en: We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.

en: Viscosity in water from first-principles and deep-neural-network simulations

ab-initio

display_name: ab-initio

machine-learning

display_name: machine-learning

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-networks

display_name: neural-networks

transport

display_name: transport

viscosity

display_name: viscosity

water

display_name: water


$oid: OBSOLETE

en: We combine the linearized Boltzmann Transport Equation (LBTE) and quantum transport by means of the Non-equilibrium Green's Functions (NEGF) to simulate monolayer MoS2 and WS2 ultra-scaled transistors with carrier mobilities extracted from experiments. Electron-phonon, charged impurity, and surface optical phonon scattering are taken into account with all necessary parameters derived from ab initio calculations or measurements, except for the impurity concentration. The LBTE method is used to scale the scattering self-energies of NEGF, which only include local interactions. This ensures an accurate reproduction of the measured mobilities by NEGF. We then perform device simulations and demonstrate that the considered transistors operate far from their performance limit (from 50% for MoS2 to 60% for WS2). Higher quality materials and substrate engineering will be needed to improve the situation.

en: Ab initio mobility of single-layer MoS2 and WS2: comparison to experiments and impact on the device characteristics

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

lbte

display_name: lbte

quantum-transport

display_name: quantum-transport

snsf

display_name: snsf


en: The simultaneous condensation of electronic and structural degrees of freedom gives rise to new states of matter, including superconductivity and charge-density-wave formation. When exciting such a condensed system, it is commonly assumed that the ultrafast laser pulse disturbs primarily the electronic order, which in turn destabilizes the atomic structure. Contrary to this conception, we show here that structural destabilization of few atoms causes macroscopic melting of the charge-density wave in 1T-TiSe2. In detail, we use ultrafast pump-probe non-resonant and resonant X-ray diffraction to track the periodic lattice distortion and the electronic charge density wave in 1T-TiSe2 upon optical excitation. We observe a fluence regime in which the periodic lattice deformation is strongly suppressed but the charge density wave related Se 4p orbital order remains mostly intact. Complete melting of both structural and electronic order occurs 4-5 times faster than expected from a purely electronic charge-screening process, strongly suggesting a structurally assisted weakening of excitonic correlations. Our experimental data provides insight on the intricate coupling between structural and electronic order in stabilizing the periodic-lattice-distortion/charge-density-wave state in 1T-TiSe2. The results further show that electron-phonon-coupling can lead to different, energy dependent phase-transition pathways in condensed matter systems, opening new possibilities in the conception of non-equilibrium phenomena at the ultrafast scale.

en: Structural involvement in the melting of the charge density wave in 1T-TiSe2

charge-density-wave

display_name: charge-density-wave

marvel

display_name: marvel

paul-scherrer-institute

display_name: paul-scherrer-institute

transient-x-ray-diffraction

display_name: transient-x-ray-diffraction

xfel

display_name: xfel


en: We present a joint theoretical and experimental study of the oxygen K-edge spectra for LaFeO₃ and homovalent Ni-substituted LaFeO₃ (LaFe₀.₇₅Ni₀.₂₅O₃), using first-principles simulations based on density-functional theory with extended Hubbard functionals and x-ray absorption near edge structure (XANES) measurements. Ground-state and excited-state XANES calculations employ Hubbard on-site U and inter-site V parameters determined from first principles and the Lanczos recursive method to obtain absorption cross sections, which allows for a reliable description of XANES spectra in transition-metal compounds in a very broad energy range, with an accuracy comparable to that of hybrid functionals but at a substantially lower cost. We show that standard gradient-corrected exchange-correlation functionals fail in capturing accurately the electronic properties of both materials. In particular, for LaFe₀.₇₅Ni₀.₂₅O₃ they do not reproduce its semiconducting behaviour and provide a poor description of the pre-edge features at the O K edge. The inclusion of Hubbard interactions leads to a drastic improvement, accounting for the semiconducting ground state of LaFe₀.₇₅Ni₀.₂₅O₃ and for good agreement between calculated and measured XANES spectra. We show that the partial substitution of Ni for Fe affects the conduction-band bottom by generating a strongly hybridized O(2p)-Ni(3d) minority-spin empty electronic state. The present work, based on a consistent correction of self-interaction errors, outlines the crucial role of extended Hubbard functionals to describe the electronic structure of complex transition-metal oxides such as LaFeO₃ and LaFe₀.₇₅Ni₀.₂₅O₃ and paves the way to future studies on similar systems.

en: Electronic structure of pristine and Ni-substituted LaFeO₃ from near edge x-ray absorption fine structure experiments and first-principles simulations

cscs

display_name: cscs

density-functional-theory

display_name: density-functional-theory

dftu

display_name: dftu

dftuv

display_name: dftuv

hubbard-corrections

display_name: hubbard-corrections

perovskites

display_name: perovskites

transition-metal-oxides

display_name: transition-metal-oxides

xanes

display_name: xanes


en: The record contains data to support our research findings regarding the development of a novel method for deriving localized basis sets in the projector augmented wave formalism, allowing to obtain a reduced basis set of atomic orbitals through the subdiagonalization of each atomic block of the Hamiltonian. The resulting local orbitals (LOs) inherit the information of the local crystal field. In the LO basis, it becomes apparent that the Hamiltonian is nearly block-diagonal, and we demonstrate that it is possible to keep only a subset of relevant LOs that provide an accurate description of the physics around the Fermi level. This reduces to some extent the redundancy of the original basis set, and at the same time, it allows one to perform post-processing of DFT calculations, ranging from the interpretation of electron transport to extracting effective tight-binding Hamiltonians, very efficiently and without sacrificing the accuracy of the results.

en: Smart local orbitals for efficient calculations within density functional theory and beyond

minimal-basis-sets

display_name: minimal-basis-sets

subdiagonalization

display_name: subdiagonalization

transport

display_name: transport


en: The dielectric properties of tetragonal hybrid perovskite CH3NH3PbI3 are studied through molecular dynamics at a temperature of 300 K in the presence of a finite electric field. The high-frequency dielectric constant ε∞ is found to be 4.5 along the a axis and 4.7 along the c axis. The values of the respective static dielectric constants ε0 are 43 ± 1 and 53 ± 3, much larger than the value of ∼25 pertaining to the orthorhombic phase, in which the organic cations cannot rotate. At frequencies below 3 cm–1, we observe a significant increase in ε0 by ∼23 (a axis) and ∼30 (c axis) compared to a vibrational approach, which does not account for the reorientation of the molecular units. The decomposition shows that the reorientation of the organic cations accounts for an increase of only ∼10. An increase of similar size results from the displacement of the cations within the cages of the lattice. The dominant contribution is found to arise from lattice vibrations coupled to the motion of the organic cations.

en: Low-frequency dielectric response of tetragonal perovskite CH3NH3PbI3

dielectric-properties

display_name: dielectric-properties

epfl

display_name: epfl

hybrid-perovskite

display_name: hybrid-perovskite

molecular-dynamics

display_name: molecular-dynamics


en: Ionosorbed oxygen is the key player in reactions on metal-oxide surfaces. This is particularly evident for chemiresistive gas sensors, which operate by modulating the conductivity of active materials through the formation/removal of surface O-related acceptors. Herein, we carried out a detailed study of various charged oxygen species on three naturally occurring surfaces of SnO2. We employed first-principles calculations and revealed that two types of surface acceptors can form spontaneously upon the adsorption of atmospheric oxygen: (i) superoxide O2 (in 1- charged state) on the (110) and the (101) surfaces and (ii) doubly ionized O (in 2- charged state) on the (100) facet, with the experimental evidence pointing to the latter as the source of sensing response. In this dataset, we present the optimized geometries (in CIF format) of different O and O_2 adsorption configurations in the most relevant charged states.

en: Chemistry of oxygen ionosorption on SnO2 surfaces

charged-oxygen-species

display_name: charged-oxygen-species

chemiresistive-sensing

display_name: chemiresistive-sensing

ionosorption-model

display_name: ionosorption-model

surface-chemistry

display_name: surface-chemistry


en:
Selector devices are indispensable components of large-scale nonvolatile memory and neuromorphic array systems. Besides the conventional silicon transistor, two-terminal ovonic threshold switching (OTS) devices with much higher scalability are currently the most industrially favored selector technology. Despite industrial confidence in OTS selectors, current devices rely heavily on intricate control of material stoichiometry and generally suffer from toxic and complex dopants to remedy the intrinsic demerits of the principal functioning materials. Here, we report on a selector with a large drive current density of 34 MA/cm2 and a ~106 high nonlinearity, realized in an environment-friendly and earth-abundant sulfide binary semiconductor, GeS. Both experiments and first-principles calculations have revealed Ge pyramid-dominated network and high density of near-valence band trap states in amorphous GeS. The high drive current capacity is associated with the strong Ge-S covalency and the high nonlinearity could arise from the synergy of the mid-gap traps assisted electronic transition and local Ge-Ge chain growth as well as locally enhanced bond alignment under high electric field. Besides the superior selector function, we have also demonstrated stochastic integrate-and-fire neuron behavior using GeS device, providing an intriguing opportunity for all-chalcogenide neuromorphic electronics.
The source data underlying Figs. 1c-e, Figs. 2a–e, and Figs. 3b, d, f are provided as a Source Data file.

en: Ultrahigh drive current and large selectivity in GeS selector

ges

display_name: ges

ots

display_name: ots

phase-change-memory

display_name: phase-change-memory

selector

display_name: selector


en: The self-consistent evaluation of Hubbard parameters using linear-response theory is crucial for quantitatively predictive calculations based on Hubbard-corrected density-functional theory. Here, we extend a recently-introduced approach based on density-functional perturbation theory (DFPT) for the calculation of the on-site Hubbard U to also compute the inter-site Hubbard V. DFPT allows to reduce significantly computational costs, improve numerical accuracy, and fully automate the calculation of the Hubbard parameters by recasting the linear response of a localized perturbation into an array of monochromatic perturbations that can be calculated in the primitive cell. In addition, here we generalize the entire formalism from norm-conserving to ultrasoft and projector-augmented wave formulations, and to metallic ground states. After benchmarking DFPT against the conventional real-space Hubbard linear response in a supercell, we demonstrate the effectiveness of the present extended Hubbard formulation in determining the equilibrium crystal structure of LiₓMnPO₄ (x=0,1) and the subtle energetics of Li intercalation.

en: Self-consistent Hubbard parameters from density-functional perturbation theory in the ultrasoft and projector-augmented wave formulations

cscs

display_name: cscs

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

extended-hubbard-functionals

display_name: extended-hubbard-functionals

li-ion-batteries

display_name: li-ion-batteries

linear-response-theory

display_name: linear-response-theory

marvel

display_name: marvel

monochromatic-perturbations

display_name: monochromatic-perturbations

projector-augmented-wave-method

display_name: projector-augmented-wave-method

self-interaction-corrections

display_name: self-interaction-corrections

ultrasoft-pseudopotentials

display_name: ultrasoft-pseudopotentials

voltages

display_name: voltages


en: An advanced modeling approach is presented to shed light on the thermal transport properties of van der Waals materials (vdWMs) composed of single-layer transition metal dichalcogenides (TMDs) stacked on top of each other with a total or partial overlap only in the middle region. It relies on the calculation of dynamical matrices from first principles and on their usage in a phonon quantum transport simulator. We observe that vibrations are transferred microscopically from one layer to the other along the overlap region which acts as a filter selecting out the states that can pass through it. Our work emphasizes the possibility of engineering heat flows at the nanoscale by carefully selecting the TMD monolayers that compose vdWMs.

en: Ab initio modeling of thermal transport through van der Waals materials

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).

snsf

display_name: snsf

thermal-properties

display_name: thermal-properties

transition-metal-dichalcogenides

display_name: transition-metal-dichalcogenides

van-der-waals-materials

display_name: van-der-waals-materials


en: Radiative cooling is a passive cooling technology by reflecting sunlight and emitting radiation in the sky window. Although highly desired, full daytime sub-ambient radiative cooling in commercial-like single-layer particle-matrix paints is yet to be achieved. Here we demonstrate full daytime sub-ambient radiative cooling in CaCO3-acrylic paint by utilizing the large bandgap CaCO3 fillers, a high particle concentration of 60% and a broad size distribution. Our paint shows high solar reflectance of 95.5% and high normal emissivity of 0.94 in the sky window. Field tests show cooling power exceeding 37W/m2 and surface temperature more than 1.7˚C below ambient at noon. A figure of merit RC is proposed to compare the cooling performance independent of weather conditions. The standard RC of our paint is 0.49, among the best radiative cooling performance while offering unprecedented benefits of the convenient paint form, low cost, and the compatibility with commercial paint fabrication process.

en: Full daytime sub-ambient radiative cooling in commercial-like paints with high figure of merit

materialscloud

en: Materials Cloud

envi

en: Environment

atmospheric-sky-window

display_name: atmospheric-sky-window

daytime-radiative-cooling

display_name: daytime-radiative-cooling

figure-of-merit

display_name: figure-of-merit

lorentz-mie-theory

display_name: lorentz-mie-theory

monte-carlo-simulation

display_name: monte-carlo-simulation

particle-matrix-paint

display_name: particle-matrix-paint


en: While alloy design has practically shown an efficient strategy to mediate two seemingly conflicted performances of writing speed and data retention in phase-change memory, the detailed kinetic pathway of alloy-tuned crystallization is still unclear. Here, we propose hierarchical melt and coordinate bond strategies to solve them, where the former stabilizes a medium-range crystal-like region and the latter provides a rule to stabilize amorphous. The Er0.52Sb2Te3 compound we designed achieves writing speed of 3.2 ns and ten-year data retention of 161 °C. We provide a direct atomic-level evidence that two neighbor Er atoms stabilize a medium-range crystal-like region, acting as a precursor to accelerate crystallization; meanwhile, the essential reason of stabilization originates from the formation of coordinate bonds by sharing lone-pair electrons of chalcogenide atoms with the empty 5d orbitals of Er atoms. The two rules pave the way for the development of storage-class memory with excellent comprehensive performance to achieve next revolutionary technology node.

en: Designing crystallization to tune the performance of phase-change memory: rules of hierarchical melt and coordinate bond

coordinate-bond

display_name: coordinate-bond

er-sb-te

display_name: er-sb-te

experimental

display_name: experimental

hierarchical-melt

display_name: hierarchical-melt

phase-change-memory

display_name: phase-change-memory


en: Amorphous alumina (a-AlOx), which plays important roles in several technological fields, shows wide variation of the density and composition. However, their influences on the properties of a-AlOx have rarely been investigated from a theoretical perspective. In this study, high dimensional neural network (NN) potentials were constructed to generate a series of atomic structures of a-AlOx with different densities (2.6–3.3 g/cm3) and O/Al ratios (1.0–1.75). The structural, vibrational, mechanical, and thermal properties of the a-AlOx models were investigated, as well as the Li and Cu diffusion behaviour in the models. The results showed that the density and composition had different degrees of effects on the different properties. The structural and vibrational properties were strongly affected by the composition, whereas the mechanical properties were mainly determined by the density. The thermal conductivity was affected by both the density and composition of a-AlOx. However, the effects on the Li and Cu diffusion behaviour were relatively unclear.

en: Effects of the density and composition on the properties of amorphous alumina: a high dimensional neural network potential study

aluminum-oxide

display_name: aluminum-oxide

amorphous

display_name: amorphous

neural-network-potential

display_name: neural-network-potential


en: This work details how to determine structural prototypes for the cubic perovskite structure that are used to study the B-site displacements in the cubic, paraelectric phase. Car-Parrinello MD simulations of cubic barium titanate (BaTiO3) show the titanium displacements from the undistorted cubic structure. Using a systematic symmetry analysis we construct microscopic templates, i.e. representative structural models in the form of supercells that satisfy a desired point symmetry but are built from the combination of lower-symmetry primitive cells. Density functional theory calculations, using the microscopic templates as starting structures for a relaxation, are carried out to find structural prototypes of BaTiO3 with local polar distortions but with cubic point symmetry. The stability of these structures is studied as a function of volume and with respect to the zone-boundary phonons of pristine cubic BaTiO3. The stable distortions patterns for BaTiO3 are investigated for other titanates and for a handful of niobates and zirconates.

en: A microscopic picture of paraelectric perovskites from structural prototypes

group-subgroup-relations

display_name: group-subgroup-relations

marvelosp

display_name: marvelosp

max

display_name: max

paraelectricity

display_name: paraelectricity

perovskite

display_name: perovskite


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

color

display_name: color

epfl

display_name: epfl

erc

display_name: erc

lsmo

display_name: lsmo

marvel

display_name: marvel

ml

display_name: ml


en: Intramolecular singlet fission (iSF) has shown potential to improve the power conversion efficiency in photovoltaic devices by promoting the splitting of a photon-absorbing singlet exciton into two triplet excitons within a single molecule. Among different possibilities, the donor-acceptor modular strategy of copolymers has shown great promise in its ability to undergo iSF under certain conditions. However, the number of iSF donor-acceptor copolymers reported in the literature remains remarkably narrow and clear trends for the molecular design of better candidates have not yet been established. In this work, we identify the trade-off between the main iSF requirements of the donor-acceptor strategy and formulate design rules that allow them to be tuned simultaneously in a fragment-based approach. Based on a library of 2944 donor-acceptor copolymers, we establish simple guidelines to build promising novel materials for iSF. These consist in (1st) selecting an acceptor core with high intrinsic singlet-triplet splitting, (2nd) locating a donor with a larger monomer frontier molecular orbital (FMO) gap than that of the acceptor, and (3rd) tuning the relative energy of donor and/or acceptor FMOs through functionalization to promote photoinduced charge transfer in the resulting polymer. Remarkably, systems containing benzothiadiazole and thiophene-1,1-dioxide acceptors, which have been shown to undergo iSF, fulfill all criteria simultaneously when paired with appropriate donors. This is due to their particular electronic features, which make them highly promising candidates in the quest for iSF materials.

en: Identifying the trade-off between intramolecular singlet fission requirements in donor-acceptor copolymers

conjugated-polymers

display_name: conjugated-polymers

excited-states

display_name: excited-states

singlet-fission

display_name: singlet-fission


en:
Exploration of structure-property relationships as a function of dopant concentration is commonly based on mean field theories for solid solutions. However, such theories that work well for semiconductors tend to fail in materials with strong correlations, either in electronic behavior or chemical segregation. In these cases, the details of atomic arrangements are generally not explored and analyzed. The knowledge of the generative physics and chemistry of the material can obviate this problem, since defect configuration libraries as stochastic representation of atomic level structures can be generated, or parameters of mesoscopic thermodynamic models can be derived. To obtain such information for improved predictions, we use data from atomically resolved microscopic images that visualize complex structural correlations within the system and translate them into statistical mechanical models of structure formation. Given the significant uncertainties about the microscopic aspects of the material's processing history along with the limited number of available images, we combine model optimization techniques with the principles of statistical hypothesis testing. We demonstrate the approach on data from a series of atomically-resolved scanning transmission electron microscopy images of MoxRe1−xS2 at varying ratios of Mo/Re stoichiometries, for which we propose an effective interaction model that is then used to generate atomic configurations and make testable predictions at a range of concentrations and formation temperatures.

This dataset includes STEM image data files and associated python code to perform statistical analysis of the images and simulations.

en: Thermodynamics of order and randomness in dopant distributions inferred from atomically resolved images

dichalcogenide

display_name: dichalcogenide

monte-carlo-simulation

display_name: monte-carlo-simulation

segregation

display_name: segregation

statistical-inference

display_name: statistical-inference


en: The reorganization energy (λ), which quantifies the structural rearrangement of a molecule when accommodating a charge, is a key parameter in the evaluation of charge mobility in molecular solids. However, it is unclear how λ is influenced by conformational isomerism, which co-exist in amorphous solids. Here, we examine the conformational space of a family of model amorphous organic hole transport materials (HTMs), derived from triphenylamine in a core-arm template, and probe the effect of conformational complexity on λ. We observe an extreme dependence of λ on the conformer geometry of sterically congested HTMs, which to the best of our knowledge has not been described previously. These results serve as a cautionary tale that, while extracting the reorganization energy from a single molecular conformer optimized in the gas phase may be appropriate for rigid and sterically unencumbered structures, it is not for many state-of-the-art HTMs that contain multiple bulky substituents.

en: Is a single conformer sufficient to describe the reorganization energy of amorphous organic transport materials?

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).

amorphous-materials

display_name: amorphous-materials

charge-transport

display_name: charge-transport

reorganization-energy

display_name: reorganization-energy

replica-exchange-molecular-dynamics

display_name: replica-exchange-molecular-dynamics

triphenylamine

display_name: triphenylamine


en:
The record contains the data supporting our recent findings on asymmetric azide-alkyne Huisgen cycloaddition on chiral metal surfaces:
Achieving fundamental understanding of enantioselective heterogeneous synthesis is marred by the permanent presence of multitudinous arrangements of catalytically active sites in real catalysts. We address this issue by using structurally comparatively simple, well‐defined, and chiral intermetallic PdGa{111} surfaces as catalytic substrates. We demonstrate the impact of chirality transfer and ensemble effect for the thermally activated azide‐alkyne Huisgen cycloaddition between 3‐(4‐azidophenyl)propionic acid and 9‐ethynylphenanthrene on these threefold symmetric intermetallic surfaces under ultrahigh vacuum conditions. Specifically, we encounter a dominating ensemble effect for this reaction as on the Pd3‐terminated PdGa{111} surfaces no stable heterocoupled structures are created, while on the Pd1‐terminated PdGa{111} surfaces, the cycloaddition proceeds regioselectively. Moreover, we observe chirality transfer from the substrate to the reaction products, as they are formed enantioselectively on the Pd1‐terminated PdGa{111} surfaces. Our results evidence a determinant ensemble effect and the immense potential of PdGa as asymmetric heterogeneous catalyst.

en: Asymmetric azide‐alkyne Huisgen cycloaddition on chiral metal surfaces

materialscloud

en: Materials Cloud

ab-initio-simulations

display_name: ab-initio-simulations

asymmetric-heterogeneous-catalysis

display_name: asymmetric-heterogeneous-catalysis

azidealkyne-huisgen-cycloaddition

display_name: azidealkyne-huisgen-cycloaddition

experimental

display_name: experimental

onsurface-synthesis

display_name: onsurface-synthesis

snsf

display_name: snsf


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

envi

en: Environment

cscs

display_name: cscs

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: Infrared and Raman spectroscopies are ubiquitous techniques employed in many experimental laboratories, thanks to their fast and non-destructive nature able to capture materials' features as spectroscopic fingerprints. Nevertheless, these measurements frequently need theoretical support in order to unambiguously decipher and assign complex spectra. Linear-response theory provides an effective way to obtain the higher-order derivatives needed, but its applicability to modern exchange-correlation functionals remains limited. Here, we devise an automated, open-source, user-friendly approach based on ground-state density-functional theory and the electric enthalpy functional to allow seamless calculations of first-principles infrared and Raman spectra. By employing a finite-displacement and finite-field approach, we allow for the use of any functional, as well as an efficient treatment of large low-symmetry structures. Additionally, we propose a simple scheme for efficiently sampling the Brillouin zone with different electric fields. To demonstrate the capabilities of our approach, we provide illustrations using the ferroelectric LiNbO₃ crystal as a paradigmatic example. We predict infrared and Raman spectra using various (semi)local, Hubbard corrected, and hybrid functionals. Our results also show how PBE0 and extended Hubbard functionals yield in this case the best match in term of peak positions and intensities, respectively.

en: Automated all-functionals infrared and Raman spectra

ab-initio

display_name: ab-initio

aiida

display_name: aiida

automated

display_name: automated

density-functional-theory

display_name: density-functional-theory

infrared

display_name: infrared

phonons

display_name: phonons

raman

display_name: raman

vibrational-properties

display_name: vibrational-properties


en: The record contains model structures of the Si(100)-SiO₂ interface with disordered and crystalline oxides. The models have been purposely designed in order to match a large variety of atomic-scale experimental data. In particular, the models with a disordered oxide reproduce the amorphous nature of the oxide and the density of the oxide near the substrate. The atomic structure does not show any coordination defects consistent with the low measured density of interfacial defect states. The transition region includes intermediate oxidation states of Si in accord with photoemission experiments. Also the dielectric properties agree with the experimental characterization. Hence, the present models synthesize the present status of our experimental knowledge on the Si(100)–SiO₂ interface and provide a solid and necessary basis for future investigations in the area of gate stacks for Si-based microelectronics.

en: Model structures of the Si(100)-SiO₂ interface

atomic-coordinates

display_name: atomic-coordinates

epfl

display_name: epfl

snsf

display_name: snsf


en: The metallic compound Mn5Si3 hosts a series of antiferromagnetic phases that can be controlled by external stimuli, such as temperature and magnetic field. In this work, we investigate the spin-excitation spectrum of bulk Mn5Si3 by combining inelastic neutron scattering measurements and density functional theory calculations. We study the evolution of the dynamical response under external parameters and demonstrate that the spin dynamics of each phase is robust against any combination of temperature and magnetic field. In particular, the high-energy spin dynamics is very characteristic of the different phases consisting of either spin waves or broad fluctuation patterns. This data set contains the data relevant to performing the spin dynamics simulations with the Spirit code, the spin-wave calculations with the SWIS code, and the neutron scattering experimental data.

en: An overview of the spin dynamics of antiferromagnetic Mn₅Si₃

antiferromagnetism

display_name: antiferromagnetism

frustration

display_name: frustration

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

intersect

display_name: intersect

marvel

display_name: marvel

noncollinear-magnetism

display_name: noncollinear-magnetism

spin-waves

display_name: spin-waves


en: We present high-quality angle-resolved photoemission (ARPES) and density functional theory calculations (DFT+U) of SmCoIn₅. We find broad agreement with previously published studies of LaCoIn₅ and CeCoIn₅, confirming that the Sm 4f electrons are mostly localized. Nevertheless, our model is consistent with an additional delocalized Sm component, stemming from hybridization between the 4f electrons and the metallic bands at "hot spot" positions in the Brillouin zone. The dominant hot spot, called γz, is similar to a source of delocalized f states found in previous experimental and theoretical studies of CeCoIn₅. In this work, we identify and focus on the role of geometric frustration in exploring the relationship between heavy quasiparticles and the magnetically ordered ground state of SmCoIn₅. Specifically, we find a globally flat band consisting of Co 3dxy/3dz2 orbital states near E = −0.7 eV, indicating a general role for geometric frustration in the "115" family of materials. We also show that the delocalized Sm 4f states can hybridize directly with the Co 3dxz/3dyz orbitals, which occurs in our model at the Brillouin zone boundary point R in a band that is locally flat and touches the Fermi level from above. Our work identifies microscopic ingredients for additional magnetic interactions in the "115" materials beyond the RKKY mechanism, and strongly suggests that the Co d bands are an important ingredient in the formation of both magnetic and superconducting ground states.

en: Flat-band hybridization between f and d states near the Fermi energy of SmCoIn₅

dftu

display_name: dftu

heavy-fermions

display_name: heavy-fermions

kondo-effect

display_name: kondo-effect


en: The recently discovered vanadium-based Kagome metals AV₃Sb₅ (A = K, Rb, Cs) undergo a unique phase transition into charge-density wave (CDW) order which precedes both unconventional superconductivity and time-reversal symmetry breaking. Therefore the essential first step in building a full understanding of the role of CDW in establishing these unconventional phases is to unveil the symmetries and the microscopic nature of the charge-ordered phase. Here, we determine the exact structure of the 2×2×2 superlattice that develops below the charge-density wave ordering temperature (TCDW) in RbV₃Sb₅. We present a comprehensive set of ⁵¹V, ⁸⁷Rb, and ¹²¹Sb nuclear magnetic resonance (NMR) measurements and density functional theory simulations of NMR observables to provide a unique site-selective view into the local nature of the charge-ordered phase. The combination of these experimental results with simulations provides compelling evidence that the CDW structure prevailing below 103 K in RbV₃Sb₅ is the so-called inverse Star of David pattern, π-shifted along the c axis. These findings put severe constraints on the topology of these Kagome compounds and thus provide essential guidance for the development of an appropriate theoretical framework for predicting properties of exotic electronic orders arising within the CDW phase.

en: Microscopic nature of the charge-density wave in the kagome superconductor RbV₃Sb₅

dft

display_name: dft

nuclear-magnetic-resonance

display_name: nuclear-magnetic-resonance

nuclear-quadrupole-resonance

display_name: nuclear-quadrupole-resonance


en: This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupied d states near the Fermi level, we determine that the large, sharp d density of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

en: Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set

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).

density-functional-theory

display_name: density-functional-theory

equivariant-neural-networks

display_name: equivariant-neural-networks

gaussian-processes

display_name: gaussian-processes

machine-learned-force-fields

display_name: machine-learned-force-fields

transition-metals

display_name: transition-metals


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

materialscloud

en: Materials Cloud

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: Muon spin rotation and relaxation spectroscopy is a powerful tool for studying magnetic materials, offering a local probe that complements scattering techniques and provides advantages in cases of strong incoherent scattering or neutron absorption. By integrating computational methods (DFT+μ), the microscopic interactions driving the observed signals can be precisely quantified, enhancing the technique's predictive power. We present a set of efficient algorithms and workflows - implemented in the AiiDA framework - that automate the DFT+μ procedure, where the muon is treated as a hydrogen impurity within the density functional theory framework. Our approach automates the identification of muon stopping sites, dipolar interactions, and hyperfine interactions. In this record we share the result of our calculations on well-known compounds, to demonstrate the accuracy and ease of use of our protocol.

en: Automated computational workflows for muon spin spectroscopy

envi

en: Environment

aiida

display_name: aiida

aiida-impuritysupercellconv

display_name: aiida-impuritysupercellconv

aiida-muon

display_name: aiida-muon

automated-workflows

display_name: automated-workflows

dftmu

display_name: dftmu

first-principles

display_name: first-principles

marvel

display_name: marvel

muon-spin-spectroscopy

display_name: muon-spin-spectroscopy


en: The nature of the liquid–solid interface determines the characteristics of a variety of physical phenomena, including catalysis, electrochemistry, lubrication, and crystal growth. Most of the established models for crystal growth are based on macroscopic thermodynamics, neglecting the atomistic nature of the liquid–solid interface. Here, experimental observations and molecular dynamics simulations are employed to identify the 3D nature of an atomic‐scale ordering of liquid Ga in contact with solid GaAs in a nanowire growth configuration. An interplay between the liquid ordering and the formation of a new bilayer is revealed, which, contrary to the established theories, suggests that the preference for a certain polarity and polytypism is influenced by the atomic structure of the interface. The conclusions of this work open new avenues for the understanding of crystal growth, as well as other processes and systems involving a liquid–solid interface.

en: 3D ordering at the liquid–solid polar interface of nanowires

gaas

display_name: gaas

gallium-arsenide

display_name: gallium-arsenide

machine-learning-potential

display_name: machine-learning-potential

molecular-dynamics-simulations

display_name: molecular-dynamics-simulations

nanowire

display_name: nanowire

neural-network-potential

display_name: neural-network-potential

ordering-at-the-interface

display_name: ordering-at-the-interface

snsf

display_name: snsf


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: Homogenous enantioselective catalysis is nowadays the cornerstone in the manufacturing of enantiopure substances, but its technological implementation suffers from well-known impediments like the lack of endurable catalysts exhibiting long-term stability. The catalytically active intermetallic compound Palladium-Gallium (PdGa), conserving innate bulk chirality on its surfaces, represent a promising system to study asymmetric chemical reactions by heterogeneous catalysis, with prospective relevance for industrial processes. In a recent publication we investigate the adsorption of 10,10′-dibromo-9,9′-bianthracene (DBBA) on the PdGa:A(-1-1-1) Pd3-terminated surface by means of scanning tunneling microscopy (STM) and spectroscopy (STS). A highly enantioselective adsorption of the molecule evolving into a near 100% enantiomeric excess below room temperature is observed. This exceptionally high enantiomeric excess is attributed to temperature activated conversion of the S to the R chiral conformer. Tip-induced bond cleavage of the R conformer shows a very high regioselectivity of the DBBA debromination. The experimental results are interpreted by density functional theory atomistic simulations. This work extends the knowledge of chirality transfer onto the enantioselective adsorption of non-planar molecules and manifests the ensemble effect of PdGa surfaces resulting in robust regioselective debromination. This record contains data that support the results discussed in the manuscript.

en: Asymmetric molecular adsorption and regioselective bond cleavage on chiral PdGa crystals

cscs

display_name: cscs

density-functional-theory

display_name: density-functional-theory

enantioselectivity

display_name: enantioselectivity

marvelasm

display_name: marvelasm

surface-chemistry

display_name: surface-chemistry


en: Advanced quantum transport approach from first-principle to shed light on the influence of orientation misalignments on the performance of BP-based field-effect transistors, for which the input files are reported. Both n-and p-type configurations are investigated for six alignment angles, in the ballistic limit of transport and in the presence of electron-phonon and charged impurity scattering.

en: Impact of orientation misalignments on black phosphorus ultrascaled field-effect transistors

computational-materials

display_name: computational-materials

field-effect-transistors

display_name: field-effect-transistors

quantum-transport

display_name: quantum-transport


en: In this paper we use density functional theory combined with dynamical mean-field theory (DFT+DMFT) to study interface effects between the correlated metal CaVO3 and the two typical substrate materials SrTiO3 and LaAlO3. We find that the CaVO3/SrTiO3 interface has only a marginal influence on the CaVO3 thin film, with the dominant effect being the (bulklike) epitaxial strain imposed by the large lattice mismatch, rendering the CaVO3 film insulating due to the enhanced orbital polarization related to the strong level splitting between the t2g orbitals. In contrast, at the polar CaVO3/LaAlO3 interface, the presence of the interface can have a huge effect on the physical properties, depending both on the specific interface termination and on the specific boundary conditions imposed by the multilayer geometry. We compare three approaches to modeling the CaVO3/LaAlO3 interface, all of which impose a different set of (electrostatic) boundary conditions. Our results demonstrate that different substrates, interface terminations, and electrostatic boundary conditions can drastically affect the properties of thin-film heterostructures, indicating the potential tunability of the interfacial properties via multilayer engineering.

en: Tailoring interfacial properties in CaVO3 thin films and heterostructures with SrTiO3 and LaAlO3: A DFT+DMFT study

correlated-metal

display_name: correlated-metal

dynamical-mean-field-theory

display_name: dynamical-mean-field-theory

first-principles-calculations

display_name: first-principles-calculations

metal-insulator-transition

display_name: metal-insulator-transition

multilayer-thin-films

display_name: multilayer-thin-films

superlattices

display_name: superlattices


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

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).

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

materialscloud

en: Materials Cloud

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 use of machine learning to accelerate computer simulations is on the rise. In atomistic simulations, the use of machine learning interatomic potentials (ML-IAPs) can significantly reduce computational costs while maintaining accuracy close to that of ab initio methods. To achieve this, ML-IAPs are trained on large datasets of images, meaning atomistic configurations labeled with data from ab initio calculations. Focusing on carbon, we have created a dataset, CA-9, consisting of 48000 images labeled with energies, forces and stress tensors obtained via ab initio molecular dynamics (AIMD). We use deep learning to train state-of-the-art neural network potentials (NNPs), a form of ML-IAP, on the CA-9 dataset and investigate how training and validation data can affect the performance of the NNPs. Our results show that image generation with AIMD causes a high degree of similarity between the generated images, which has a detrimental effect on the NNPs. However, by carefully choosing which images from the dataset are included in the training and validation data, this effect can be mitigated. We end by benchmarking our trained NNPs in real-world applications and show we can reproduce results from ab initio calculations with an accuracy higher than previously published ML- or classic IAPs.

en: CA-9, a dataset of carbon allotropes for training and testing of neural network potentials

envi

en: Environment

carbon

display_name: carbon

dataset

display_name: dataset

interatomic-potential

display_name: interatomic-potential

machine-learning

display_name: machine-learning

neural-network-potential

display_name: neural-network-potential


en: Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain the proper balance between the black-box nature of ML frameworks and the physics of the target properties. One of the most appealing quantum-chemical properties for regression models is the electron density, and some of us recently proposed a transferable and scalable model based on the decomposition of the density onto an atom-centered basis set. The decomposition, as well as the training of the model, is at its core a minimization of some loss function, which can be arbitrarily chosen and may lead to results of different quality. Well-studied in the context of density fitting (DF), the impact of the metric on the performance of ML models has not been analyzed yet. In this work, we compare predictions obtained using the overlap and the Coulomb repulsion metrics for both the decomposition and training. As expected, the Coulomb metric used as both the DF and ML loss functions leads to the best results for the electrostatic potential and dipole moments. The origin of this difference lies in the fact that the model is not constrained to predict densities that integrate to the exact number of electrons N. Since an a posteriori correction for the number of electrons decreases the errors, we proposed a modification of the model where N is included directly into the kernel function, which allowed to lower the errors on the test and out-of-sample sets.

en: Impact of quantum-chemical metrics on the machine learning prediction of electron density

correction-number-of-electrons

display_name: correction-number-of-electrons

epfl

display_name: epfl

erc

display_name: erc

quantum-chemical-metrics

display_name: quantum-chemical-metrics

snsf

display_name: snsf


en:
We have studied electrochemical *CO-*CO coupling in explicit electrolyte with density functional theory, molecular dynamics, and metadynamics. We considered both the *CO-*CO coupling reaction and the charging process required to keep the potential constant. The charging process consists of transferring explicit cations from the electrolyte and electrons from the potentiostat to the interface. Under constant charge conditions (non-constant electrostatic potential), the *CO-*CO coupling reaction energies are relative insensitive to the charge state at the interface and the electrolyte composition and the reaction occurs with co-adsorption of water. Under constant potential conditions, the *CO-*CO coupling reaction is stabilized at lower potentials because of charging and the reaction is influenced by the electrolyte composition.
Here we have collected the data from the eight AIMD metadynamics simulations conducted in the study. Each AIMD data tar.gz file contains the VASP input files (INCAR, KPOINTS, POTCAR, ICONST), the VASP output files for the full AIMD run (OUTCAR*, PENALTYPOT), python scripts that have been used to analyze the AIMD run, data files made by those scripts (*dat), a folder (0/) used to set up single point calculations of workfunction and Bader chages at 0.125 ps intervals along the AIMD trajectory, and one folder (*.000ps) containing an example of a workfunction and Bader charge calculation.

en: Towards constant potential modeling of CO-CO coupling at liquid water-Cu(100) interfaces

ab-initio-molecular-dynamics

display_name: ab-initio-molecular-dynamics

electrocatalysis

display_name: electrocatalysis

water-metal-interfaces

display_name: water-metal-interfaces


en:
Energy efficiency is motivating the search for new high-temperature metals. Some new body-centered-cubic random multicomponent "high entropy alloys (HEAs)" based on refractory elements (Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr) possess exceptional strengths at high temperatures but the physical origins of this outstanding behavior are not known.
Here, by using a recent mechanistic theory, we have computed the high-temperature (T=1300K) yield strength based on solute strengthening of over 10 million alloys within the whole Al-Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr alloy family. Also the yield strength/density has been computed.
This database enables the efficient search of new alloys with exceptional high-temperature strength.

en: Prediction of yield strength in refractory body-centered-cubic High Entropy Alloys

high-entropy-alloys

display_name: high-entropy-alloys

high-temperature-strength

display_name: high-temperature-strength

solute-strengthening

display_name: solute-strengthening


en: We investigate optical absorption spectra obtained through time-dependent density functional theory (TD-DFT) based on nonempirical hybrid functionals that are designed to correctly reproduce the dielectric function. The comparison with state-of-the-art GW calculations followed by the solution of the Bethe-Salpeter equation (BSE-GW) shows close agreement for both the transition energies and the main features of the spectra. We confront TD-DFT with BSE-GW by focusing on the model dielectric function and the local exchange-correlation kernel. The present TD-DFT approach achieves the accuracy of BSE-GW at a fraction of the computational cost.

en: Accurate optical spectra through time-dependent density functional theory based on screening-dependent hybrid functionals

bethe-salpeter-equation

display_name: bethe-salpeter-equation

gw

display_name: gw

hybrid-functionals

display_name: hybrid-functionals

many-body-techniques

display_name: many-body-techniques

optical-absorption-spectroscopy

display_name: optical-absorption-spectroscopy


en: Temperature-induced Lifshitz transitions have been identified in several materials. Their chemical potential shows a substantial shift with changing temperature. The common feature of these materials is the coexistence of electron and hole pockets in the vicinity of the chemical potential. Here, we report the observation of temperature-induced chemical potential shift and Lifshitz transition in a layered type-II Weyl semimetal, TaIrTe4. The reversal of the polarity of the Hall resistivity and thermoelectric power (TEP) as the temperature increases clearly signal an appreciable shift of the chemical potential and change of the Fermi surface. It is corroborated by the improving agreement between the experimental TEP and the one calculated with temperature-dependent chemical potential. The complete disappearance of an electron pocket, consistent with the change of the Fermi surface when the chemical potential moves downwards, provides an evident signature of a temperature-induced Lifshitz transition in TaIrTe4.

en: Transport signatures of temperature-induced chemical potential shift and Lifshitz transition in layered type-II Weyl semimetal TaIrTe4

chemical-potential-shift

display_name: chemical-potential-shift

cscs

display_name: cscs

lifshitz-transition

display_name: lifshitz-transition

marvel

display_name: marvel

thermoelectric-power

display_name: thermoelectric-power

two-channel-model

display_name: two-channel-model

weyl-semimetal

display_name: weyl-semimetal


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

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

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

materialscloud

en:
Materials Cloud 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).

first-principles

display_name: first-principles

hybrid-functional-dft

display_name: hybrid-functional-dft

molecular-crystals

display_name: molecular-crystals

snsf

display_name: snsf


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

materialscloud

en: Materials Cloud

absorption-spectrum

display_name: absorption-spectrum

bismuth

display_name: bismuth

density-functional-theory

display_name: density-functional-theory

density-of-states

display_name: density-of-states

extreme-ultraviolet

display_name: extreme-ultraviolet

investissements-davenir

display_name: investissements-davenir

marvelosp

display_name: marvelosp

photoexcitation

display_name: photoexcitation

real-space-atomic-motion

display_name: real-space-atomic-motion

transient-reflectivity

display_name: transient-reflectivity


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

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

envi

en: Environment

cscs

display_name: cscs

dftdmft

display_name: dftdmft

electronic-structure

display_name: electronic-structure

oxygen-vacancies

display_name: oxygen-vacancies


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

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

dftuv

display_name: dftuv


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

en: Revealing hidden magneto-electric multipoles using Compton scattering

compton-scattering

display_name: compton-scattering

erc

display_name: erc

horizon-europe

display_name: horizon-europe

magneto-electric-multipoles

display_name: magneto-electric-multipoles


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

en: Confinement effects and acid strength in Zeolites

acid-base

display_name: acid-base

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics

zeolites

display_name: zeolites


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

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

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning


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

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

aluminum

display_name: aluminum

chemical-composition

display_name: chemical-composition

experimental

display_name: experimental

materials-discovery

display_name: materials-discovery

mechanical-properties

display_name: mechanical-properties

metals-and-alloys

display_name: metals-and-alloys


en:
This repository has the input files and a guide to recreate the data from "The importance of reference frame for pressure at the liquid-vapour interface" (https://arxiv.org/abs/2107.00499). It requires the Flowmol code to be downloaded and built from https://github.com/edwardsmith999/flowmol (it should work with the latest version but the paper was generated from commit c4a52d434053d676c0281449b0fce7112116fd54 or the persistent version linked to DOI https://doi.org/10.5281/zenodo.4639546). The included README.txt file outlines how to do this. The input files are also included on the Github repository. The summarised data is also included as a Python pickle (summary.p) with scripts to produce all plots from the paper. This data, which shows the profile going through a liquid vapour interface, can be analysed in Python.

The abstract for the article, which explains the importance of this data, is as follows: The local pressure tensor is non-unique, a fact which has generated confusion and debate in the seventy years since the seminal work by Irving Kirkwood. This non-uniqueness is normally attributed to the interaction path between molecules, especially in the interfacial-science community. In this work we reframe this discussion of non-uniqueness in terms of the location, or reference frame, used to measure the pressure. By using a general mathematical description of the liquid-vapour interface, we obtain a reference frame that moves with the interface through time, providing a new insight into the pressure. We compare this instantaneous moving reference frame with the fixed Eulerian one. Through this process, we show the requirement that normal pressure balance at the moving surface is satisfied by surface fluxes, however an additional corrective term based on surface curvature is required for the average pressure in a volume. We make the case that a focus on the path of integration is the cause of much of the confusion in the literature. Using an explicit reference frame with a more general derivation of pressure clarifies some of the issues of uniqueness in the pressure tensor and provides a pressure tensor which is defined at any instant in time and valid away from thermodynamic equilibrium.

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).

flowmol

display_name: flowmol

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

python-pickle

display_name: python-pickle

software-input-files

display_name: software-input-files


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

en: Structure-property maps with kernel principal covariates regression

dimensionality-reduction

display_name: dimensionality-reduction

epfl

display_name: epfl

erc

display_name: erc

kernel-methods

display_name: kernel-methods

materials-science

display_name: materials-science

max

display_name: max

snsf

display_name: snsf


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

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

materialscloud

en: Materials Cloud

cerium-oxide-surfaces

display_name: cerium-oxide-surfaces

excess-charge-localization

display_name: excess-charge-localization

facet-dependent-stability

display_name: facet-dependent-stability

lattice-relaxations

display_name: lattice-relaxations

oxygen-vacancies

display_name: oxygen-vacancies


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

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

envi

en: Environment

cineca

display_name: cineca

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

linear-response

display_name: linear-response

liouville-lanczos-approach

display_name: liouville-lanczos-approach

magnons

display_name: magnons

marvel

display_name: marvel

quantum-espresso

display_name: quantum-espresso

spin-orbit-coupling

display_name: spin-orbit-coupling

spin-wave-spectra

display_name: spin-wave-spectra


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

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

density-functional-theory

display_name: density-functional-theory

foster-boys-centers

display_name: foster-boys-centers

machine-learning

display_name: machine-learning


superconductivity-in-antiperovskites

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

en: Superconductivity in antiperovskites

title_for_slug: superconductivity-in-antiperovskites

anti-perovskites

display_name: anti-perovskites

electron-phonon-coupling

display_name: electron-phonon-coupling

superconductivity

display_name: superconductivity


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

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

experimental

display_name: experimental

large-area-device

display_name: large-area-device

nonfused-ring-electron-acceptor

display_name: nonfused-ring-electron-acceptor

organic-solar-cells

display_name: organic-solar-cells

record-efficiency

display_name: record-efficiency


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

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

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

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

message-passing

display_name: message-passing

pasc

display_name: pasc

reproducibility

display_name: reproducibility


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

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

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).

aiidalab

display_name: aiidalab

dft

display_name: dft

graphene-nanoribbons

display_name: graphene-nanoribbons

marvel

display_name: marvel

on-surface-synthesis

display_name: on-surface-synthesis

scanning-probe-microscopy

display_name: scanning-probe-microscopy

snsf

display_name: snsf


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

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

aqua-complex

display_name: aqua-complex

graph-neural-network

display_name: graph-neural-network

spectroscopy

display_name: spectroscopy