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

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

conjugated-polymers

display_name: conjugated-polymers

excited-states

display_name: excited-states

singlet-fission

display_name: singlet-fission


$oid: OBSOLETE

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

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

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

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

dichalcogenide

display_name: dichalcogenide

monte-carlo-simulation

display_name: monte-carlo-simulation

segregation

display_name: segregation

statistical-inference

display_name: statistical-inference


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

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

amorphous-materials

display_name: amorphous-materials

charge-transport

display_name: charge-transport

epfl

display_name: epfl

erc

display_name: erc

reorganization-energy

display_name: reorganization-energy

replica-exchange-molecular-dynamics

display_name: replica-exchange-molecular-dynamics

triphenylamine

display_name: triphenylamine


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

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

ab-initio-simulations

display_name: ab-initio-simulations

asymmetric-heterogeneous-catalysis

display_name: asymmetric-heterogeneous-catalysis

azidealkyne-huisgen-cycloaddition

display_name: azidealkyne-huisgen-cycloaddition

experimental

display_name: experimental

onsurface-synthesis

display_name: onsurface-synthesis

snsf

display_name: snsf


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

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

cscs

display_name: cscs

dftu

display_name: dftu

dftuv

display_name: dftuv

extended-hubbard-functionals

display_name: extended-hubbard-functionals

first-principles-calculations

display_name: first-principles-calculations

formation-energy

display_name: formation-energy

hybrid-functionals

display_name: hybrid-functionals

intercalation-voltages

display_name: intercalation-voltages

li-ion-batteries

display_name: li-ion-batteries

marvelosp

display_name: marvelosp

phospho-olivine-cathode-materials

display_name: phospho-olivine-cathode-materials


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

en: Automated all-functionals infrared and Raman spectra

ab-initio

display_name: ab-initio

aiida

display_name: aiida

automated

display_name: automated

density-functional-theory

display_name: density-functional-theory

infrared

display_name: infrared

phonons

display_name: phonons

raman

display_name: raman

vibrational-properties

display_name: vibrational-properties


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

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

atomic-coordinates

display_name: atomic-coordinates

epfl

display_name: epfl

snsf

display_name: snsf


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

en: An overview of the spin dynamics of antiferromagnetic Mn₅Si₃

antiferromagnetism

display_name: antiferromagnetism

frustration

display_name: frustration

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

intersect

display_name: intersect

marvel

display_name: marvel

noncollinear-magnetism

display_name: noncollinear-magnetism

spin-waves

display_name: spin-waves


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

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

dftu

display_name: dftu

heavy-fermions

display_name: heavy-fermions

kondo-effect

display_name: kondo-effect


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

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

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).

dft

display_name: dft

nuclear-magnetic-resonance

display_name: nuclear-magnetic-resonance

nuclear-quadrupole-resonance

display_name: nuclear-quadrupole-resonance


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

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

materialscloud

en: Materials Cloud

envi

en: Environment

density-functional-theory

display_name: density-functional-theory

equivariant-neural-networks

display_name: equivariant-neural-networks

gaussian-processes

display_name: gaussian-processes

machine-learned-force-fields

display_name: machine-learned-force-fields

transition-metals

display_name: transition-metals


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

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

catalytic-enhancement

display_name: catalytic-enhancement

ceria-microspheres

display_name: ceria-microspheres

co-oxidation

display_name: co-oxidation

gallium-doping

display_name: gallium-doping

hydrothermal-synthesis

display_name: hydrothermal-synthesis

oxygen-vacancy

display_name: oxygen-vacancy


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

en: Automated computational workflows for muon spin spectroscopy

aiida

display_name: aiida

aiida-impuritysupercellconv

display_name: aiida-impuritysupercellconv

aiida-muon

display_name: aiida-muon

automated-workflows

display_name: automated-workflows

dftmu

display_name: dftmu

first-principles

display_name: first-principles

marvel

display_name: marvel

muon-spin-spectroscopy

display_name: muon-spin-spectroscopy


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

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

gaas

display_name: gaas

gallium-arsenide

display_name: gallium-arsenide

machine-learning-potential

display_name: machine-learning-potential

molecular-dynamics-simulations

display_name: molecular-dynamics-simulations

nanowire

display_name: nanowire

neural-network-potential

display_name: neural-network-potential

ordering-at-the-interface

display_name: ordering-at-the-interface

snsf

display_name: snsf


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

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

ab-initio

display_name: ab-initio

alloy-theory

display_name: alloy-theory

cluster-expansion

display_name: cluster-expansion

phase-stability

display_name: phase-stability

precipitation

display_name: precipitation


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

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

cscs

display_name: cscs

density-functional-theory

display_name: density-functional-theory

enantioselectivity

display_name: enantioselectivity

marvelasm

display_name: marvelasm

surface-chemistry

display_name: surface-chemistry


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

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

computational-materials

display_name: computational-materials

field-effect-transistors

display_name: field-effect-transistors

quantum-transport

display_name: quantum-transport


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

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

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).

correlated-metal

display_name: correlated-metal

dynamical-mean-field-theory

display_name: dynamical-mean-field-theory

first-principles-calculations

display_name: first-principles-calculations

metal-insulator-transition

display_name: metal-insulator-transition

multilayer-thin-films

display_name: multilayer-thin-films

superlattices

display_name: superlattices


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

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

materialscloud

en: Materials Cloud

calcium-batteries

display_name: calcium-batteries

marokite

display_name: marokite

perovskite

display_name: perovskite

post-spinel

display_name: post-spinel


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

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

envi

en: Environment

computational-screening

display_name: computational-screening

marvel

display_name: marvel

photoelectrochemical-water-splitting

display_name: photoelectrochemical-water-splitting

surface-passivation

display_name: surface-passivation


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

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

carbon

display_name: carbon

dataset

display_name: dataset

interatomic-potential

display_name: interatomic-potential

machine-learning

display_name: machine-learning

neural-network-potential

display_name: neural-network-potential


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

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

correction-number-of-electrons

display_name: correction-number-of-electrons

epfl

display_name: epfl

erc

display_name: erc

quantum-chemical-metrics

display_name: quantum-chemical-metrics

snsf

display_name: snsf


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

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

ab-initio-molecular-dynamics

display_name: ab-initio-molecular-dynamics

electrocatalysis

display_name: electrocatalysis

water-metal-interfaces

display_name: water-metal-interfaces


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

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

high-entropy-alloys

display_name: high-entropy-alloys

high-temperature-strength

display_name: high-temperature-strength

solute-strengthening

display_name: solute-strengthening


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

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

bethe-salpeter-equation

display_name: bethe-salpeter-equation

gw

display_name: gw

hybrid-functionals

display_name: hybrid-functionals

many-body-techniques

display_name: many-body-techniques

optical-absorption-spectroscopy

display_name: optical-absorption-spectroscopy


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

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

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).

chemical-potential-shift

display_name: chemical-potential-shift

cscs

display_name: cscs

lifshitz-transition

display_name: lifshitz-transition

marvel

display_name: marvel

thermoelectric-power

display_name: thermoelectric-power

two-channel-model

display_name: two-channel-model

weyl-semimetal

display_name: weyl-semimetal


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

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

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

materialscloud

en: Materials Cloud

first-principles

display_name: first-principles

hybrid-functional-dft

display_name: hybrid-functional-dft

molecular-crystals

display_name: molecular-crystals

snsf

display_name: snsf


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

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

envi

en: Environment

absorption-spectrum

display_name: absorption-spectrum

bismuth

display_name: bismuth

density-functional-theory

display_name: density-functional-theory

density-of-states

display_name: density-of-states

extreme-ultraviolet

display_name: extreme-ultraviolet

investissements-davenir

display_name: investissements-davenir

marvelosp

display_name: marvelosp

photoexcitation

display_name: photoexcitation

real-space-atomic-motion

display_name: real-space-atomic-motion

transient-reflectivity

display_name: transient-reflectivity


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

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

cscs

display_name: cscs

dftdmft

display_name: dftdmft

electronic-structure

display_name: electronic-structure

oxygen-vacancies

display_name: oxygen-vacancies


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

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

dftuv

display_name: dftuv


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

en: Revealing hidden magneto-electric multipoles using Compton scattering

compton-scattering

display_name: compton-scattering

erc

display_name: erc

horizon-europe

display_name: horizon-europe

magneto-electric-multipoles

display_name: magneto-electric-multipoles


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

en: Confinement effects and acid strength in Zeolites

acid-base

display_name: acid-base

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics

zeolites

display_name: zeolites


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

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

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning


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

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

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).

aluminum

display_name: aluminum

chemical-composition

display_name: chemical-composition

experimental

display_name: experimental

materials-discovery

display_name: materials-discovery

mechanical-properties

display_name: mechanical-properties

metals-and-alloys

display_name: metals-and-alloys


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

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

materialscloud

en: Materials Cloud

flowmol

display_name: flowmol

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

python-pickle

display_name: python-pickle

software-input-files

display_name: software-input-files


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

en: Structure-property maps with kernel principal covariates regression

dimensionality-reduction

display_name: dimensionality-reduction

epfl

display_name: epfl

erc

display_name: erc

kernel-methods

display_name: kernel-methods

materials-science

display_name: materials-science

max

display_name: max

snsf

display_name: snsf


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

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

envi

en: Environment

cerium-oxide-surfaces

display_name: cerium-oxide-surfaces

excess-charge-localization

display_name: excess-charge-localization

facet-dependent-stability

display_name: facet-dependent-stability

lattice-relaxations

display_name: lattice-relaxations

oxygen-vacancies

display_name: oxygen-vacancies


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

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

cineca

display_name: cineca

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

linear-response

display_name: linear-response

liouville-lanczos-approach

display_name: liouville-lanczos-approach

magnons

display_name: magnons

marvel

display_name: marvel

quantum-espresso

display_name: quantum-espresso

spin-orbit-coupling

display_name: spin-orbit-coupling

spin-wave-spectra

display_name: spin-wave-spectra


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

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

density-functional-theory

display_name: density-functional-theory

foster-boys-centers

display_name: foster-boys-centers

machine-learning

display_name: machine-learning


superconductivity-in-antiperovskites

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

en: Superconductivity in antiperovskites

title_for_slug: superconductivity-in-antiperovskites

anti-perovskites

display_name: anti-perovskites

electron-phonon-coupling

display_name: electron-phonon-coupling

superconductivity

display_name: superconductivity


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

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

experimental

display_name: experimental

large-area-device

display_name: large-area-device

nonfused-ring-electron-acceptor

display_name: nonfused-ring-electron-acceptor

organic-solar-cells

display_name: organic-solar-cells

record-efficiency

display_name: record-efficiency


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

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

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

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

message-passing

display_name: message-passing

pasc

display_name: pasc

reproducibility

display_name: reproducibility


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

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

materialscloud

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

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

aiidalab

display_name: aiidalab

dft

display_name: dft

graphene-nanoribbons

display_name: graphene-nanoribbons

marvel

display_name: marvel

on-surface-synthesis

display_name: on-surface-synthesis

scanning-probe-microscopy

display_name: scanning-probe-microscopy

snsf

display_name: snsf


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

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

materialscloud

en: Materials Cloud

aqua-complex

display_name: aqua-complex

graph-neural-network

display_name: graph-neural-network

spectroscopy

display_name: spectroscopy


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

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

envi

en: Environment

experimental

display_name: experimental

graphene-nanoplatelets

display_name: graphene-nanoplatelets

machine-learning

display_name: machine-learning

raman

display_name: raman


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

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

compton-scattering

display_name: compton-scattering

density-functional-theory

display_name: density-functional-theory

erc

display_name: erc

horizon-europe

display_name: horizon-europe

magneto-electric-toroidal-moment

display_name: magneto-electric-toroidal-moment


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

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

dft

display_name: dft

electronic-properties

display_name: electronic-properties

stm

display_name: stm


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

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

diffuse-double-layer

display_name: diffuse-double-layer

electrochemical-system

display_name: electrochemical-system

finite-element-method

display_name: finite-element-method

semi-analytical-solution

display_name: semi-analytical-solution


en:
The potential profile and the energy level offset of core/shell heterostructured nanocrystals (h-NCs) determine the photophysical properties and the charge transport characteristics of h-NC solids. However, limited material choices for heavy metal-free III-V/II-VI h-NCs pose challenges in comprehensive control of the potential profile. Herein, we present an approach to such control by steering dipole densities at the interface of III-V/II-VI h-NCs. The controllable heterovalency at the interface is responsible for interfacial dipole densities that result in the vacuum-level shift, providing an additional knob for the control of optical and electrical characteristics of h-NCs. The synthesis of h-NCs with atomic precision allows us to correlate interfacial dipole moments with the nanocrystals' photochemical stability and optoelectronic performance.

The description was referred to the abstract of the original paper. All numerical data of the main figures in the articles are included in this data repository.
In particular, each tab of the data file indicates each panel in the main figures.
Please refer to the main research article.

en: Interface polarization in heterovalent core/shell nanocrystals

heterostructured-nanocrystal

display_name: heterostructured-nanocrystal

interface-polarization

display_name: interface-polarization


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

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

deacon-process

display_name: deacon-process

oxygen-induced-de-chlorination-process

display_name: oxygen-induced-de-chlorination-process

peroxo-surface-species

display_name: peroxo-surface-species

reduced-ceria

display_name: reduced-ceria


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

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

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).

band-gap

display_name: band-gap

bond-angles

display_name: bond-angles

bond-length

display_name: bond-length

crystal-structure

display_name: crystal-structure

density-functional-theory

display_name: density-functional-theory

density-of-states

display_name: density-of-states

dftu

display_name: dftu

dftuv

display_name: dftuv

hubbard-parameters

display_name: hubbard-parameters

hubbard-projectors

display_name: hubbard-projectors

interstitial-doping

display_name: interstitial-doping

magnetic-moment

display_name: magnetic-moment

orthogonalized-atomic-orbitals

display_name: orthogonalized-atomic-orbitals

oxidation-state

display_name: oxidation-state

self-interactions

display_name: self-interactions

spin-configuration

display_name: spin-configuration

substitutional-doping

display_name: substitutional-doping


en:
The transport and optical properties of semiconducting transition metal dichalcogenides around room temperature are dictated by electron-phonon scattering mechanisms within a complex, spin-textured and multi-valley electronic landscape. The relative positions of the valleys are critical, yet they are sensitive to external parameters and very difficult to determine directly. We propose a first-principles model as a function of valley positions to calculate carrier mobility and Kerr rotation angles, and apply it to MoS₂, WS₂, MoSe₂, and WSe₂. The model brings valuable insights, as well as quantitative predictions of macroscopic properties for a wide range of carrier density. The doping-dependant mobility displays a characteristic peak, the height depending on the position of the valleys. In parallel, the Kerr rotation signal is enhanced when same spin-valleys are aligned, and quenched when opposite spin-valleys are populated. We provide guidelines to optimize and correlate these quantities with respect to experimental parameters, as well as the theoretical support for in situ characterization of the valley positions.
The dataset contains the necessary data, a jupyter notebook, a supporting python library, and a small app with a simple user interface to reproduce the results.

en: The impact of valley profile on the mobility and Kerr rotation of transition metal dichalcogenides

materialscloud

en: Materials Cloud

electron-phonon-coupling

display_name: electron-phonon-coupling

kerr-angle

display_name: kerr-angle

transport

display_name: transport


en:
The characterization of the atomic structure of disordered systems, such as amorphous, glasses and (bio)molecule in solution, is a fundamental step for most theoretical investigations. The properties of short- and medium-range local order structures are responsible for the electronic, optical and transport properties of these systems. Here, we present the BELLO open source code, a post-processing script-tool created for the automatic analysis and extraction of structural characteristics of disordered and amorphous systems. BELLO is agnostic to the code that generated single configurations or trajectories, it provides an intuitive access through a graphical user interface (GUI), and it requires minimal computational resources. Its capabilities include the calculation of the order parameter , the folded structure identification, and statistical analysis tools such as atomic coordination number and pair/angle-distribution functions. The working principles of the code are described and tested on ab initio molecular dynamics trajectories of amorphous chalcogenides.

The code is also on the GitHub repository
https://github.com/behnood-dianat/BELLO

en: BELLO: A post-processing tool for the local-order analysis of disordered systems

envi

en: Environment

amorphous

display_name: amorphous

disorder

display_name: disorder

intersect

display_name: intersect

molecular-dynamics

display_name: molecular-dynamics

order

display_name: order

software

display_name: software


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

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

cationic-intermixing

display_name: cationic-intermixing

experimental

display_name: experimental

magnetic-topological-insulator

display_name: magnetic-topological-insulator


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

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

ab-initio

display_name: ab-initio

carbon-nanotubes

display_name: carbon-nanotubes

cscs

display_name: cscs

snsf

display_name: snsf

terahertz-detector

display_name: terahertz-detector


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

en: Jahn-Teller effect in the cubic fullerides A₃C₆₀

cubic-fullerides

display_name: cubic-fullerides

elastic-coupling

display_name: elastic-coupling

jahn-teller-effect

display_name: jahn-teller-effect

phonons

display_name: phonons


en: In this record we provide data to support our recent findings related to the fabrication of [7]triangulene. Triangulene and its π-extended homologues constitute non-Kekulé polyradical frameworks with high-spin ground states, and are anticipated to be key components of organic spintronic devices. In our publication we report a combined in-solution and on-surface synthesis of the hitherto largest triangulene homologue, [7]triangulene (C78H24), consisting of twenty-eight benzenoid rings fused in a triangular fashion. We employ low-temperature scanning tunneling microscopy to confirm the chemical structure of individual molecules adsorbed on a Cu(111) surface. While neutral [7]triangulene in the gas phase is predicted to have an open-shell septet ground state; our scanning tunneling spectroscopy measurements, in combination with density functional theory calculations, reveal chemisorption of [7]triangulene on Cu(111) together with considerable charge transfer, resulting in a closed-shell state. Furthermore, substantial hybridization between the molecular orbitals of [7]triangulene is observed.

en: Synthesis and characterization of [7]triangulene

dft

display_name: dft

erc

display_name: erc

graphene-flagship

display_name: graphene-flagship

nanographene

display_name: nanographene

triangulene

display_name: triangulene


en: In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for an increased accuracy in the calculations. Here, we present the updated alexandria dataset of calculations for more than 415k solid-state materials obtained with two improved functionals: PBE for solids (that yields consistently better geometries than the PBE) and SCAN (probably the best all-around functional at the moment). Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for more reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBE for solids, and SCAN.

en: A new dataset of 415k stable and metastable materials calculated with the PBEsol and SCAN functionals

density-functional-theory

display_name: density-functional-theory

high-throughput

display_name: high-throughput

pbe-for-solids

display_name: pbe-for-solids

scan

display_name: scan