption spectroscopy. Polarized x-ray techniques combined with atomic multiplet calculations reveal localized magnetic moments assigned to Ti3+ at the interface with equivalent size for in- and out-of-plane magnetic field directions. Although magnetic fingerprints are revealed, the Ti3+ magnetism can be explained by a paramagnetic response at low temperature under applied magnetic fields. Modeling the x-ray linear dichroism results in a Delta0 ∼ 1.9 eV splitting between the t2g and eg states for the Ti4+ 3d0 orbitals. In addition these results indicate that the lowest energy states have the out-of-plane dxz/dyz symmetry. The isotropic magnetic moment behavior and the lowest energy dxz/dyz states are in contrast to the observations for the two-dimensional electron gas at the perovskite/perovskite interface of LaAlO3/SrTiO3 that exhibits an anisotropic magnetic dxy ground state.

materialscloud

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

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

en: Materials Cloud

en: Magnetic and electronic properties at the γ-Al2O3/SrTiO3 interface

experimental

display_name: experimental

magnetism

display_name: magnetism

two-dimensional-electron-gas

display_name: two-dimensional-electron-gas

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


exploding-and-weeping-ceramics

en: The systematic tuning of the lattice parameters to achieve improved kinematic compatibility between phases is a broadly effective strategy for improving the reversibility, and lowering the hysteresis, of solid-solid phase transformations. Here, "kinematic compatibility" refers to the fitting together of the phases. We present an apparently paradoxical example in which tuning to near perfect compatibility in (Zr/Hf)O2-(YNb)O4 results in a high degree of irreversibility, as manifested in explosive or "weeping" behavior on cooling through the tetragonal-to-monoclinic phase transformation. In the case of weeping the polycrystal slowly and steadily falls apart at the grain boundaries. These effects occur without chemical change. Finally, tuning to satisfy a condition we term the equidistance condition results in reversible behavior with the lowest hysteresis in this system. We give evidence that all these observations are explained by a more careful analysis of compatibility of the polycrystal, accounting for sample shape. These results show that an extreme diversity of behaviors, from reversible to explosive, is possible in a chemically homogeneous system by manipulating conditions of compatibility in unexpected ways. They provide critical concepts underlying the current search for a shape memory oxide ceramic.

en: Exploding and weeping ceramics

title_for_slug: exploding-and-weeping-ceramics

envi

en: Environment

ceramics

display_name: ceramics

cystallographic-compatibility

display_name: cystallographic-compatibility

shape-memory

display_name: shape-memory


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

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

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

dark-current

display_name: dark-current

detectivity

display_name: detectivity

organic-photodetectors

display_name: organic-photodetectors

traps

display_name: traps


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

en: Gas adsorption and process performance data for MOFs

aiida

display_name: aiida

marvel

display_name: marvel

mof

display_name: mof

nanoporous

display_name: nanoporous


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

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

cscs

display_name: cscs

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

hubbard-parameters

display_name: hubbard-parameters

linear-response-theory

display_name: linear-response-theory

marvelosp

display_name: marvelosp

open-science

display_name: open-science

open-source-software

display_name: open-source-software

quantum-espresso

display_name: quantum-espresso

self-interaction-corrections

display_name: self-interaction-corrections

transition-metal-compounds

display_name: transition-metal-compounds


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

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

band-gap

display_name: band-gap

dftu

display_name: dftu

hubbard-correction

display_name: hubbard-correction

lowdin-orthogonalization

display_name: lowdin-orthogonalization

self-interaction

display_name: self-interaction

snsf

display_name: snsf


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

dft

display_name: dft

epfl

display_name: epfl

marvel

display_name: marvel

nmr

display_name: nmr


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

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

materialscloud

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

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

en: Materials Cloud

en: The importance of nuclear quantum effects for NMR crystallography

first-principles-gipaw-dft

display_name: first-principles-gipaw-dft

machine-learning

display_name: machine-learning

molecular-crystals

display_name: molecular-crystals

nmr-chemical-shielding

display_name: nmr-chemical-shielding

nuclear-quantum-effects

display_name: nuclear-quantum-effects


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

cscs

display_name: cscs

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

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

ab-initio

display_name: ab-initio

boltzmann-transport

display_name: boltzmann-transport

electron-drag

display_name: electron-drag

electron-phonon-coupling

display_name: electron-phonon-coupling

electron-transport

display_name: electron-transport

intersect

display_name: intersect

phonon-drag

display_name: phonon-drag

phonon-transport

display_name: phonon-transport

thermoelectricity

display_name: thermoelectricity


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

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

dft

display_name: dft

gw

display_name: gw

marvel

display_name: marvel

negf

display_name: negf


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

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

aiidalab

display_name: aiidalab

graphene-nanoribbons

display_name: graphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis

scanning-probe-microscopy

display_name: scanning-probe-microscopy

snsf

display_name: snsf


en: The quantum effect of light nuclei in materials is usually considered as lattice vibration and zero-point motion (ZPM) from an ab initio perspective. Here we start from full-quantized particles and take the muon as an example, considering the two-body quantized system of the muon and the electrons within Two-Component Density Functional Theory, which can calculate the related two-body wave functions directly and automatically taking into account the quantum effect of the muon. This is an attempt to reveal the quantum effect of light nuclei in materials from a different respect. It is tested in some metallic systems (Fe-bcc, Co-fcc, Co-hcp) and some non-metallic systems (NaF, CaF₂, diamond), we finally find this fundamental method agrees better with experiments and shows good potential for further such study.

en: Two-component density functional theory study of quantized muons in solids

casino-quantum-monte-carlo

display_name: casino-quantum-monte-carlo

density-functional-theory

display_name: density-functional-theory

quantum-espresso

display_name: quantum-espresso


en: Charge-transfer-to-solvent states in aqueous halides are ideal systems for studying the electron-transfer dynamics to the solvent involving a complex interplay between electronic excitation and solvent polarization. Despite extensive experimental investigations, a full picture of the charge-transfer-to-solvent dynamics has remained elusive. Here, we visualise the intricate interplay between the dynamics of the electron and the solvent polarization occurring in this process. Through the combined use of ab initio molecular dynamics and machine learning methods, we investigate the structure, dynamics and free energy as the excited electron evolves through the charge-transfer-to-solvent process, which we characterize as a sequence of states denoted charge-transfer-to-solvent, contact-pair, solvent-separated, and hydrated electron states, depending on the distance between the iodine and the excited electron. Our assignment of the charge-transfer-to-solvent states is supported by the good agreement between calculated and measured vertical binding energies. Our results reveal the charge transfer process in terms of the underlying atomic processes and mechanisms.

en: Dynamics of the charge transfer to solvent process in aqueous iodide

ab-initio

display_name: ab-initio

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en: The resolution of proton solid-state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic-angle spinning alleviates this broadening by inducing coherent averaging. However, even the highest spinning rates experimentally accessible today are not able to completely remove dipolar interactions. Here, we introduce a deep learning approach to determine pure isotropic proton spectra from a two-dimensional set of magic-angle spinning spectra acquired at different spinning rates. Applying the model to 8 organic solids yields high-resolution 1H solid-state NMR spectra with isotropic linewidths in the 50-400 Hz range.

en: Pure isotropic proton NMR spectra in solids using deep learning

nmr

display_name: nmr

resolution

display_name: resolution


en: The electronic, optical, and magnetic properties of graphene nanoribbons (GNRs) can be engineered by controlling their edge structure and width with atomic precision through bottom-up fabrication based on molecular precursors. This approach offers a unique platform for all-carbon electronic devices but requires careful optimization of the growth conditions to match structural requirements for successful device integration, with GNR length being the most critical parameter. In a recent work we study, the growth, characterization, and device integration of 5-atom wide armchair GNRs (5-AGNRs), which are expected to have an optimal bandgap as active material in switching devices. 5-AGNRs are obtained via on-surface synthesis under ultrahigh vacuum conditions from Br- and I-substituted precursors. It is shown that the use of I-substituted precursors and the optimization of the initial precursor coverage quintupled the average 5-AGNR length. This significant length increase allowed the integration of 5-AGNRs into devices and the realization of the first field-effect transistor based on narrow bandgap AGNRs that shows switching behavior at room temperature. The study highlights that the optimized growth protocols can successfully bridge between the sub-nanometer scale, where atomic precision is needed to control the electronic properties, and the scale of tens of nanometers relevant for successful device integration of GNRs. The record contains data supporting the results presented in the publication

materialscloud

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

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

en: Materials Cloud

en: Growth optimization and device integration of narrow-bandgap graphene nanoribbons

dft

display_name: dft

fet

display_name: fet

grapheneflagship

display_name: grapheneflagship

raman

display_name: raman


en: The recently discovered layered kagome metals AV₃Sb₅ (A=K, Rb, Cs) exhibit diverse correlated phenomena, which are intertwined with a topological electronic structure with multiple van Hove singularities (VHSs) in the vicinity of the Fermi level. As the VHSs with their large density of states enhance correlation effects, it is of crucial importance to determine their nature and properties. Here, we combine polarization-dependent angle- resolved photoemission spectroscopy with density functional theory to directly reveal the sublattice properties of 3d-orbital VHSs in CsV₃Sb₅. Four VHSs are identified around the M point and three of them are close to the Fermi level, with two having sublattice-pure and one sublattice-mixed nature. Remarkably, the VHS just below the Fermi level displays an extre- mely flat dispersion along MK, establishing the experimental discovery of higher-order VHS. The characteristic intensity modulation of Dirac cones around K further demonstrates the sublattice interference embedded in the kagome Fermiology. The crucial insights into the electronic structure, revealed by our work, provide a solid starting point for the understanding of the intriguing correlation phenomena in the kagome metals AV₃Sb₅.

en: Rich nature of Van Hove singularities in Kagome superconductor CsV₃Sb₅

envi

en: Environment

arpes-dft

display_name: arpes-dft

nccr-marvel

display_name: nccr-marvel

vhss

display_name: vhss


en: Dehydrogenation reactions are key steps in many metal-catalyzed chemical processes and in the on-surface synthesis of atomically precise nanomaterials. The principal role of the metal substrate in these reactions is undisputed, but the role of metal adatoms remains, to a large extent, unanswered, particularly on gold substrates. In a recent publication, we discuss their importance by studying the surface-assisted cyclodehydrogenation on Au(111) as an ideal model case. We choose a polymer theoretically predicted to give one of two cyclization products depending on the presence or absence of gold adatoms. Scanning probe microscopy experiments observe only the product associated with adatoms. We challenge the prevalent understanding of surface-assisted cyclodehydrogenation, unveiling the catalytic role of adatoms and their effect on regioselectivity. The study adds new perspectives to the understanding of metal catalysis and the design of on-surface synthesis protocols for novel carbon nanomaterials. The record contains data for the CP2K-based calculations that support the results.

en: The role of metal adatoms in a surface-assisted cyclodehydrogenation reaction on a gold surface

graphene-nanoribbons

display_name: graphene-nanoribbons

marvel

display_name: marvel

on-surface-synthesis

display_name: on-surface-synthesis

stm

display_name: stm


en: Recently, a process has been proposed for generating negatively-curved carbon schwarzites via zeolite-templating (Braun et al., 2018). However, the proposed process leads to atomistic models which are not very symmetric and often rather defective. In the present work, an improved generation approach is developed, by imposing symmetry constraints, which systematically leads to defect-free, hence more stable, schwarzites. The stability of the newly predicted symmetric schwarzites is also compared to that of other carbon nanostructures (in particular carbon nanotubes - CNTs), which could also be accommodated within the same templates. Our results suggest that only a few of these (such as FAU, SBT and SBS) can fit schwarzites more stable than CNTs. Our predictions could help experimentalists in the crucial choice of the template for the challenging synthesis of schwarzites. Furthermore, being highly symmetric and stable phases, the models could also be synthesized by means of other experimental procedures.

en: Modeling symmetric and defect-free carbon schwarzites into various zeolite templates

carbon-nanotubes

display_name: carbon-nanotubes

random-symmetric-generation

display_name: random-symmetric-generation

schwarzites

display_name: schwarzites

zeolite-templated-nanostructures

display_name: zeolite-templated-nanostructures


en: Atomistic simulations provide insights into structure-property relations on an atomic size and length scale that are complementary to the macroscopic observables that can be obtained from experiments.  Quantitative predictions, however, are usually hindered by the need to strike a balance between the accuracy of the calculation of the interatomic potential and the modelling of realistic thermodynamic conditions.  Machine-learning techniques make it possible to efficiently approximate the outcome of accurate electronic-structure calculations that can, therefore, be combined with extensive thermodynamic sampling.  We take elemental nickel as a prototypical material, whose alloys have applications from cryogenic temperatures up to close to their melting point and use it to demonstrate how a combination of machine-learning models of electronic properties and statistical sampling methods makes it possible to compute accurate finite-temperature properties at an affordable cost.  We demonstrate the calculation of a broad array of bulk, interfacial, and defect properties over a temperature range from 100 to 2500 K, modelling also, when needed, the impact of nuclear quantum fluctuations and electronic entropy.  The framework we demonstrate here can be easily generalized to more complex alloys and different classes of materials.

en: Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime

cosmo

display_name: cosmo

electronic-density-of-states

display_name: electronic-density-of-states

epfl

display_name: epfl

machine-learning

display_name: machine-learning

materials-science

display_name: materials-science

metal-alloys

display_name: metal-alloys

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potentials

display_name: neural-network-potentials

snsf

display_name: snsf


en: We unveil the nature of the structural disorder in bottom-up zigzag graphene nanoribbons along with its effect on the magnetism and electronic transport on the basis of scanning probe microscopies and first-principles calculations. We find that edge-missing m-xylene units emerging during the cyclodehydrogenation step of the on-surface synthesis are the most common point defects. These "bite" defects act as spin-1 paramagnetic centers, severely disrupt the conductance spectrum around the band extrema, and give rise to spin-polarized charge transport. We further show that the electronic conductance across graphene nanoribbons is more sensitive to "bite" defects forming at the zigzag edges than at the armchair ones. Our work establishes a comprehensive understanding of the low-energy electronic properties of disordered bottom-up graphene nanoribbons.

en: Edge disorder in bottom-up zigzag graphene nanoribbons: implications for magnetism and quantum electronic transport

cscs

display_name: cscs

defects

display_name: defects

electronic-transport

display_name: electronic-transport

gnr

display_name: gnr

graphene-nanoribbons

display_name: graphene-nanoribbons

magnetism

display_name: magnetism

onr

display_name: onr


en: On-surface synthesis has recently emerged as an effective route towards the atomically precise fabrication of graphene nanoribbons (GNRs) of controlled topologies and widths. However, whether and to what degree structural disorder occurs in the resulting samples is a crucial issue for prospective applications that remains to be explored. Here, we experimentally visualize ubiquitous missing benzene rings at the edges of 9-atom wide armchair nanoribbons that form upon cleavage of phenyl groups in the precursor molecules. These defects are referred to as 'bite' defects. First, we address their density and spatial distribution on the basis of scanning tunnelling microscopy and find that they exhibit a strong tendency to aggregate. Next, we explore their effect on the quantum charge transport from first-principles calculations, revealing that such imperfections substantially disrupt the conduction properties at the band edges. Finally, we generalize our theoretical findings to wider nanoribbons in a systematic manner, hence establishing practical guidelines to minimize the detrimental role of such defects on the charge transport. Overall, our work portrays a detailed picture of 'bite' defects in bottom-up armchair GNRs and assesses their effect on the performance of carbon-based nanoelectronic devices.

en: Quantum electronic transport across 'bite' defects in graphene nanoribbons

marvel

display_name: marvel


en: Crystalline defects can modify quantum interactions in solids, causing unintuitive, even favourable, properties such as quantum Hall effect or superconducting vortex pinning. Here we present another example of this notion—an unexpected unidirectional Kondo scattering in single crystals of 2H-NbS2. This manifests as a pronounced low-temperature enhancement in the out-of-plane resistivity and thermopower below 40 K, hidden for the in-plane charge transport. The anomaly can be suppressed by the c-axis-oriented magnetic field, but is unaffected by field applied along the planes. The magnetic moments originate from layers of 1T-NbS2, which inevitably form during the growth, undergoing a charge-density-wave reconstruction with each superlattice cell (David-star-shaped cluster of Nb atoms) hosting a localized spin. Our results demonstrate the unique and highly anisotropic response of a spontaneously formed Kondo-lattice heterostructure, intercalated in a layered conductor.

en: Unidirectional Kondo scattering in layered NbS2

first-principles-calculation

display_name: first-principles-calculation

vasp

display_name: vasp


en: Time-dependent density functional theory has become state-of-the-art for describing photophysical and photochemical processes in extended materials due to its affordable cost. The inclusion of exact exchange was shown to be essential for the correct description of the long-range asymptotics of electronic interactions and thus a well-balanced description of valence, Rydberg and charge-transfer excitations. Several approaches for an efficient treatment of exact exchange have been established for the ground state, while implementations for excited-state properties are rare. Furthermore, the high computational costs required for excited-state properties in comparison to ground-state computations often hinder large-scale applications on periodic systems with hybrid functional accuracy. We therefore propose two approximate schemes for improving computational efficiency for the treatment of exact exchange. Within the auxiliary density matrix method (ADMM), exact exchange is estimated using a relatively small auxiliary basis and the introduced basis-set incompleteness error is compensated by an exchange density functional correction term. Benchmark results for a test set of 35 molecules demonstrate that the mean absolute error introduced by ADMM is smaller than 0.3 pm for excited-state bond lengths and in the range of 0.02 - 0.07 eV for vertical excitation, adiabatic excitation and fluorescence energies. Computational timings for a series of covalent-organic frameworks demonstrate that a speed-up of at least one order of magnitude can be achieved for ES geometry optimizations in comparison to conventional hybrid functionals. The second method is to use a semi-empirical tight binding approximation for both Coulomb and exchange contributions to the excited-state kernel. This simplified Tamm-Dancoff approximation (sTDA) achieves an accuracy comparable to approximated hybrid density functional theory when referring to highly accurate coupled-cluster reference data. We find that excited-state bond lengths deviate by 1.1 pm on average and mean absolute errors in vertical excitation, adiabatic excitation and fluorescence energies are in the range of 0.2 - 0.5 eV. In comparison to ADMM-approximated hybrid functional theory, sTDA accelerates the computation of broad-band excitation spectra by one order of magnitude, suggesting its potential use for large-scale screening purposes.

materialscloud

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

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

en: Materials Cloud

en: Excited-state properties for extended systems: efficient hybrid density functional methods

excited-state-properties

display_name: excited-state-properties

hybrid-density-functional-theory

display_name: hybrid-density-functional-theory

marie-curie-fellowship

display_name: marie-curie-fellowship

theoretical-spectroscopy

display_name: theoretical-spectroscopy


en: It is well known that interfaces in nanomaterials can act as ultra-fast short-circuit diffusion paths, as originating from local structural, chemical and/or electronic modifications at the interface. For example, the interface diffusivity of Cu in Cu/AlN nanomultilayers can be up to two orders of magnitude higher as compared to the bulk, which may promote interfacial premelting of Cu. Extensive ab initio calculations of vacancy formation and migration energies in Cu/AlN nanomultilayers were performed to arrive at the fundamental understanding of such anomalously fast interface diffusion phenomena. It was found that both the metallic Al-terminated interface and the mixed-bonded N-terminated interface promote high atomic interface mobilities by lowering the vacancy formation and vacancy migration energies in the interfacial Cu planes. Moreover, the out-of-plane vacancy migration energies highlights a strong tendency of vacancy segregation toward both interfaces.

en: Anomalously low vacancy formation energies and migration barriers at Cu/AlN interfaces from ab initio calculations

envi

en: Environment

ab-initio

display_name: ab-initio

metal-ceramic-interfaces

display_name: metal-ceramic-interfaces

nanomultilayers

display_name: nanomultilayers

vacancy-driven-diffusion

display_name: vacancy-driven-diffusion


en: As exemplified by the growing interest in the quantum anomalous Hall effect, the research on topology as an organizing principle of quantum matter is greatly enriched from the interplay with magnetism. In this vein, we present a combined electrical and thermoelectrical transport study on the magnetic Weyl semimetal EuCd₂As₂. Unconventional contribution to the anomalous Hall and anomalous Nernst effects were observed both above and below the magnetic transition temperature of EuCd₂As₂, indicating the existence of significant Berry curvature. EuCd₂As₂ represents a rare case in which this unconventional transverse transport emerges both above and below the magnetic transition temperature in the same material. The transport properties evolve with temperature and field in the antiferromagnetic phase in a different manner than in the paramagnetic phase, suggesting different mechanisms to their origin. Our results indicate EuCd₂As₂ is a fertile playground for investigating the interplay between magnetism and topology, and potentially a plethora of topologically nontrivial phases rooted in this interplay. This record contains the raw data for this manuscript [Physical Review Letters 126, 076602 (2021)].

en: Unconventional transverse transport above and below the magnetic transition temperature in Weyl semimetal EuCd₂As₂

anomalous-hall-effect

display_name: anomalous-hall-effect

anomalous-nernst-effect

display_name: anomalous-nernst-effect

experimental

display_name: experimental

magnetic-weyl-semimetal

display_name: magnetic-weyl-semimetal

topological-spin-texture

display_name: topological-spin-texture


en:
Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an important endeavour towards better uncertainty quantification in deep learning. In this manuscript, we introduce a novel calibration technique named expectation consistency (EC), consisting of a post-training rescaling of the last layer weights by enforcing that the average validation confidence coincides with the average proportion of correct labels. First, we show that the EC method achieves similar calibration performance to temperature scaling (TS) across different neural network architectures and data sets, all while requiring similar validation samples and computational resources. However, we argue that EC provides a principled method grounded on a Bayesian optimality principle known as the Nishimori identity. Next, we provide an asymptotic characterization of both TS and EC in a synthetic setting and show that their performance crucially depends on the target function. In particular, we discuss examples where EC significantly outperforms TS.
This record provides the code for the paper "Expectation consistency for calibration of neural networks".

en: Expectation consistency for calibration of neural networks (code)

neural-networks

display_name: neural-networks

temperature-scaling

display_name: temperature-scaling

uncertainty-quantification

display_name: uncertainty-quantification


en: A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic and non-invasive way is to incorporate a photoresponsive unit into a catalytically competent molecule. However, the description of the photoinduced conformational or structural changes that alter the catalyst's intrinsic reactivity is often reduced to a handful of intuitive static representations, which can struggle to capture the complexity of flexible organocatalysts. Here, we show how a comprehensive exploration of the free energy landscape of N-alkylated azobenzene-tethered piperidine catalysts is essential to unravel the conformational characteristics of each configurational state and explain the experimentally observed reactivity trends. Mapping the catalysts' conformational space highlights the existence of false ON or OFF states that lower their switching ability. Our findings expose the challenges associated with the realisation of reversible steric shielding for the photocontrol of Brønsted basicity of piperidine photoswitchable organocatalysts.

en: How robust is the reversible steric shielding strategy for photoswitchable organocatalysts?

epfl

display_name: epfl

erc

display_name: erc

photoswitchable-organocatalysis

display_name: photoswitchable-organocatalysis

reversible-steric-shielding

display_name: reversible-steric-shielding

snsf

display_name: snsf

statistical-sampling

display_name: statistical-sampling


en: In this record we provide the data to support our recent finding on surface catalyzed cycloaromatization. Immobilization of organic building blocks on metal surfaces and their coupling via thermally induced C-C bond formations are developing as an important addition to the toolbox of organic and polymer synthesis. Additional advantages of this technique are the in situ monitoring of the reaction by scanning probe methods and the accessibility of insoluble and reactive carbon nanostructures. The diversity of conceivable products, however, sensitively depends on the number of available on-surface reactions. In the manuscript where the results are discussed, we introduce an unprecedented example, the intermolecular oxidative coupling of isopropyl substituents of arenes. With a new phenylene ring being formed, this [3+3] dimerization can be regarded as a formal cycloaromatization. The synthetic value of this novel reaction is proven by the synthesis of polyarylenes and co-polyarylenes, which we demonstrate by synthesizing poly(2,7-pyrenylene-1,4-phenylene). Scanning tunneling microscopy and non-contact atomic force microscopy studies complemented by density functional theory calculations are employed to obtain mechanistic insights into the title reaction.

en: On-surface polyarylene synthesis by cycloaromatization of isopropyl substituents

ab-initio

display_name: ab-initio

catalysis

display_name: catalysis

on-surface-synthesis

display_name: on-surface-synthesis


en: CeO₂ greatly enhances the electrocatalytic oxygen evolution reaction (OER) activity of CoOx, though the enhancement mechanism beyond this synergy is yet to be understood. Here, operando hard X-ray adsorption spectroscopy (hXAS) is applied to monitor the Co K edge and Ce L₃ edge in CoOx/CeO₂ to shed light on the evolution of the Co and Ce oxidation states during OER. In addition, ex situ soft XAS (sXAS) characterizations provide information on the irreversible surface-specific transformations of the Co L₃ edge as well as of the O K edge. Combining the operando and ex situ spectroscopic characterizations with comprehensive electrochemical analyses, we confirm CeO₂ is not the active center for the OER. However, coupling CeO₂ with CoOx introduces significant modifications in the Co and O species at the CoOx surface and alters the flat band potential (Efb), leading to more favorable Co oxidation state transformations during OER and possibly modifying the preferential reaction pathway. This work establishes the connections between electronic structures, Co oxidation state and the OER reaction mechanism for CoOx/CeO₂ composites electrodes.

en: Operando tracking the interactions between CoOx and CeO₂ during oxygen evolution reaction

co-oxidation

display_name: co-oxidation

co-redox

display_name: co-redox

operando-xas

display_name: operando-xas

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en:
Metal-organic frameworks (MOF) and covalent organic frameworks (COFs) are promising nanocarriers for targeted drug delivery. For their uptake and release, non-covalent interactions between the framework and the drugs play a fundamental role. However, an in-depth understanding of how different functional groups affect these interactions is still lacking. Using a multilevel approach combining molecular docking and density functional theory, we show in the publication associated with this data how computational modeling can be exploited to gain information on the interplay between functionalization and drug delivery features. We find that functional groups significantly impact the strength of the host-guest interactions. Moreover, the interaction strength qualitatively correlates with drug release data from experimental literature, providing a link between framework structure and release properties. The results of our study facilitate the customization of non-covalent interactions between MOFs/COFs and drugs and give direction on how to rationally design nanocarriers for sustaining the drug release in the body over longer times.
The data deposited on the Materials Cloud Archive contains the inputs and outputs of the molecular docking and DFT simulations.

en: Engineering host-guest interactions in organic framework materials for drug delivery

covalent-organic-frameworks

display_name: covalent-organic-frameworks

dft

display_name: dft

drug-delivery

display_name: drug-delivery

metal-organic-frameworks

display_name: metal-organic-frameworks


en: Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all rely on the molecular information that determines the form of the electronic Schrödinger equation. Existing representations take the most varied forms, from non-linear functions of atom types and positions to atom densities and potential, up to complex quantum chemical objects directly injected into the ML architecture. In this work, we present the Spectrum of Approximated Hamiltonian Matrices (SPAᴴM) as an alternative pathway to construct quantum machine learning representations through leveraging the foundation of the electronic Schrödinger equation itself: the electronic Hamiltonian. As the Hamiltonian encodes all quantum chemical information at once, SPAᴴM representations not only distinguish different molecules and conformations, but also different spin, charge, and electronic states. As a proof of concept, we focus here on efficient SPAᴴM representations built from the eigenvalues of a hierarchy of well-established and readily-evaluated "guess" Hamiltonians. These SPAᴴM representations are particularly compact and efficient for kernel evaluation and their complexity is independent of the number of different atom types in the database

materialscloud

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

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

en: Materials Cloud

en: SPAᴴM: the spectrum of approximated hamiltonian matrices representations

epfl

display_name: epfl

guess-hamiltonians

display_name: guess-hamiltonians

machine-learning

display_name: machine-learning

molecular-representation

display_name: molecular-representation

spahm

display_name: spahm


many-body-self-interaction-and-polarons

en: We address the many-body self-interaction in relation to polarons in density functional theory. Our study provides (i) a unified theoretical framework encompassing many-body and one-body forms of self-interaction and (ii) an efficient semilocal scheme for charge localization. Our theoretical formulation establishes a quantitative connection between the many-body and one-body forms of self-interaction in terms of electron screening, thereby conferring superiority to the concept of many-body self-interaction. Our semilocal methodology involves the use of a weak localized potential and applies equally to electron and hole polarons. We find that polarons free from many-body self-interaction have formation energies that are robust with respect to the functional adopted.

en: Many-body self-interaction and polarons

title_for_slug: many-body-self-interaction-and-polarons

envi

en: Environment

density-functional-theory

display_name: density-functional-theory

electron-screening

display_name: electron-screening

many-body-self-interaction

display_name: many-body-self-interaction

mgo

display_name: mgo

one-body-self-interaction

display_name: one-body-self-interaction

polaron-localization

display_name: polaron-localization

polarons

display_name: polarons

self-interaction

display_name: self-interaction