en: Carbon is often used as a conductive additive in catalyst layers to increase conductivity and catalytic activity. However, the effect of carbon addition to perovskites on the oxygen reduction (ORR) and oxygen evolution (OER) reactions is convoluted. In this work, composites of perovskite Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF) and conductive additives, carbon and indium doped tin oxide are compared. It is found that the conductive additives have differing effects on the ORR and OER activities and cobalt redox behavior, with carbon having a much more significant effect. In order to elucidate further these differences between BSCF and BSCF/Carbon, operando X-ray absorption spectroscopy (XAS) is measured simultaneously with cyclic voltammetry into the ORR and OER regions and the continuous changes in the Co oxidation state are observed with high time resolution. We theorize that carbon is enhancing the Co redox activity and as a result, the ORR and OER activities are likewise improved.

en: Influence of carbon on the dynamic changes in Co oxidation state of Ba0.5Sr0.5Co0.8Fe0.2O3-δ perovskite catalyst during the oxygen reduction and evolution reactions

envi

en: Environment

alkaline

display_name: alkaline

bscf

display_name: bscf

cobalt

display_name: cobalt

electrocatalysis

display_name: electrocatalysis

oer

display_name: oer

orr

display_name: orr

xas

display_name: xas


$oid: OBSOLETE

en: Strengthening by needle-shaped β′′ precipitates is critical in Al–Mg–Si alloys. Here, the strengthening is studied computationally at the peak-aged condition where precipitate shearing and Orowan looping are usually considered to have equal strengths. Pseudo-random precipitate microstructures are constructed based on experimental precipitate dimensions and volume fractions at peak aging. A Discrete Dislocation Dynamics method is then adapted to compute the Critical Resolved Shear Stress (CRSS) for Orowan looping of dislocations moving through the non-shearable precipitate field. The CRSS for Orowan looping is determined by a typical in-situ precipitate spacing that is smaller than the average spacing and by the dislocation core energy within a radius of ≈5b, a factor rarely considered. The matrix misfit stresses, volume fraction, and precipitate shape have small effects on the CRSS. With microstructure and property details introduced as faithfully as possible, the CRSS for Orowan looping using atomistically-calibrated core energies at room temperature is nonetheless ≈33% higher than experiments. This suggests that precipitate shearing controls strength, and analyses of (i) forces acting on the precipitates, (ii) misfit stresses inside the precipitates, (iii) first-principles results for the relevant precipitate fault energies, and (iv) simulations that mimic precipitate shearing indicate a shearing CRSS closer to experiments. Thus, Orowan looping only sets an upper bound for the CRSS even at peak aging, and further quantitative progress requires detailed modeling of precipitate shearing.

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

en: Modeling peak-aged precipitate strengthening in Al-Mg-Si alloys

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

fr: Environnement

it: Ambiente

aluminum

display_name: aluminum

discrete-dislocation-dynamics

display_name: discrete-dislocation-dynamics

metallurgy

display_name: metallurgy

orowan-mechanism

display_name: orowan-mechanism

precipitation-strengthening

display_name: precipitation-strengthening


en: Structure determination of amorphous materials remains challenging, owing to the disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder crystallography is a powerful method to determine the structure of molecular solids, but disorder leads to both a high degree of overlap between measured signals, resulting in challenges for spectral assignment, and prevents the unambiguous identification of a single modelled periodic structure as representative of the whole material. Here, we determine the atomic-level ensemble structure of the amorphous form of the drug AZD4625 by combining solid-state NMR experiments with molecular dynamics (MD) simulations and machine-learned chemical shifts. By considering the combined shifts of all 1H and 13C atomic sites in the molecule, we determine the structure of the amorphous form by identifying an ensemble of local molecular environments that are in agreement with experiment. We then extract preferred conformations and intermolecular interactions in the amorphous sample, and analyze the structure in terms of the hydrogen bonding and conformational factors that stabilize the amorphous form of the drug.

en: Atomic-level structure determination of amorphous molecular solids by NMR

amorphous-solids

display_name: amorphous-solids

machine-learning

display_name: machine-learning

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nmr

display_name: nmr

structure-determination

display_name: structure-determination


en: We present a large dataset of density functional theory calculations of thiophene (C4H4S) adsorbed on metallic and bimetallic (100) surfaces performed in Quantum Espresso.  In total 1131 different surfaces were explored resulting in 2235 total calculations.  Slabs were built from 37 different possible elements in the stoichiometry of A3B, Ll2 structures.  The 37 single element slabs, A1 structures, were included as well.  Slabs were cleaved in the (100) direction.  Two hollow adsorption sites were chosen to be explored over bimetallic surfaces and one hollow configuration for the single metal surfaces.   Bader charge analysis files are also presented.

en: High-throughput thiophene adsorption calculations on bimetallic surfaces

chemisorption

display_name: chemisorption

dft

display_name: dft

heterogenous-catalysis

display_name: heterogenous-catalysis

surfaces

display_name: surfaces

thiophene

display_name: thiophene


en:
Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys and is remarkably stable when extrapolating to structures outside its training set.
In this record, we provide a dataset containing 25,000 structures utilized for fitting the aforementioned potential, with a focus on 25 d-block transition metals, excluding Tc, Cd, Re, Os and Hg.

en: Modeling high-entropy transition-metal alloys with alchemical compression: dataset HEA25

alchemical-compression

display_name: alchemical-compression

high-entropy-alloys

display_name: high-entropy-alloys

marvel

display_name: marvel

neural-network-potential

display_name: neural-network-potential


en: We perform a large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow. We start by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predicting the electron-phonon and superconducting properties based on structural, compositional, and electronic ground-state properties. Using this machine, we evaluate the transition temperature (Tc) of approximately 200000 metallic compounds, all of which on the convex hull of thermodynamic stability (or close to it) to maximize the probability of synthesizability. Compounds predicted to have Tc values exceeding 5 K are further validated using density-functional perturbation theory. As a result, we identify 541 compounds with Tc values surpassing 10 K, encompassing a variety of crystal structures and chemical compositions. This work is complemented with a detailed examination of several interesting materials, including nitrides, hydrides, and intermetallic compounds. Particularly noteworthy is LiMoN2, which we predict to be superconducting in the stoichiometric trigonal phase, with a Tc exceeding 38 K. LiMoN2 has been previously synthesized in this phase, further heightening its potential for practical applications.

en: Sampling the materials space for conventional superconducting compounds

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

electron-phonon-coupling

display_name: electron-phonon-coupling

high-throughput

display_name: high-throughput

superconductivity

display_name: superconductivity


en:
We present an orbital-resolved extension of the Hubbard U correction to density-functional theory (DFT). Compared to the conventional shell-averaged approach, the prediction of energetic, electronic and structural properties is strongly improved, particularly for compounds characterized by both localized and hybridized states in the Hubbard manifold. The numerical values of all Hubbard parameters are readily obtained from linear-response calculations. The relevance of this more refined approach is showcased by its application to bulk solids pyrite (FeS₂) and pyrolusite (β-MnO₂), as well as to six Fe(II) molecular complexes. Our findings indicate that a careful definition of Hubbard manifolds is indispensable for extending the applicability of DFT+U beyond its current boundaries. The present orbital-resolved scheme aims to provide a computationally undemanding yet accurate tool for electronic structure calculations of charge-transfer insulators, transition-metal (TM) complexes and other compounds displaying significant orbital hybridization.
This dataset contains all Quantum ESPRESSO input and output files as well as all pseudopotentials that were used to generate the results of this study. Moreover, an ``EXAMPLES'' folder provides guidance on how to apply the LR-cDFT approach to evaluate orbital-resolved DFT+U parameters in practise.

en: Orbital-resolved DFT+U for molecules and solids

cdft

display_name: cdft

constrained-dft

display_name: constrained-dft

dftu

display_name: dftu

dftuv

display_name: dftuv

hubbard

display_name: hubbard

linear-response

display_name: linear-response

lr-cdft

display_name: lr-cdft

orbital-resolved

display_name: orbital-resolved

quantum-espresso

display_name: quantum-espresso


en: We use first-principles density functional theory to investigate how the polar distortion is affected by doping in multiferroic hexagonal yttrium manganite, h-YMnO₃. While the introduction of charge carriers tends to suppress the polar distortion in conventional ferroelectrics, the behavior in improper geometric ferroelectrics, of which h-YMnO₃ is the prototype, has not been studied to date. Using both background charge doping and atomic substitution, we find an approximately linear dependence of the polar distortion on doping concentration, with hole doping reducing and electron doping enhancing it. We show that this behavior is a direct consequence of the improper geometric nature of the ferroelectricity. In addition to its doping effect, atomic substitution can further suppress or enhance the polar distortion through changes in the local chemistry and geometry.

en: Interplay between ferroelectricity and metallicity in hexagonal YMnO₃

erc

display_name: erc

ethz

display_name: ethz


en:
In a recent preprint, entitled: "Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in Germanium", we presented a novel Neural-Network (NN)  interatomic potential for Ge. We recall that Ge phases different from the cubic-diamond one are of particular interest for applications. Hexagonal Ge, for instance, displays superior optical properties. It is therefore important to investigate how, exploiting pressure, Ge can be transformed into different allotropes. In order to build a potential tackling kinetics of pressure-induced phase transformations, several kinetic paths (mainly sampled using the solid-state Nudged Elastic Band method) were added to the database, following a suitable active-learning procedure. Energies, forces, and stressed relative to the various configurations were computed ab initio using VASP with the PBE functional. The NN potential was trained using the Deep Potential Molecular Dynamic package (DeePMDkit). The potential greatly reproduces the relative stability of several Ge phases and yields at least a semi-quantitative description of the energetics along complex phase-transformation paths.
In the present archive, we provide the full potential for use in LAMMPS and ASE, together with the full database produced using VASP.

en: A NN-Potential for phase transformations in Ge

envi

en: Environment

deepmd

display_name: deepmd

germanium

display_name: germanium

neural-network

display_name: neural-network

phase-transitions

display_name: phase-transitions


en: A theory for strengthening for multicomponent non-dilute alloys possessing short-range order (SRO) has recently been developed. The theory predicts that, in addition to a well-known athermal strengthening, there is a notable effect of SRO on the solute–dislocation interactions that can decrease or increase the strength relative to a random alloy. Here, carefully designed atomistic simulations in a model binary NbW alloy are used to demonstrate that alloy strength due to solute–dislocation interactions can be increased or decreased, depending on the SRO and consistent with theoretical predictions. Specifically, SRO is introduced using fictitious solute– solute interactions in an alloy system with very small true solute–solute interactions, and the Nudged Elastic Band (NEB) method is then used to compute the energy barriers for edge dislocation motion for various levels of SRO. Energy barriers, and hence alloy strengths, can be decreased when the Warren-Cowley SRO parameters are negative (attraction of unlike solutes). The theoretical predictions for the same system are in reasonable quantitative agreement with the simulation results. These findings demonstrate the unexpected possibility of reduced strength due to SRO and also further validate the analytical theory as a tool to guide alloy design.

materialscloud

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

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

en: Materials Cloud

en: Atomistic simulations reveal strength reductions due to short-range order in alloys

alloys

display_name: alloys

marvel

display_name: marvel

short-range-order

display_name: short-range-order

simulation

display_name: simulation

theory

display_name: theory

yield-strength

display_name: yield-strength


en: We investigate the interplay of spin-orbit coupling, electronic correlations, and lattice distortions in the 5d¹ double perovskite Ba₂MgReO₆. Combining density-functional theory (DFT) and dynamical mean-field theory (DMFT), we establish the Mott-insulating character of Ba₂MgReO₆ in both its cubic and tetragonal paramagnetic phases. Despite substantial spin-orbit coupling, its impact on the formation of the insulating state is minimal, consistent with theoretical expectations for d¹ systems. We further characterize the electronic properties of the cubic and tetragonal phases by analyzing spectral functions and local occupations in terms of multipole moments centered on the Re sites. Our results confirm the presence of ferroically ordered z² quadrupoles in addition to the antiferroic x²-y²-type order. We compare two equivalent but complementary descriptions in terms of either effective Re-t2g frontier orbitals or more localized atomic-like Re-d and O-p orbitals. The former maps directly on a physically intuitive picture in terms of nominal d¹ Re cations, while the latter explicitly demonstrates the role of hybridization with the ligands in the spin-orbit splitting and the formation of the charge quadrupoles around the Re sites. Finally, we compare our DFT+DMFT results with a previous DFT+U study of the tetragonal paramagnetic state. We find good qualitative agreement for the dominant charge quadrupoles, but also notable differences in the corresponding spectral functions, underscoring the need for more comparative studies between these two methods.

en: Probing the Mott-insulating behavior of Ba₂MgReO₆ with DFT+DMFT

constrained-random-phase-approximation

display_name: constrained-random-phase-approximation

density-functional-theory

display_name: density-functional-theory

double-perovskite

display_name: double-perovskite

dynamical-mean-field-theory

display_name: dynamical-mean-field-theory

mott-insulator

display_name: mott-insulator


en:
High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals (d-block transition metals, excluding Tc, Cd, Re, Os and Hg) to study the tendency of different elements to segregate at the surface of a HEA.
In this record, we provide a dataset HEA25S, containing 10000 bulk HEA structures (Dataset O), 2640 HEA surface slabs (Dataset A), together with 1000 bulk and 1000 surface slabs snapshots from the molecular dynamics (MD) runs (Datasets B and C), and 500 MD snapshots of the 25 elements Cantor-style alloy surface slabs.
We also provide the HEA25-4-NN and HEA25S-4-NN final models, which were used in the study. Full description of both the dataset and the models can be found the reference paper below.

en: Surface segregation in high-entropy alloys from alchemical machine learning: dataset HEA25S

alchemical-compression

display_name: alchemical-compression

atomistic-modeling

display_name: atomistic-modeling

cosmo

display_name: cosmo

high-entropy-alloys

display_name: high-entropy-alloys

machine-learning

display_name: machine-learning

surface-segregation

display_name: surface-segregation


en: A strong driving force for charge separation and transfer in semiconductors is essential for designing effective photoelectrodes for solar energy conversion. While defect engineering and polarization alignment can enhance this process, their potential interference within a photoelectrode remains unclear. Here we show that oxygen vacancies in bismuth vanadate (BiVO4) can create defect dipoles due to a disruption of symmetry. The modified photoelectrodes exhibit a strong correlation between charge separation and transfer capability and external electrical poling, which is not seen in unmodified samples. Applying poling at -150 Volt boosts charge separation and transfer efficiency to over 90 %. A photocurrent density of 6.3 mA cm-2 is achieved on the photoelectrode after loading with a nickel-iron oxide-based cocatalyst. Furthermore, using generated holes for methane partial oxidation can produce methanol with a Faradaic efficiency of approximately 6 %. These findings provide valuable insights into the photoelectrocatalytic conversion of greenhouse gases into valuable chemical products.

en: Oxygen vacancy induced defect dipoles in BiVO4 for photoelectrocatalytic partial oxidation of methane

arc

display_name: arc

electronic-structure

display_name: electronic-structure

experimental

display_name: experimental


en: The development of unified regenerative fuel cells (URFC) necessitates an active and stable bifunctional oxygen electrocatalyst. The unique challenge of possessing high activity for both the oxygen reduction (ORR) and oxygen evolution (OER) reactions, while maintaining stability over a wide potential window impedes the design of bifunctional oxygen electrocatalysts. Herein, two design strategies are explored to optimize their performance. The first incorporates active sites for ORR and OER, Mn and Co, into a single perovskite structure, which is achieved with the perovskites Ba0.5Sr0.5Co0.8Mn0.2O3-δ (BSCM) and La0.5Ba0.25Sr0.25Co0.5Mn0.5O3-δ (LBSCM). The second combines an active ORR perovskite catalyst (La0.4Sr0.6MnO3-δ (LSM)) with an OER active perovskite catalyst (Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF)) in a physical mixed composite (BSCF/LSM). The success of the two strategies is investigated by measuring the catalysts' catalytic performance and response to alternating reducing and oxidizing potentials to mimic the dynamic conditions experienced during operation of URFCs. Additionally, the continuous, potentiodynamic change in Mn, Co, and Fe oxidation state during ORR and OER is elucidated with operando X-ray absorption spectroscopy (XAS) measurements, revealing key insights into the nature of the active sites. The results reveal important catalyst physiochemical properties and provide a guide for future research and design principles for bifunctional oxygen electrocatalysts.

en: Designing bifunctional perovskite catalysts for the oxygen reduction and evolution reactions

oer

display_name: oer

orr

display_name: orr

perovskite

display_name: perovskite


en: We calculate the screened electron-electron interaction for the charge-disproportionated insulator CaFeO₃ using the constrained random-phase approximation (cRPA). While in many correlated materials, the formation of a Mott-insulating state is driven by a large local Coulomb repulsion, represented by the Hubbard U, several cases have been identified more recently where U is strongly screened and instead the Hund's interaction J dominates the physics. Our results confirm a strong screening of the local Coulomb repulsion U in CaFeO₃ whereas J is much less screened and can thus stabilize a charge-disproportionated insulating state. This is consistent with the case of the rare-earth nickelates where similar behavior has been demonstrated. In addition, we validate some common assumptions used for parametrizing the local electron-electron interaction in first-principles calculations based on density-functional theory (DFT), assess the dependence of the interaction on the choice of correlated orbitals, and discuss the use of the calculated interaction parameters in DFT+U calculations of CaFeO₃. Our work also highlights certain limitations for the direct use of cRPA results in DFT-based first-principles calculations, in particular for systems with strong entanglement between the correlated and uncorrelated bands.

en: Calculation of screened Coulomb interaction parameters for the charge-disproportionated insulator CaFeO₃

charge-disproportionation

display_name: charge-disproportionation

constrained-random-phase-approximation

display_name: constrained-random-phase-approximation

metal-insulator-transition

display_name: metal-insulator-transition


en: Due to presence of magnetic field gradient needed for coherent spin control, dephasing of single-electron spin qubits in silicon quantum dots is often dominated by 1/f charge noise. We investigate theoretically fluctuations of ground state energy of an electron in gated quantum dot in realistic Si/SiGe structure. We assume that the charge noise is caused by motion of charges trapped at the semiconductor-oxide interface. We consider a realistic range of trapped charge densities, ρ∼10¹⁰ cm⁻², and typical lenghtscales of isotropically distributed displacements of these charges, δr≤1 nm, and identify pairs (ρ,δr) for which the amplitude and shape of the noise spectrum is in good agreement with spectra reconstructed in recent experiments on similar structures.

en: Simulation of 1/f charge noise affecting a quantum dot in a Si/SiGe structure

charge-noise

display_name: charge-noise

quantum-dots

display_name: quantum-dots

simulation

display_name: simulation


en: Contact-Electro-Catalysis is an emerging catalytic principle that takes advantage of exchanges of electrons occurring through contact electrification events at solid-liquid interfaces to initiate or drive the catalysis of redox reactions. In this publication, the authors have proven the ability of various polymer insulators to catalyze the reduction of a wide variety of metal ions in aqueous solution, in both aerobic and anaerobic conditions. This property of the dielectric polymers was employed to design a 1-step method to selectively extract gold from e-waste leachates. In anaerobic conditions, the rate of the reactions increase due to the absence of competition form oxygen for the electrons. The influence of metal ions in solution on the distance between O₂ and the polymer chain of polytetrafluoroethylene was evaluated, as well as the resulting adsorption energy. The effect of tacticity on the ability of polymers such as PP to perform the contact-electro-catalytic reduction of oxygen was also evaluated. The files in this repository contains the position of the atoms before and after relaxation in all cases that were used to conduct the calculations.

en: Reduction of precious metal ions in aqueous solutions by contact-electro-catalysis

envi

en: Environment

catalysis

display_name: catalysis

contact-electrification

display_name: contact-electrification

contact-electro-catalysis

display_name: contact-electro-catalysis

vasp

display_name: vasp


en: The functionality of atomic quantum emitters is intrinsically linked to their host lattice coordination. Structural distortions that spontaneously break the lattice symmetry strongly impact their optical emission properties and spin-photon interface. In a recent manuscript, we report on the direct imaging of charge state-dependent symmetry breaking of two prototypical atomic quantum emitters in mono- and bilayer MoS₂ by scanning tunneling microscopy (STM) and non-contact atomic force microscopy (nc-AFM). By changing the built-in substrate chemical potential, different charge states of sulfur vacancies (VacS) and substitutional rhenium dopants (ReMo) can be stabilized. VacS⁻¹ as well as ReMo⁰ and ReMo⁻¹ exhibit local lattice distortions and symmetry-broken defect orbitals attributed to a Jahn-Teller effect (JTE) and pseudo-JTE, respectively. By mapping the electronic and geometric structure of single point defects, we disentangle the effects of spatial averaging, charge multistability, configurational dynamics, and external perturbations that often mask the presence of local symmetry breaking. This record contains data to support the results discussed in our manuscript.

materialscloud

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

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

en: Materials Cloud

en: Charge state-dependent symmetry breaking of atomic defects in transition metal dichalcogenides

dfg

display_name: dfg

erc

display_name: erc

jahn-teller-effect

display_name: jahn-teller-effect

snsf

display_name: snsf

stm

display_name: stm


en: Several methods have been developed to improve the predictions of density functional theory (DFT) in the case of strongly correlated electron systems. Out of these approaches, DFT+U, which corresponds to a static treatment of the local interaction, and DFT combined with dynamical mean field theory (DFT+DMFT), which considers local fluctuations, have both proven incredibly valuable in tackling the description of materials with strong local electron-electron interactions. While it is in principle known that the Hartree-Fock (HF) limit of the DFT+DMFT approach should recover DFT+U, demonstrating this equivalence in practice is challenging, due to the very different ways in which the two approaches are generally implemented. In this work, we introduce a way to perform DFT+U calculations in QE using Wannier functions as calculated by Wannier90, which allows us to use the same Hubbard projector functions both in DFT+U and in DFT+DMFT. We benchmark these DFT+U calculations against DFT+DMFT calculations where the DMFT impurity problem is solved within the HF approximation. Considering a number of prototypical materials including NiO, MnO, LaMnO₃, and LuNiO₃, we establish the sameness of the two approaches. Finally, we showcase the versatility of our approach by going beyond the commonly used atomic orbital-like projectors by performing DFT+U calculations for VO₂ using a special set of bond-centered Wannier functions.

en: Explicit demonstration of the equivalence between DFT+U and the Hartree-Fock limit of DFT+DMFT

dftdmft

display_name: dftdmft

dftu

display_name: dftu

hartree-fock

display_name: hartree-fock

marvel

display_name: marvel


en: Graphene nanoribbons (GNRs), nanometer-wide strips of graphene, have garnered significant attention due to their tunable electronic and magnetic properties arising from quantum confinement. A promising approach to manipulate their electronic characteristics involves substituting carbon with heteroatoms, such as nitrogen, with different effects predicted depending on their position. In a recent publication, we present the extension of the edges of 7-atom-wide armchair graphene nanoribbons (7-AGNRs) with pyridine rings, achieved on a Au(111) surface via on-surface synthesis. High-resolution structural characterization confirms the targeted structure, showcasing the pre-dominant formation of carbon-nitrogen (C-N) bonds (over 90% of the units) during growth. This favored bond formation pathway is elucidated and confirmed through density functional theory (DFT) simulations. Furthermore, an analysis of the electronic properties reveals a reduction of the band gap of the GNR, accompanied by the presence of nitrogen-localized states. Our results underscore the successful formation of C-N bonds on the metal surface, providing insights for designing new GNRs that incorporate substitutional nitrogen atoms to precisely control their electronic properties. The record contains data that support the findings described in the publication.

en: Preferential graphitic-nitrogen formation in pyridine-extended graphene nanoribbons

aiidalab

display_name: aiidalab

cscs

display_name: cscs

dft

display_name: dft

graphene-nanoribbons

display_name: graphene-nanoribbons

nitrogen-doping

display_name: nitrogen-doping


en: Accurate descriptions of intermolecular interactions are of great importance in simulations of molecular liquids. We present an electronic structure method that combines the accuracy of the Harris functional approach with the computational efficiency of approximately linear-scaling density functional theory (DFT). The proposed method allows for simulations with accuracies close to the Kohn-Sham DFT reference. Embedded in the CP2K program package, the method is designed to enable ab initio molecular dynamics simulations of molecular solutions for system sizes of several thousands of atoms. As example of production applications we applied the method to molecular dynamics simulations in the isobaric-isothermal ensemble of the binary mixtures cyclohexane-methanol and toluene-methanol at different molar fractions of methanol. This record contains all CP2K input files necessary for the MD simulations, as well as 30ps trajectory files, and benchmark data of the energy correction.

en: A variational formulation of the Harris functional as correction to approximate Kohn-Sham density functional theory

cyclohexane

display_name: cyclohexane

harris-functional

display_name: harris-functional

isobaric-isothermal-ensemble

display_name: isobaric-isothermal-ensemble

linear-scaling-dft

display_name: linear-scaling-dft

methanol

display_name: methanol

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

subsystem-dft

display_name: subsystem-dft

toluene

display_name: toluene


en: Koopmans spectral functionals are a class of orbital-density-dependent functionals designed to accurately predict spectroscopic properties. They do so markedly better than their Kohn-Sham density-functional theory counterparts, as demonstrated in earlier works on benchmarks of molecules and bulk systems. This work is a complementary study where --- instead of comparing against real, many-electron systems --- we test Koopmans spectral functionals on Hooke's atom, a toy two-electron system that has analytical solutions for particular strengths of its harmonic confining potential. As these calculations clearly illustrate, Koopmans spectral functionals do an excellent job of describing Hooke's atom across a range of confining potential strengths. This work also provides broader insight into the features and capabilities of Koopmans spectral functionals more generally.

en: Testing Koopmans spectral functionals on the analytically-solvable Hooke's atom

electronic-structure

display_name: electronic-structure

hookes-atom

display_name: hookes-atom

koopmans-spectral-functionals

display_name: koopmans-spectral-functionals

orbital-density-dependent-functionals

display_name: orbital-density-dependent-functionals


en: Garnets, known since the early stages of human civilization, have found important applications in modern technologies including magnetorestriction, spintronics, lithium batteries, etc. The overwhelming majority of experimentally known garnets are oxides, while explorations (experimental or theoretical) for the rest of the chemical space have been limited in scope.  A key issue is that the garnet structure has a large primitive unit cell, requiring an enormous amount of computational resources. To perform a comprehensive search of the complete chemical space for new garnets, we combine recent progress in graph neural networks with high-throughput calculations. We apply the machine learning model to identify the potential (meta-)stable garnet systems before systematic density-functional calculations to validate the predictions. In this way, we discover more than 600 ternary garnets with distances to the convex hull below 100~meV/atom with a variety of physical and chemical properties. This includes sulfide, nitride and halide garnets. This record includes the results of 15742 vasp calculations of the ternary garnets investigated. All calculations were performed using the PBE functional.

en: Machine learning guided high-throughput search of non-oxide garnets

density-functional-theory

display_name: density-functional-theory

garnets

display_name: garnets

vasp

display_name: vasp


en: We use piecewise-linear functionals to study the polaron energy landscape and hopping rates in 𝜷-Ga₂O₃, which we adopt as an example of an anisotropic material hosting multiple polaronic states. We illustrate various functionals for polaron localization, including a hybrid functional and two types of semilocal functionals, and discuss how to ensure the piecewise linearity condition. Then, we determine the formation energies of stable polarons, and show that single-site and multi-site polaronic states can be found in close energetic competition. We calculate the hyperfine and superhyperfine parameters associated with each polaron, and discuss the comparison with experiment. Next, we perform nudged-elastic-band calculations to determine energy landscapes and hole transfer rates of all first-nearest-neighbor polaron hoppings. We show that when the piecewise linearity condition is ensured polaron properties are robust upon variation of the functional adopted, including formation energies, energy barriers, and charge transfer rates. This supports the use of semilocal functionals for calculating polaron transport properties.

en: Polaron hopping through piecewise-linear functionals

dftu

display_name: dftu

gammadft

display_name: gammadft

hybrid-functionals

display_name: hybrid-functionals

piecewise-linearity

display_name: piecewise-linearity

polaron-hopping

display_name: polaron-hopping

polarons

display_name: polarons

self-interaction

display_name: self-interaction


en: Transition-metal-based kagome materials at van Hove filling are a rich frontier for the investigation of novel topological electronic states and correlated phenomena. To date, in the idealized two-dimensional kagome lattice, topologically Dirac surface states (TDSSs) have not been unambiguously observed, and the manipulation of TDSSs and van Hove singularities (VHSs) remains largely unexplored. Here, we reveal TDSSs originating from a Z2 bulk topology and identify multiple VHSs near the Fermi level (EF) in magnetic kagome material GdV6Sn6. Using in situ surface potassium deposition, we successfully realize manipulation of the TDSSs and VHSs. The Dirac point of the TDSSs can be tuned from above to below EF, which reverses the chirality of the spin texture at the Fermi surface. These results establish GdV6Sn6 as a fascinating platform for studying the nontrivial topology, magnetism, and correlation effects native to kagome lattices. They also suggest potential application of spintronic devices based on kagome materials.

en: Tunable topological Dirac surface states and van Hove singularities in kagome metal GdV6Sn6

envi

en: Environment

arpesdft

display_name: arpesdft

nccr-marvel

display_name: nccr-marvel

tdsss-and-vhss

display_name: tdsss-and-vhss


en:
Interfacing a topological insulator (TI) with an s-wave superconductor (SC) is a promising material platform that offers the possibility to realize a topological superconductor through which Majorana-based topologically protected qubits can be engineered. In our computational study of the prototypical SC/TI interface between Nb and Bi₂Te₃, we identify the benefits and possible bottlenecks of this potential Majorana material platform. Bringing Nb in contact with the TI film induces charge doping from the SC to the TI, which shifts the Fermi level into the TI conduction band. For thick TI films, this results in band bending leading to the population of trivial TI quantum-well states at the interface. In the superconducting state, we uncover that the topological surface state experiences a sizable superconducting gap-opening at the SC/TI interface, which is furthermore robust against fluctuations of the Fermi energy. We also show that the trivial interface state is only marginally proximitized, potentially obstructing the realization of Majorana-based qubits in this material platform.
This dataset contains the data for the DFT-based calculations for interfaces between the s-wave superconductor Nb and the topological insulator Bi₂Te₃ which is discussed in the publication referenced below.

materialscloud

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

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

en: Materials Cloud

en: Dataset of proximity induced superconductivity in a topological insulator

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

superconductivity

display_name: superconductivity

topological-materials

display_name: topological-materials

topological-superconductor

display_name: topological-superconductor


en: Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a key bottleneck for NMR crystallography. We recently introduced ShiftML, a machine-learning model of chemical shifts in molecular solids, trained on minimum-energy geometries of materials composed of C, H, N, O, and S that provides rapid chemical shift predictions with density functional theory (DFT) accuracy. Here, we extend the capabilities of ShiftML to predict chemical shifts for both finite temperature structures and more chemically diverse compounds, while retaining the same speed and accuracy. For a benchmark set of 13 molecular solids, we find a root-mean-squared error of 0.47 ppm with respect to experiment for 1H shift predictions (compared to 0.35 ppm for explicit DFT calculations), while reducing the computational cost by over four orders of magnitude.

en: A machine learning model of chemical shifts for chemically and structurally diverse molecular solids

machine-learning

display_name: machine-learning

snsf

display_name: snsf


en:
With increasing interest in Majorana physics for possible quantum bit applications, a large interest has been developed to understand the properties of the interface between a s-type superconductor and a topological insulator. Up to this point the interface analysis was mainly focused on in-situ prepared Josephson junctions, which consist of two coupled single interfaces or to ex-situ fabricated single interface devices. In our work we utilize a novel fabrication process, combining selective area growth and shadow evaporation which allows the characterization of a single in-situ fabricated Nb/(Bi0.15Sb0.85)2Te3 nano interface. The resulting high interface transparency, is apparent by a zero bias conductance increase by a factor of 1.7. Furthermore, we present a comprehensive differential conductance analysis of our single in-situ interface for various magnetic fields, temperatures and gate voltages. Additionally, density functional theory calculations of the superconductor/topological insulator interface are performed in order to explain the peak-like shape of our differential conductance spectra and the origin of the observed smearing of conductance features.

This dataset contains the DFT and experimental raw data discussed in the associated publication.

en: Characterization of single in situ prepared interfaces composed of niobium and a selectively-grown (Bi1-xSbx)2Te3 topological insulator nanoribbon

density-functional-theory

display_name: density-functional-theory

experiment

display_name: experiment

majorana

display_name: majorana

transport

display_name: transport


en: The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is computationally demanding, while simulations of small cells are often plagued with non-physical effects that are a consequence of the system's finite size. Therefore, one is often forced to use empirical models that describe the physics of the material in terms of effective interaction terms, that are fitted using the results from DFT, to perform simulations that do not suffer from finite size effects. In this study we use a machine-learning (ML) potential trained on DFT, in combination with accelerated sampling techniques, to converge the thermodynamic properties of Barium Titanate (BTO) with first-principles accuracy and a full atomistic description. Our results indicate that the predicted Curie temperature depends strongly on the choice of DFT functional and system size, due to the presence of emergent long-range directional correlations in the local dipole fluctuations. Our findings demonstrate how the combination of ML models and traditional bottom-up modeling allow one to investigate emergent phenomena with the accuracy of first-principles calculations and the large size and time scales afforded by empirical models.

en: Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

dielectric-correlations

display_name: dielectric-correlations

ferroelectrics

display_name: ferroelectrics

finite-size-effects

display_name: finite-size-effects

hybrid-dft-ml

display_name: hybrid-dft-ml

machine-learning-potentials

display_name: machine-learning-potentials

marvel

display_name: marvel

metadynamics

display_name: metadynamics

phase-transitions

display_name: phase-transitions

sinergia

display_name: sinergia


en:
We present a sulfur K-edge X-ray absorption near-edge structure (XANES) database of 18 crystalline and 48 amorphous Lithium-Phosphorous-Sulfur (LPS) compounds. The database contains a total of 2681 XANES spectra of symmetrically inequivalent absorbing S sites. Structures were taken from Materials Cloud entry 2022.17 (archive.materialscloud.org/record/2022.17) and were originally generated by systematically removing Li, P and S atoms from known crystal structures using an evolutionary algorithm and an artificial neural network based interatomic potential. The details of this procedure can be found in Guo et al. (see references below). From this data set, low-energy structures were selected for spectral simulations. The excited electron and core hole method as implemented in VASP 6.2.1 was used to compute the XANES spectra for each symmetrically inequivalent Sulfur atom. The details of the VASP simulations can be found in the associated manuscript.

Acknowledgements: We acknowledge financial support by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, Contract No. DE-SC0012704. These results used the computational resources of the Center for Functional Nanomaterials and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under the Contract No. DE-SC0012704. We also acknowledge computing resources from Columbia University's Shared Research Computing Facility project, which is supported by NIH Research Facility Improvement Grant 1G20RR030893-01, and associated funds from the New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR) Contract C090171, both awarded April 15, 2010.

en: Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes

amorphous-lps

display_name: amorphous-lps

lps

display_name: lps

vasp

display_name: vasp

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


en: Charge transport plays a crucial role in manifold potential applications of two-dimensional materials, including field effect transistors, solar cells, and transparent conductors. At most operating temperatures, charge transport is hindered by scattering of carriers by lattice vibrations. Assessing the intrinsic phonon-limited carrier mobility is thus of paramount importance to identify promising candidates for next-generation devices. Here we provide a framework to efficiently compute the  drift and Hall carrier mobility of two-dimensional materials through the Boltzmann transport equation by relying on a Fourier-Wannier interpolation. Building on a recent formulation of long-range contributions to dynamical matrices and phonon dispersions [Phys. Rev. X 11, 041027 (2021)], we extend the approach to electron-phonon coupling including the effect of dynamical dipoles and quadrupoles. We identify an unprecedented contribution associated with the Berry connection that is crucial to preserve the Wannier-gauge covariance of the theory. This contribution is not specific to 2D crystals, but also concerns the 3D case, as we demonstrate via an application to bulk SrO. We showcase our method on a wide selection of relevant monolayers ranging from SnS₂ to MoS₂, graphene, BN, InSe, and phosphorene. We also discover a non-trivial temperature evolution of the Hall hole mobility in InSe whereby the mobility increases with temperature above 150~K due to the mexican-hat electronic structure of the InSe valence bands. Overall, we find that dynamical quadrupoles are essential and can impact the carrier mobility in excess of 75%.

en: Long-range electrostatic contribution to the electron-phonon couplings and mobilities of two-dimensional and bulk materials

ab-initio

display_name: ab-initio

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles


en: This database contains the neural network potential (NNP) model and training data for aqueous ZnCl₂ solutions from 1 m to 30 m. The NNP model can be used to compute total energies and atomic forces, with one of its major applications being large-scale molecular dynamics (MD) simulations. The model was trained using DeePMD-kit v2.2.1, with training data generated through an active learning approach implemented in DP-GEN. The energies and forces in the training set were obtained from density functional theory (DFT) calculations using the SCAN exchange-correlation functional performed using Quantum ESPRESSO. Further details on the ab initio calculation procedures and model training methodology are available in the associated manuscript (see reference below).

en: Resolving the solvation structure and transport properties of aqueous zinc electrolytes from salt-in-water to water-in-salt using neural network potential

deepmd

display_name: deepmd

neural-network-potential

display_name: neural-network-potential

water

display_name: water

water-in-salt

display_name: water-in-salt


en: Organic solvents are widely used in polymer synthesis, despite their use lengthening purification steps and generating chemical waste. All-dry synthesis techniques, such as initiated Chemical Vapour Deposition (iCVD) polymerization, eliminate the use of solvents, however, only a narrow palette of material properties is accessible. Inspired by the principles of solvent engineering in solution synthesis, we report a strategy to broaden this palette by vapour-phase complexing (namely, vapour-phase solvation) mediated by hydrogen-bonding. Broad ranges of polymer chain length, as well as the mechanical strength and variety of film surface morphology are demonstrating using this strategy. We further achieve an unprecedented solvation modality; more specifically, interfacial solvation. The molecular interactions, locations of solvation, and kinetics of the coupled solvation-adsorption-polymerization process are investigated using molecular dynamic simulations and experimental validation of a theoretical kinetic model. The strategy can be applied to various methacrylate and vinyl monomers. Solvation in all-dry polymerization offers a new degree of freedom in polymer design and synthesis with improved environmental benignness, pointing to accelerated discovery of polymer thin films by simply introducing active solvents in the vapour phase. Here we provided the code for data processing to the result of the molecular dynamic simulation.

en: Engineering solvation in initiated Chemical Vapor Deposition (iCVD) for process-property control

envi

en: Environment

icvd

display_name: icvd

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

polymer-thin-film

display_name: polymer-thin-film


en: Mn₅Si₃ displays an unusual and complex magnetic ground state, which is considered to be the origin of the anomalous transport and thermodynamic properties that it exhibits. We report the magnetic exchange couplings of the noncollinear antiferromagnetic phase of Mn₅Si₃ using inelastic neutron scattering measurements and density functional theory calculations. We determine the ground-state spin configuration and compute its magnon dispersion relations which are in good agreement with the ones obtained experimentally. Furthermore, we investigate the evolution of the spin texture under the application of an external magnetic field to demonstrate theoretically the multiple field-induced phase transitions that Mn₅Si₃ undergoes. Finally, we model the stability of some of the material's magnetic moments under a magnetic field and we find that very susceptible magnetic moments in a frustrated arrangement can be tuned by the field. This data set contains the data relevant to perform the DFT calculations with the JuKKR code, the spin dynamics simulations with the Spirit code, the spin-wave calculations with the SWIS code, and the neutron scattering experimental data.

materialscloud

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

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

en: Materials Cloud

en: Complex magnetic structure and spin waves of the noncollinear antiferromagnet 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


fatigue-database-of-high-entropy-alloys

en: Fatigue failure of metallic structures is of great concern to industrial applications. A material will not be able to practically useful if it is prone to fatigue failure. To take the advantage of lately emerged high entropy alloys (HEAs) for designing novel fatigue-resistant alloys, we compiled a fatigue database of HEAs from the literature reported till the yearend of 2021. The database is subdivided into three categories, i.e., low-cycle fatigue (LCF), high-cycle fatigue (HCF), and fatigue crack growth rate (FCGR), which contains 15, 23, and 28 distinct data records, respectively. Each data record in any of three categories is characteristic of a summary, which is comprised of alloy composition, key fatigue properties, and additional information influential to or interrelated with fatigue (e.g., material processing history, phase constitution, grain size, uniaxial tensile properties, and fatigue testing conditions), and an individual dataset, which makes up the original fatigue testing curve.

en: Fatigue database of high entropy alloys

title_for_slug: fatigue-database-of-high-entropy-alloys

fatigue-crack-growth-rate

display_name: fatigue-crack-growth-rate

high-cycle-fatigue

display_name: high-cycle-fatigue

high-entropy-alloy

display_name: high-entropy-alloy

low-cycle-fatigue

display_name: low-cycle-fatigue


en: Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We successfully interface off-the-shelf battery cycling hardware with the computational workflow management software AiiDA, allowing us to control experiments, while ensuring trust in the data by tracking its provenance. We design user interfaces compatible with this tooling, which span the inventory, experiment design, and result analysis stages. Other features, including monitoring of workflows and import of externally generated and legacy data are also implemented. Finally, the full software stack required for this work is made available in a set of open-source packages.

en: A bridge between trust and control: Computational workflows meet automated battery cycling

aurora

display_name: aurora

battery

display_name: battery

big-map

display_name: big-map

cycling

display_name: cycling

premise

display_name: premise

robot

display_name: robot

workflow

display_name: workflow


en: Understanding the phononic origin of the infrared dielectric properties of yttria (Y₂O₃) and other rare-earth sesquioxides (RE₂O₃) is a fundamental task in the search of appropriate RE₂O₃ materials that serve particular infrared optical applications. We herein investigate the infrared dielectric properties of RE₂O₃ (RE = Y, Gd, Ho, Lu) using DFT-based phonon calculations and Lorentz oscillator model. The abundant IR-active optical phonon modes that are available for effective absorption of photons result in high reflectance of RE₂O₃, among which four IR-active modes originated from large distortions of REO₆ octahedra are found to contribute dominantly to the phonon dielectric constants. Particularly, the present calculation method by considering one-phonon absorption process is demonstrated with good reliability in predicting the infrared dielectric parameters of RE₂O₃ at the far-infrared as well as the vicinity of mid-infrared region, and the potential cutoff frequency/wavelength of its applicability is disclosed as characterized by the maximum frequency of IR-active longitudinal phonon modes. The results deepen the understanding on infrared dielectric properties of RE₂O₃, and aid the computational design of materials with appropriate infrared properties.

en: Phononic origin of the infrared dielectric properties of RE₂O₃ (RE = Y, Gd, Ho, Lu) compounds

first-principles

display_name: first-principles

infrared

display_name: infrared

phonon

display_name: phonon

sesquioxides

display_name: sesquioxides


en:
Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and singlet exciton size for the discovery of singlet fission materials. We show that uncertainty in the model predictions can control how far the genetic optimization moves away from previously known molecules.
Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom-rich mesoionic compounds as acceptors for charge-transfer mediated singlet fission. When included in larger conjugated donor-acceptor systems, these units exhibit strong localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells. As the proposed candidates are composed of fragments from synthesized molecules, they are likely synthetically accessible.

en: Inverse design of singlet fission materials with uncertainty-controlled genetic optimization

genetic-algorithm

display_name: genetic-algorithm

inverse-design

display_name: inverse-design

machine-learning

display_name: machine-learning

singlet-fission

display_name: singlet-fission

uncertainty-quantification

display_name: uncertainty-quantification


en: Singlet fission (SF) is a two-step process in which a singlet splits into two triplets throughout the so-called correlated triplet-pair (1TT) state. Intramolecular SF (iSF) materials in particular, have attracted growing interest as they can be easily implemented in single junction solar cells and boost their power conversion efficiency. Still, the potential of iSF materials such as polymers and oligomers for photovoltaic applications has been partially hindered by their ability to go beyond the 1TT intermediate and generate free triplets, which mechanism remains poorly understood. In this work, the main aspects governing the 1TT dissociation in donor-acceptor copolymers and the key features that optimize this process are exposed. First, we show that both thermodynamics and kinetics play a crucial role in the intramolecular triplet-pair separation and second, we uncover the inherent flexibility of the donor unit as the fundamental ingredient to optimize them simultaneously. Overall, these results provide a better understanding of the intramolecular 1TT dissociation process and establish a new paradigm for the development of novel iSF active materials.

en: Optimizing the thermodynamics and kinetics of the triplet-pair dissociation in donor-acceptor copolymers for intramolecular singlet fission

donor-acceptor-copolymers

display_name: donor-acceptor-copolymers

electronic-structure

display_name: electronic-structure

epfl

display_name: epfl

marie-curie-fellowship

display_name: marie-curie-fellowship


en: Extensive machine-learning assisted research has been dedicated to predicting band gaps for perovskites, driven by their immense potential in photovoltaics. Yet, the effectiveness is often hampered by the lack of high-quality band-gap datasets, particularly for perovskites involving d orbitals. In this work, we consistently calculate a large dataset of band gaps with a high level of accuracy, which is rigorously validated by experimental and state-of-the-art GW band gaps. Leveraging this achievement, our machine-learning derived descriptor exhibits exceptional universality and robustness, proving effectiveness not only for single and double, halide and oxide perovskites regardless of the underlying atomic structures, but also for hybrid organic-inorganic perovskites. With this approach, we comprehensively explore up to 15,659 materials, unveiling 14 unreported lead-free perovskites with suitable band gaps for photovoltaics. Notably, MASnBr₃, FA₂SnGeBr₆, MA₂AuAuBr₆, FA₂AuAuBr₆, FA₂InBiCl₆, FA₂InBiBr₆, and Ba₂InBiO₆ stand out with direct band gaps, small effective masses, low exciton binding energies, and high stabilities.

en: High-quality data enabling universality of band-gap descriptor and discovery of photovoltaic perovskites

band-gap-descriptor

display_name: band-gap-descriptor

high-quality-dataset

display_name: high-quality-dataset

hybrid-functional

display_name: hybrid-functional

photoboltaic-perovskites

display_name: photoboltaic-perovskites


en: Dimensionality provides a clear fingerprint on the dispersion of infrared-active, polar-optical phonons. For these phonons, the local dipoles parametrized by the Born effective charges drive the LO-TO splitting of bulk materials; this splitting actually breaks down in two-dimensional materials. Here, we develop the theory for one-dimensional (1D) systems -nanowires, nanotubes, and atomic and polymeric chains. Combining an analytical model with the implementation of density-functional perturbation theory in 1D boundary conditions, we show that the dielectric splitting in the dispersion relations collapses as x²log(x) at the zone center. The dielectric properties and the radius of the 1D materials are linked by the present work to these red shifts, opening infrared and Raman characterization avenues.

en: Infrared-active phonons in one-dimensional materials and their spectroscopic signatures

envi

en: Environment

ab-initio

display_name: ab-initio

density-functional-theory

display_name: density-functional-theory

marvel

display_name: marvel

one-dimensional-materials

display_name: one-dimensional-materials

phonons

display_name: phonons

raman

display_name: raman


en: The reliable ab-initio description of strongly correlated materials is a long-sought capability in condensed matter physics. The GW+EDMFT method is a promising scheme, which provides a self-consistent description of correlations and screening, and does not require user-provided parameters. In order to test the reliability of this approach we apply it to the experimentally well characterized perovskite compound Ca₂RuO₄, in which a temperature-dependent structural deformation drives a paramagnetic metal-insulator transition. Our results demonstrate that the nonlocal polarization and self-energy components introduced by GW are essential for setting the correct balance between interactions and bandwidths, and that the GW+EDMFT scheme produces remarkably accurate predictions of the electronic properties of this strongly correlated material.

materialscloud

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

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

en: Materials Cloud

en: Fully ab-initio electronic structure of Ca₂RuO₄

first-principles

display_name: first-principles

gw-method

display_name: gw-method

snsf

display_name: snsf

strongly-correlated-systems

display_name: strongly-correlated-systems


en: We perform extensive molecular dynamics simulations of an ensemble of realistic microgel particles in swollen conditions in a wide range of packing fractions ζ. We compare neutral and charged microgels, where we consider charge distribution adherent to experimental conditions. Through a detailed analysis of singleparticle behavior, we are able to identify the different regimes occurring upon increasing concentration: from shrinking to deformation and interpenetration, always connecting our findings with available experimental observations. We then link these single-particle features with the collective behavior of the suspension, finding evidence of a structural reentrance that has no counterpart in the dynamics. Hence, while the maximum of the radial distribution function displays a nonmonotonic behavior with increasing ζ, the dynamics, quantified by the microgels' mean-squared displacement, always slows down. This behavior, at odds with the simple Hertzian model, can be described by a phenomenological multi-Hertzian model, which takes into account the enhanced internal stiffness of the core. However, also this model fails when deformation enters into play, whereby more realistic many-body models are required. Thanks to our analysis, we are able to unveil the key physical mechanisms, shrinking and deformation, giving rise to the structural reentrance that holds up to very large packing fractions. We further identify key similarities and differences between neutral and charged microgels, for which we detect at high enough ζ the fusion of charged shells, previously invoked to explain key experimental findings, and responsible for the structural reentrance. Overall, our study establishes a powerful framework to uncover the physics of microgel suspensions, paving the way to tackle different regimes, e.g., high temperature, and internal architectures, such as for hollow and ultralow-cross-linked microgels, where experimental evidence is still limited.

en: Numerical study of neutral and charged microgel suspensions: from single-particle to collective behavior

collective-behavior

display_name: collective-behavior

elasticity

display_name: elasticity

microgels

display_name: microgels


en: Melt post-condensation, thermal, and thermo-oxidative degradation of a cyclo-aliphatic polyamide were studied through time-resolved rheometry (TRR). The implemented TRR elucidates structural changes occurring during two concurrent phenomena, namely melt post-condensation and thermal/thermo-oxidative degradation, during the time sweep in a parallel plate rheometer. TRR measurements were conducted on neat polyamide under nitrogen (inert/non-oxidative) and air (oxidative) environment at 3 % strain amplitude and a range of frequencies between 0.1 and 100 rad/sec for two hours. At temperatures of 260, 270, and 275 °C, a dual-stage time-dependent growth in viscoelastic properties was observed under an oxidative environment. Thermo-oxidative degradation of polyamide melt 270 °C was shown to occur from the exposed sample edge, continuing inwards, effectively reducing the radius of the unoxidized polymer melt by 6.4 %. A modeling approach using MATLAB is presented to interpret and differentiate the rheological behavior of edge oxidized gel and bulk polymer within the volume of the sample melt. The code removes the effects of the strain limiter and the edge gelation from experimental measurements in predicting the material properties.

en: Predicting the influence of edge oxidation in parallel-plate rheometry

edge-oxidation

display_name: edge-oxidation

experimental

display_name: experimental

matlab

display_name: matlab


en: We present the NaviCatGA package, a versatile genetic algorithm capable of optimizing molecular catalyst structures using well-suited fitness functions to achieve a set of targeted properties. The flexibility and generality of this tool are demonstrated with two examples: i) Ligand optimization and exploration for Ni-catalyzed aryl-ether cleavage manipulating SMILES and using a fitness function derived from molecular volcano plots, ii) multiobjective (i.e., activity/selectivity) optimization of bipyridine N.N'-dioxide Lewis basic organocatalysts for the asymmetric propargylation of benzaldehyde from 3D molecular fragments. We show that evolutionary optimization, enabled by NaviCatGA, is an efficient way of accelerating catalyst discovery that bypasses combinatorial scaling issues and incorporates compelling chemical constraints.

en: Genetic optimization of homogeneous catalysts

catalysis

display_name: catalysis

homogeneous-catalysis

display_name: homogeneous-catalysis

optimization

display_name: optimization

organocatalysis

display_name: organocatalysis

volcano-plot

display_name: volcano-plot


en: Prismatic slip of the screw  dislocation in magnesium at temperatures ≳ 150 K is understood to be governed by double-cross-slip of the stable basal screw through the unstable prism screw and back to the basal screw, with the activation energy controlled by the formation energy of two basal-basal kinks. However, atomistic studies of the double-kink process predict activation energies roughly twice those derived experimentally. Here, a new mechanism of prism glide is proposed, analyzed theoretically, and demonstrated qualitatively and quantitatively via direct molecular dynamics (MD) simulations. The new mechanism is intrinsically 3d, and involves the nucleation of a single kink at the junction where a 3d prismatic dislocation loop transitions from the basal screw segment to non-screw prismatic character. The relevant kink energies are calculated using recently-developed Neural-Network Potentials (NNPs) for Mg that show good agreement versus DFT for basal and prism  dislocations, enabling a parameter-free analytic model for the activation barrier. Direct MD simulations show both operation of the precise proposed mechanism and a stress-dependent activation barrier that agrees reasonably with the analytic model. Predictions of dislocation velocity compare very well with in-situ TEM data, and macroscopic strength versus temperature can also be understood. Overall, the new intrinsically 3d mechanism for dislocation glide due to single kink nucleation rather than double-kink nucleation explains key features of the prismatic slip in Mg and may have broader applicability in other metals where kink nucleation processes control thermally-activated flow.

en: Mechanism of thermally-activated prismatic slip in Mg

magnesium

display_name: magnesium

marvel

display_name: marvel

neural-network-potential

display_name: neural-network-potential

prismatic-slip

display_name: prismatic-slip


en: Weyl nodes can be classified into zero-dimensional (0D) Weyl points, 1D Weyl nodal lines, and 2D Weyl nodal surfaces (WNS), which possess finite Chern numbers. Up to date, the largest Chern number of WPs identified in Weyl semimetals is 4, which is thought to be a maximal value for linearly crossing points in solids. On the other hand, whether the Chern numbers of nonzero-dimensional linear crossing Weyl nodal objects have one upper limit is still an open question. In this work, combining angle-resolved photoemission spectroscopy with density-functional theory calculations, we show that the chiral crystal AlPt hosts a cube-shaped charged WNS which is formed by the linear crossings of two singly degenerate bands. Different from conventional Weyl nodes, the cube-shaped nodal surface in AlPt is enforced by nonsymmorphic chiral symmetries and time-reversal symmetry rather than accidental band crossings, and it possesses a giant Chern number |C|=26. Moreover, our results and analysis prove that there is no upper limit for the Chern numbers of such kind of 2D Weyl nodal object. This record includes the data in the related paper Phys. Rev. B 105, 115118 – Published 14 March 2022.

en: Giant Chern number of a Weyl nodal surface without upper limit

giant-chern-number

display_name: giant-chern-number

unpaired-weyl-point

display_name: unpaired-weyl-point

weyl-nodal-surface

display_name: weyl-nodal-surface

weyl-nodal-wall

display_name: weyl-nodal-wall


en:
Quantum spin Hall insulators (QSHIs) are a class of topological materials that has been extensively studied during the past decade. One of their distinctive features is the presence of a finite band gap in the bulk and gapless, topologically protected edge states that are spin-momentum locked. These materials are characterized by a Z₂ topological order where, in the 2D case, a single topological invariant can be even or odd for a trivial or a topological material, respectively. Thanks to their interesting properties, such as the realization of dissipationless spin currents, spin pumping and spin filtering, they are of great interest in the field of electronics, spintronics and quantum computing. In this work we perform an high-throughput screening of QSHI starting from a set of 783 2D exfoliable materials (603 after removing materials with lanthanides), recently identified from a systematic screening of the ICSD, COD, and MPDS databases (MC2D). In this screening we identify 4 Z₂ topological insulators, 3 of which already known in the literature, and 7 direct gap metals with the potential of becoming QSHIs under a reasonable external perturbation.

All the data are also displayed in the dedicated DISCOVER section "2D topological insulators" https://www.materialscloud.org/discover/2dtopo/dashboard/ptable

en: A complementary screening for quantum spin Hall insulators in 2D exfoliable materials

dft

display_name: dft

quantum-espresso

display_name: quantum-espresso

quantum-spin-hall

display_name: quantum-spin-hall

topological-insulator

display_name: topological-insulator


en: In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. We show that when coupled with quantum machine learning (QML) based surrogate methods the computational burden can be alleviated such that QMC shows clear potential to undergird the formation of high quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: The fixed node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons set based QML (AQML) models. Numerical evidence presented includes converged DMC results for over one thousand small organic molecules with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules with 9 heavy atoms to validate the AQML predictions. Numerical evidence collected for 𝛥-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies  with near chemical accuracy throughout chemical space. In this archive, we present DMC energies for over one thousand small organic molecules with up to 5 heavy atoms, and 50 medium sized organic molecules with 9 heavy atoms, as well as energies computed at cheaper levels of theory such as HF, DFT, MP2 and CCSD(T).

en: Fixed node diffusion Monte Carlo energies for over one thousand small organic molecules

envi

en: Environment

chemical-space

display_name: chemical-space

quantum-machine-learning

display_name: quantum-machine-learning

quantum-monte-carlo

display_name: quantum-monte-carlo


en: Thin film organic photovoltaics (OPVs) aspire to extract solar energy in a green, high efficiency, and cost-effective pathway, offering a sustainable solution to energy and ecosystem. We take the effort of developing low ecological harmful solvent processing of OPV devices and mini-modules, to strengthen the environmental benefits of OPV. New formula utilizing safer solvents enhanced with additives affords an optimal laboratory device efficiency of 20% as well as superior operational and thermal stability. Mini-module shows an efficiency up to 17.6% in upscaled processing, representing the highest performance for green solvent OPV development. Suitable nanoscaled phase separation is obtained in together with a micron-scale surface wrinkle pattern from the new developed processing strategy. Thus efficient photon-to-electron conversion at nano-interfaces and light extraction in broader receiving angles is achieved, which allows for more flexible installation geometries in building-integrated applications.

materialscloud

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

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

en: Materials Cloud

en: Organic solar cells with eco-friendly preparation to achieve an efficiency of 20%

long-term-lifetime

display_name: long-term-lifetime

organic-solar-cells

display_name: organic-solar-cells

sustainability

display_name: sustainability


en: Prismatic slip in magnesium at temperatures T ≲ 150 K occurs at ∼ 100 MPa independent of temperature, and jerky flow due to large prismatic dislocation glide distances is observed; this athermal regime is not understood. In contrast, the behavior at T ≳ 150 K is understood to be governed by a thermally-activated double-cross-slip of the stable basal screw dislocation through an unstable or weakly metastable prism screw configuration and back to the basal screw. Here, a range of neural network potentials (NNPs) that are very similar for many properties of Mg including the basal-prism-basal cross-slip path and pro- cess, are shown to have an instability in prism slip at a potential-dependent critical stress. One NNP, NNP-77, has a critical instability stress in good agreement with experiments and also has basal-prism-basal transition path energies in very good agreement with DFT results, making it an excellent potential for understanding Mg prism slip. Full 3d simulations of the expansion of a prismatic loop using NNP-77 then also show a transition from cross-slip onto the basal plane at low stresses to prismatic loop expansion with no cross- slip at higher stresses, consistent with in-situ TEM observations. These results reveal (i) the origin and prediction of the observed unstable low-T prismatic slip in Mg and (ii) the critical use of machine-learning potentials to guide discovery and understanding of new important metallurgical behavior.

en: A low-temperature prismatic slip instability in Mg understood using machine learning potentials

magnesium

display_name: magnesium

marvel

display_name: marvel

minimum-energy-path

display_name: minimum-energy-path


en: A key strategy to design environmental barrier coatings focuses on doping multiple rare-earth principal components into β-type rare-earth disilicates (RE2Si2O7) to achieve versatile property optimization. However, controlling the phase formation capability of (nRExi)2Si2O7 remains a crucial challenge, due to the complex polymorphic phase competitions and evolutions led by different RE3+ combination. Herein, by fabricating twenty-one model (REI0.25REII0.25REIII0.25REIV0.25)2Si2O7 compounds, we find that their formation capability can be evaluated by the ability to accommodate configurational randomness of multiple RE3+ cations in β-type lattice while preventing the β-to-γ polymorphic transformation. The phase formation and stabilization are controlled by the average RE3+ radius and the deviations of different RE3+ combinations. Subsequently, based on high-throughput density-functional-theory calculations, we propose that the configurational entropy of mixing is a reliable descriptor to predict the phase formation of β-type (nRExi)2Si2O7. The results may accelerate the design of (nRExi)2Si2O7 materials with tailored compositions and controlled polymorphic phases.

en: Phase formation capability and compositional design of β-phase multiple rare-earth principal component disilicates

density-functional-theory

display_name: density-functional-theory

environmental-barrier-coating

display_name: environmental-barrier-coating

experimental

display_name: experimental

high-entropy-ceramics

display_name: high-entropy-ceramics

phase-formation

display_name: phase-formation

rare-earth-disilicate

display_name: rare-earth-disilicate


en:
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (e.g. atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the applicability of the prediction rigidities to an even broader class of atomistic modeling problems.
This record contains all the data used for the analyses conducted in the associated work published in Faraday Discussions.

en: Prediction rigidities for data-driven chemistry

machine-learning

display_name: machine-learning

ml-model-robustness

display_name: ml-model-robustness

prediction-rigidity

display_name: prediction-rigidity

uncertainty-quantification

display_name: uncertainty-quantification


en: Augmented plane wave methods enable an efficient description of atom-centered or localized features of the electronic density, circumventing high energy cutoffs and thus prohibitive computational costs of pure plane wave formulations. To complement existing implementations for ground-state properties and excitation energies, we present the extension of the Gaussian and augmented plane wave method to excited-state nuclear gradients within the Tamm–Dancoff approximation of time-dependent density functional theory and its implementation in the CP2K program package. Benchmarks for a test set of 35 small molecules demonstrate that maximum errors in the nuclear forces for excited states of singlet and triplet spin multiplicity are smaller than 0.1 eV/Å. The method is furthermore applied to the calculation of the zero-phonon line of defective hexagonal boron nitride. This spectral feature is reproduced with an error of 0.6 eV in comparison to GW–Bethe–Salpeter reference computations and 0.4 eV in comparison to experimental measurements. Accuracy assessments and applications thus demonstrate the potential use of the outlined developments for large-scale applications on excited-state properties of extended systems. The data in this record are supplementary data to the published article with DOI 10.1021/acs.jctc.4c00614.

en: Excited-state forces with the Gaussian and augmented plane wave method for the Tamm–Dancoff approximation of time-dependent density functional theory

augmented-plane-wave-methods

display_name: augmented-plane-wave-methods

gaussian-basis-sets

display_name: gaussian-basis-sets


en: Complex oxide functionality, such as ferroelectricity, magnetism or superconductivity is often achieved in epitaxial thin-film geometries. Oxygen vacancies tend to be the dominant type of defect in these materials but a fundamental understanding of their stability and electronic structure has so far mostly been established in the bulk or strained bulk, neglecting interfaces and surfaces present in a thin-film geometry. We investigate here, via density functional theory calculations, oxygen vacancies in the model system of a SrMnO3 (SMO) thin film grown on a SrTiO3 (STO) (001) substrate. Structural and electronic differences compared to bulk SMO result mainly from undercoordination at the film surface. The changed crystal field leads to a depletion of subsurface valence-band states and transfer of this charge to surface Mn atoms, both of which strongly affect the defect chemistry in the film. The result is a strong preference of oxygen vacancies in the surface region compared to deeper layers. Finally, for metastable oxygen vacancies in the substrate, we predict a spatial separation of the defect from its excess charge, the latter being accommodated in the film but close to the substrate boundary. These results show that surface and interface effects lead to significant differences in stability and electronic structure of oxygen vacancies in thin-film geometries compared to the (strained) bulk.

en: Surface and interface effects in oxygen deficient SrMnO3 thin films grown on SrTiO3

crystal-field

display_name: crystal-field

cscs

display_name: cscs

interface

display_name: interface

oxygen-vacancy

display_name: oxygen-vacancy

snsf

display_name: snsf

surface

display_name: surface

thin-film

display_name: thin-film


en: In the upcoming process to overcome the limitations of the standard von Neumann architecture, synaptic electronics is gaining a primary role for the development of in-memory computing. In this field, Ge-based compounds have been proposed as switching materials for nonvolatile memory devices and for selectors. By employing the classical molecular dynamics, we study the structural features of both the liquid states at 1500 K and the amorphous phase at 300 K of Ge-rich and Se-rich chalcogenides binary Ge_x Se_1–x systems in the range 0.4 ≤ x ≤ 0.6. The simulations rely on a model of interatomic potentials where ions interact through steric repulsion, as well as Coulomb and charge–dipole interactions given by the large electronic polarizability of Se ions. Our results indicate the formation of temperature-dependent hierarchical structures with short-range local orders and medium-range structures, which vary with the Ge content. Our work demonstrates that nanosecond-long simulations, not accessible via ab initio techniques, are required to obtain a realistic amorphous phase from the melt. Our classical molecular dynamics simulations are able to describe the profound structural differences between the melt and the glassy structures of GeSe chalcogenides. These results open to the understanding of the interplay between chemical composition, atomic structure, and electrical properties in switching materials.

en: Hierarchical short- and medium-range order structures in amorphous Ge_x Se_1–x for selectors applications

amorphous

display_name: amorphous

chalcogenides

display_name: chalcogenides

intersect

display_name: intersect

molecular-dynamics

display_name: molecular-dynamics


en: Organic semiconductors with high-spin ground states are fascinating because they could enable fundamental understanding on the spin-related phenomenon in light element and provide opportunities for organic magnetic and quantum materials. Although high-spin ground states have been observed in some quinoidal type small molecules or doped organic semiconductors, semiconducting polymers with high-spin at their neutral ground state are rarely reported. Here we report three high-mobility semiconducting polymers with different spin ground states. We show that polymer building blocks with small singlet-triplet energy gap (ΔES-T) could enable small ΔES-T gap and increase the diradical character in copolymers. We demonstrate that the electronic structure, spin density, and solid-state interchain interactions in the high-spin polymers are crucial for their ground states. Polymers with a triplet ground state (S = 1) could exhibit doublet (S = 1/2) behavior due to different spin distributions and solid-state interchain spin-spin interactions. Besides, these polymers showed outstanding charge transport properties with high hole/electron mobilities and can be both n- and p-doped with superior conductivities. Our results demonstrate a rational approach to high-mobility semiconducting polymers with different spin ground states.

en: High-mobility semiconducting polymers with different spin ground states

envi

en: Environment

high-mobility-polymers

display_name: high-mobility-polymers

high-spin-materials

display_name: high-spin-materials

organic-semiconductor

display_name: organic-semiconductor


en:
Despite the high promise of Li-metal-based batteries, its commercialization has been hampered due to the formation of dendrites that lead to mechanical instability,
energy loss and eventual internal short circuits. The provided dataset consists of the phase-field simulation results for investigating the effect of applied overpotential and operating temperature on dendrite growth. These data are used for elucidating the correlation of overpotential and temperature with the surface modulation during electrodeposition. The simulation cell consists of a Li metal anode and 1M LiPF6 in EC:DMC (1:1), and the electrodeposition process was simulated under the applied overpotential ranging from 0.30 V to 0.44 V with a 0.02 V increment at temperatures from 268 K and 333 K with a 5 K increment. The data contains order parameter, chemical potential and overpotential. The supplied Python script can compute the surface tortuosity, average and maximum Li heights and dendrite height at a given temperature, overpotential and time step.

materialscloud

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

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

en: Materials Cloud

en: Phase-field investigation of lithium electrodeposition under different applied overpotentials and operating temperatures

anode

display_name: anode

big-map

display_name: big-map

dendrites

display_name: dendrites

interfaces

display_name: interfaces

lithium

display_name: lithium

phase-field

display_name: phase-field


en:
Dynamical potentials appear in many advanced electronic-structure methods, including self-energies from many-body perturbation theory, dynamical mean-field theory, electronic-transport formulations, and many embedding approaches. Here, we propose a novel treatment for the frequency dependence, introducing an algorithmic inversion method that can be applied to dynamical potentials expanded as sum-over-poles. This approach allows for an exact solution of Dyson-like equations at all frequencies via a mapping to a matrix diagonalization, and provides simultaneously frequency-dependent (spectral) and frequency-integrated (thermodynamic) properties of the Dyson-inverted propagators.
The transformation to a sum-over-poles is performed introducing n-th order generalized Lorentzians as an improved basis set to represent the spectral function of a propagator.
Numerical results for the homogeneous electron gas at the G0W0 level are provided to argue for the accuracy and efficiency of such unified approach.
In this record, we provide the input and outputs of the AGWX suite (see article) used in the work. Also, we add the plots of the obtained Green's function for all densities, k-points, and frequencies.

en: A unified Green's function approach for spectral and thermodynamic properties from algorithmic inversion of dynamical potentials

computational-materials-science

display_name: computational-materials-science

homogeneous-electron-gas

display_name: homogeneous-electron-gas

many-body-perturbation-theory-gw

display_name: many-body-perturbation-theory-gw

marvel

display_name: marvel

max

display_name: max


en: The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the energy requirement is reduced by as much as a factor of two using the correct heat capacity.

en: A data-science approach to predict the heat capacity of nanoporous materials

heat-capacity

display_name: heat-capacity

machine-learning

display_name: machine-learning

mof

display_name: mof

snsf

display_name: snsf

thermal-properties

display_name: thermal-properties

zeolite

display_name: zeolite