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

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

erc

display_name: erc

zeolites

display_name: zeolites


$oid: OBSOLETE

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

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

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

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

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


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

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

color

display_name: color

epfl

display_name: epfl

lsmo

display_name: lsmo

marvel

display_name: marvel

ml

display_name: ml


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

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

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

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

high-throughput-calculation

display_name: high-throughput-calculation

low-friction-materials

display_name: low-friction-materials

moire-patterns

display_name: moire-patterns

tribology

display_name: tribology

two-dimensional-materials

display_name: two-dimensional-materials


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

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

catalytic-enhancement

display_name: catalytic-enhancement

ceria-microspheres

display_name: ceria-microspheres

co-oxidation

display_name: co-oxidation

gallium-doping

display_name: gallium-doping

hydrothermal-synthesis

display_name: hydrothermal-synthesis

oxygen-vacancy

display_name: oxygen-vacancy


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

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

ab-initio

display_name: ab-initio

alloy-theory

display_name: alloy-theory

cluster-expansion

display_name: cluster-expansion

phase-stability

display_name: phase-stability

precipitation

display_name: precipitation


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

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

calcium-batteries

display_name: calcium-batteries

marokite

display_name: marokite

perovskite

display_name: perovskite

post-spinel

display_name: post-spinel


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

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

computational-screening

display_name: computational-screening

marvel

display_name: marvel

photoelectrochemical-water-splitting

display_name: photoelectrochemical-water-splitting

surface-passivation

display_name: surface-passivation


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

materialscloud

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

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

en: Materials Cloud

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

experimental

display_name: experimental

magnetism

display_name: magnetism

two-dimensional-electron-gas

display_name: two-dimensional-electron-gas

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


exploding-and-weeping-ceramics

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

en: Exploding and weeping ceramics

title_for_slug: exploding-and-weeping-ceramics

envi

en: Environment

ceramics

display_name: ceramics

cystallographic-compatibility

display_name: cystallographic-compatibility

shape-memory

display_name: shape-memory


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

en: The role of water in host-guest interaction

enhanced-sampling

display_name: enhanced-sampling

erc

display_name: erc

host-guest

display_name: host-guest

ligand-binding

display_name: ligand-binding

molecular-dynamics

display_name: molecular-dynamics

neural-network

display_name: neural-network

snsf

display_name: snsf

water

display_name: water


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

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

dark-current

display_name: dark-current

detectivity

display_name: detectivity

organic-photodetectors

display_name: organic-photodetectors

traps

display_name: traps


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

en: Gas adsorption and process performance data for MOFs

aiida

display_name: aiida

marvel

display_name: marvel

mof

display_name: mof

nanoporous

display_name: nanoporous


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

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

cscs

display_name: cscs

density-functional-perturbation-theory

display_name: density-functional-perturbation-theory

hubbard-parameters

display_name: hubbard-parameters

linear-response-theory

display_name: linear-response-theory

marvelosp

display_name: marvelosp

open-science

display_name: open-science

open-source-software

display_name: open-source-software

quantum-espresso

display_name: quantum-espresso

self-interaction-corrections

display_name: self-interaction-corrections

transition-metal-compounds

display_name: transition-metal-compounds


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

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

band-gap

display_name: band-gap

dftu

display_name: dftu

hubbard-correction

display_name: hubbard-correction

lowdin-orthogonalization

display_name: lowdin-orthogonalization

self-interaction

display_name: self-interaction

snsf

display_name: snsf


en: Mg-Al mixed metal oxides (MMOs), derived from the decomposition of layered double hydroxides (LDHs), have been purposed as a material for CO2 capture of industrial plant emissions. In order to aid in the design and optimization of these materials for CO2 capture at 200 °C, we have used the combination of solid-state nuclear magnetic resonance (ssNMR) and density functional theory (DFT) to characterize the CO2 gas sorption products and determine the various sorption sites in the Mg-Al MMOs. Comparison of DFT cluster calculations with 13C chemical shift of the chemisorbed products indicates that mono and bi-dentate carbonate are formed at the Mg-O site with an adjacent Al substitution of an Mg atom, while bicarbonate is formed at Mg-OH sites without adjacent Al substitution. Quantitative 13C NMR shows an increase in the relative amount of strongly basic sites, where the monodentate carbonate product is formed, with increasing Al mole % in the MMO. This detailed understanding of the various basic Mg-O sites presents in the MMO material, and the formation of the carbonate, bidentate carbonate and bicarbonate chemisorbed species yields new insight into the mechanism of CO2 adsorption at 200 °C which can further aid in the design and capture capacity optimization of the materials.

en: Characterization of chemisorbed species and active adsorption sites in Mg-Al mixed metal oxides for high temperature CO2 capture

carbon-capture

display_name: carbon-capture

dft

display_name: dft

epfl

display_name: epfl

marvel

display_name: marvel

nmr

display_name: nmr


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

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

materialscloud

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

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

en: Materials Cloud

en: The importance of nuclear quantum effects for NMR crystallography

first-principles-gipaw-dft

display_name: first-principles-gipaw-dft

machine-learning

display_name: machine-learning

molecular-crystals

display_name: molecular-crystals

nmr-chemical-shielding

display_name: nmr-chemical-shielding

nuclear-quantum-effects

display_name: nuclear-quantum-effects


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

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

envi

en: Environment

absorption-spectrum

display_name: absorption-spectrum

bismuth

display_name: bismuth

cscs

display_name: cscs

density-functional-theory

display_name: density-functional-theory

density-of-states

display_name: density-of-states

extreme-ultraviolet

display_name: extreme-ultraviolet

investissements-davenir

display_name: investissements-davenir

marvelosp

display_name: marvelosp

photoexcitation

display_name: photoexcitation

real-space-atomic-motion

display_name: real-space-atomic-motion

transient-reflectivity

display_name: transient-reflectivity


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

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

ab-initio

display_name: ab-initio

boltzmann-transport

display_name: boltzmann-transport

electron-drag

display_name: electron-drag

electron-phonon-coupling

display_name: electron-phonon-coupling

electron-transport

display_name: electron-transport

intersect

display_name: intersect

phonon-drag

display_name: phonon-drag

phonon-transport

display_name: phonon-transport

thermoelectricity

display_name: thermoelectricity


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

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

dft

display_name: dft

gw

display_name: gw

marvel

display_name: marvel

negf

display_name: negf


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

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

aiidalab

display_name: aiidalab

graphene-nanoribbons

display_name: graphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis

scanning-probe-microscopy

display_name: scanning-probe-microscopy

snsf

display_name: snsf


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

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

casino-quantum-monte-carlo

display_name: casino-quantum-monte-carlo

density-functional-theory

display_name: density-functional-theory

quantum-espresso

display_name: quantum-espresso


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

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

ab-initio

display_name: ab-initio

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


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

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

nmr

display_name: nmr

resolution

display_name: resolution


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

materialscloud

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

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

en: Materials Cloud

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

dft

display_name: dft

fet

display_name: fet

grapheneflagship

display_name: grapheneflagship

raman

display_name: raman


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

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

envi

en: Environment

arpes-dft

display_name: arpes-dft

nccr-marvel

display_name: nccr-marvel

vhss

display_name: vhss


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

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

graphene-nanoribbons

display_name: graphene-nanoribbons

marvel

display_name: marvel

on-surface-synthesis

display_name: on-surface-synthesis

stm

display_name: stm


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

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

carbon-nanotubes

display_name: carbon-nanotubes

random-symmetric-generation

display_name: random-symmetric-generation

schwarzites

display_name: schwarzites

zeolite-templated-nanostructures

display_name: zeolite-templated-nanostructures


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

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

cosmo

display_name: cosmo

electronic-density-of-states

display_name: electronic-density-of-states

epfl

display_name: epfl

machine-learning

display_name: machine-learning

materials-science

display_name: materials-science

metal-alloys

display_name: metal-alloys

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potentials

display_name: neural-network-potentials

snsf

display_name: snsf


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

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

cscs

display_name: cscs

defects

display_name: defects

electronic-transport

display_name: electronic-transport

gnr

display_name: gnr

graphene-nanoribbons

display_name: graphene-nanoribbons

magnetism

display_name: magnetism

onr

display_name: onr


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

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

marvel

display_name: marvel


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

en: Unidirectional Kondo scattering in layered NbS2

first-principles-calculation

display_name: first-principles-calculation

vasp

display_name: vasp


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

materialscloud

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

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

en: Materials Cloud

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

excited-state-properties

display_name: excited-state-properties

hybrid-density-functional-theory

display_name: hybrid-density-functional-theory

marie-curie-fellowship

display_name: marie-curie-fellowship

theoretical-spectroscopy

display_name: theoretical-spectroscopy


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

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

envi

en: Environment

ab-initio

display_name: ab-initio

metal-ceramic-interfaces

display_name: metal-ceramic-interfaces

nanomultilayers

display_name: nanomultilayers

vacancy-driven-diffusion

display_name: vacancy-driven-diffusion


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

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

anomalous-hall-effect

display_name: anomalous-hall-effect

anomalous-nernst-effect

display_name: anomalous-nernst-effect

experimental

display_name: experimental

magnetic-weyl-semimetal

display_name: magnetic-weyl-semimetal

topological-spin-texture

display_name: topological-spin-texture


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

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

neural-networks

display_name: neural-networks

temperature-scaling

display_name: temperature-scaling

uncertainty-quantification

display_name: uncertainty-quantification


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

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

epfl

display_name: epfl

erc

display_name: erc

photoswitchable-organocatalysis

display_name: photoswitchable-organocatalysis

reversible-steric-shielding

display_name: reversible-steric-shielding

snsf

display_name: snsf

statistical-sampling

display_name: statistical-sampling


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

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

ab-initio

display_name: ab-initio

catalysis

display_name: catalysis

on-surface-synthesis

display_name: on-surface-synthesis


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

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

co-oxidation

display_name: co-oxidation

co-redox

display_name: co-redox

operando-xas

display_name: operando-xas

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


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

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

covalent-organic-frameworks

display_name: covalent-organic-frameworks

dft

display_name: dft

drug-delivery

display_name: drug-delivery

metal-organic-frameworks

display_name: metal-organic-frameworks


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

materialscloud

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

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

en: Materials Cloud

en: SPAᴴM: the spectrum of approximated hamiltonian matrices representations

epfl

display_name: epfl

guess-hamiltonians

display_name: guess-hamiltonians

machine-learning

display_name: machine-learning

molecular-representation

display_name: molecular-representation

spahm

display_name: spahm


many-body-self-interaction-and-polarons

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

en: Many-body self-interaction and polarons

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

envi

en: Environment

density-functional-theory

display_name: density-functional-theory

electron-screening

display_name: electron-screening

many-body-self-interaction

display_name: many-body-self-interaction

mgo

display_name: mgo

one-body-self-interaction

display_name: one-body-self-interaction

polaron-localization

display_name: polaron-localization

polarons

display_name: polarons

self-interaction

display_name: self-interaction


en: Hot exciton materials have the potential to improve the quantum efficiency of organic light-emitting diodes (OLEDs) by promoting high Reversed InterSystem Crossing (hRISC) between a high-lying triplet (Tn, n≥2) and a radiative singlet (Sm). In recent years, donor–acceptor-donor (D-A-D) molecular systems have shown great promise in its ability to enhance the hRISC process under certain conditions. However, strategies to find appropriate D-A-D combinations beyond trial-and-error are still elusive. This work exposes the limited applicability of the current fragment-based design rules and proposes high-throughput screening as the optimal route to find promising candidates that fulfill the energy criteria for hot exciton materials. The strategy consists of first establishing the thresholds for large triplet-triplet splitting and small singlet-triplet gap, then filtering combinations through rate comparison of competitive crossing pathways, and finally confirming hRISC with spin-orbital coupling (SOC) evaluation. Based on a curated dataset of 234 D-A-D compounds, our protocol identifies 31 promising candidates with potential for hRISC, 4 of which have been previously reported in the literature. Remarkably, while most of the promising systems show prominent hybridized local and CT (HLCT) character, several candidates do not fulfill this condition, indicating that different routes are possible to design efficient OLED materials.

en: Donor-acceptor-donor "hot exciton" triads for high reverse intersystem crossing in OLEDs

computational-screening

display_name: computational-screening

donor-acceptor-donor

display_name: donor-acceptor-donor

hot-exciton-oleds

display_name: hot-exciton-oleds

marie-curie-fellowship

display_name: marie-curie-fellowship


en:
Prediction of microstructure evolution during material processing is essential to control the material properties. Simulation tools for microstructure evolution prediction based on physical concepts are computationally expensive and time-consuming. Therefore, they are not practical when either there is an urgent need for microstructure morphology during the process, or there is a need to generate big microstructure datasets. Essentially, microstructure evolution prediction is a spatiotemporal sequence prediction problem, where the prediction of material microstructure is difficult due to different process histories and chemistry. We propose a Predictive Recurrent Neural Network (PredRNN) model for the microstructure prediction, which extends the inner-layer transition function of memory states in LSTMs to spatiotemporal memory flow. As a case study, we used a dataset from spinodal decomposition simulation of FeCrCo alloy created by the phase-field method for training and predicting future microstructures by previous observations. The results show that the trained network predicts quantitatively accurate microstructure morphologies while it is several orders of magnitude faster than the phase field method.
The trained model aims to generate future material microstructures by learning from the historical microstructures, where the visual dynamics are believed to have modular structures that can be learned with compositional subsystems. A PredRNN (https://github.com/thuml/predrnn-pytorch) model was trained to predict Fe-Cr-Co microstructures evolution during the spinodal decomposition.

en: Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network

phase-field-spinodal-decomposition

display_name: phase-field-spinodal-decomposition


en: Simulations of condensed matter systems at the hybrid density functional theory (DFT) level  pose significant computational challenges. The elevated costs arise from the non-local nature of the Hartree-Fock exchange (HFX) in conjunction with the necessity to approach the thermodynamic limit (TDL). In this work, we address these issues with the development of a new efficient method for the calculation of HFX in periodic systems, employing k-point sampling. We rely on a local atom-specific resolution-of-the-identity scheme, the use of atom-centered Gaussian type orbitals (GTOs), and the truncation of the Coulomb interaction to limit computational complexity. Our real-space approach exhibits a scaling that is  at worst linear with the number of k-points.  Issues related to basis set diffuseness are effectively addressed through the auxiliary density matrix method (ADMM). We report the implementation in the CP2K software package, as well as accuracy and performance benchmarks. The method demonstrates excellent agreement with equivalent Gamma-point supercell calculations in terms of relative energies and nuclear gradients. Good strong and weak scaling performances, as well as GPU acceleration, make this implementation a promising candidate for high-performance computing.

en: Efficient periodic resolution-of-the-identity Hartree-Fock exchange method with k-point sampling and Gaussian basis sets

auxiliary-density-matrix-method

display_name: auxiliary-density-matrix-method

high-performance-computing

display_name: high-performance-computing

hybrid-dft

display_name: hybrid-dft

k-point-sampling

display_name: k-point-sampling

method-development

display_name: method-development

periodic-hartree-fock

display_name: periodic-hartree-fock


en: The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation of experimentally observable material properties such as optical absorption and electrical conductivity. We discuss the challenges inherent in the construction of a machine-learning (ML) framework aimed at predicting the DOS as a combination of local contributions that depend in turn on the geometric configuration of neighbors around each atom, using quasiparticle energy levels from density functional theory as training data. We present a challenging case study that includes configurations of silicon spanning a broad set of thermodynamic conditions, ranging from bulk structures to clusters and from semiconducting to metallic behavior. We compare different approaches to represent the DOS, and the accuracy of predicting quantities such as the Fermi level, the electron density at the Fermi level, or the band energy, either directly or as a side product of the evaluation of the DOS. We find that the performance of the model depends crucially on the resolution chosen to smooth the DOS and that there is a tradeoff to be made between the systematic error associated with the smoothing and the error in the ML model for a specific structure. We find however that the errors are not strongly correlated among similar structures, and so the average DOS over an ensemble of configurations is in very good agreement with the reference electronic structure calculations, despite the large nominal error on individual configurations. We demonstrate the usefulness of this approach by computing the density of states of a large amorphous silicon sample, for which it would be prohibitively expensive to compute the DOS by direct electronic structure calculations and show how the atom-centered decomposition of the DOS that is obtained through our model can be used to extract physical insights into the connections between structural and electronic features.

en: Learning the electronic density of states in condensed matter

cosmo-epfl

display_name: cosmo-epfl

marvel

display_name: marvel

snsf

display_name: snsf


en:
Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional formulation. These functionals address two fundamental issues with density functional theory (DFT). First, while Kohn-Sham eigenvalues can loosely mirror experimental quasiparticle energies, they are not meant to reproduce excitation energies and there is formally no connection between the two (except for the HOMO for the exact functional). Second, (semi-)local DFT deviates from the expected piecewise linear behavior of the energy as a function of the total number of electrons. This can make eigenvalues an even poorer proxy for quasiparticle energies and, together with the absence of the exchange-correlation derivative discontinuity, contributes to DFT's underestimation of band gaps. By enforcing a generalized piecewise linearity condition to the entire electronic manifold, Koopmans functionals yield molecular orbital energies and solids-state band structures with comparable accuracy to many-body perturbation theory but at greatly reduced computational cost and preserving a functional formulation. This paper introduces "koopmans", an open-source package that contains all of the code and workflows needed to perform Koopmans functional calculations without requiring expert knowledge. The theory and algorithms behind Koopmans functionals are summarized, and it is shown how one can easily use the koopmans package to obtain reliable spectral properties of molecules and materials.

This archive contains files that accompany the article of the same name.

en: koopmans: an open-source package for accurately and efficiently predicting spectral properties with Koopmans functionals

charged-excitations

display_name: charged-excitations

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure

koopmans-spectral-functionals

display_name: koopmans-spectral-functionals

orbital-density-dependent-functionals

display_name: orbital-density-dependent-functionals


en: For decades transition-metal oxides have generated a huge interest due to the multitude of physical phenomena they exhibit. In this class of materials, the rare-earth nickelates, RNiO₃, stand out for their rich phase diagram stemming from complex couplings between the lattice, electronic and magnetic degrees of freedom. Here, we present a first-principles study of the low-temperature phase for two members of the RNiO₃ series, with R = Pr, Y. We employ density-functional theory with Hubbard corrections accounting not only for the on-site localizing interactions among the Ni-3d electrons (U), but also the inter-site hybridization effects between the transition-metals and the ligands (V). All the U and V parameters are calculated from first-principles using density-functional perturbation theory, resulting in a fully ab initio methodology. Our simulations show that the inclusion of the inter-site interaction parameters V is necessary to simultaneously capture the features well-established by experimental characterizations of the low-temperature state: insulating character, antiferromagnetism and bond disproportionation. On the contrary, for some magnetic orderings the inclusion of on-site interaction parameters U alone completely suppresses the breathing distortion occurring in the low-temperature phase and produces an erroneous electronic state with a vanishing band gap. In addition - only when both the U and V are considered - we predict a polar phase with a magnetization-dependent electric polarization,  supporting very recent experimental observations that suggest a possible occurrence of type-II multiferroicity for these materials.

en: Hybridization driving distortions and multiferroicity in rare-earth nickelates

magnetism

display_name: magnetism

rare-earth-nickelates

display_name: rare-earth-nickelates


en: We study the water dehydrogenation reaction at the BiVO₄(010)-water interface by combining nudged-elastic-band calculations and electronic structure calculations at the hybrid functional level. We investigate the pathway and the kinetic barrier for the adiabatic reaction going from the hole polaron localized in BiVO₄ to the dehydrogenation of the adsorbed water molecule at the interface. The reaction is found to involve the H2O•⁺ radical cation as intermediate, to have a kinetic barrier of 0.7 eV, and to be initiated by the electron transfer. The calculated kinetic barrier is in good agreement with experiment and is consistent with the slow hole transfer kinetics observed at the surface of BiVO₄. To characterize the structural changes occurring during this process, we analyze the O-H distances for three relevant water molecules. We also examine the Wannier functions around the O atom of the adsorbate involved in the reaction to reveal the changes in the electronic structure during the hole hopping. The projected density of states of the lowest unoccupied molecular orbitals allows us to identify the atomic orbitals that are primarily involved in the reaction. We expect that the proposed reaction mechanism generally holds when the surface coverage is dominated by molecularly adsorbed water.

materialscloud

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

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

en: Materials Cloud

en: Oxygen evolution at the BiVO₄-water interface: mechanism of the water dehydrogenation reaction

metal-oxide

display_name: metal-oxide

water-dehydrogenation-reaction

display_name: water-dehydrogenation-reaction


en: We present an inexpensive first-principles approach for describing quantum paraelectricity that combines density functional theory (DFT) treatment of the electronic subsystem with quantum mechanical treatment of the ions through solution of the single-particle Schrödinger equation with the DFT-calculated potential. Using BaTiO3, SrTiO3, and KTaO3 as model systems, we show that the approach can straightforwardly distinguish between ferroelectric, paraelectric, and quantum paraelectric materials, based on simple quantities extracted from standard density functional and density functional perturbation theories. We calculate the influence of isotope substitution and strain on quantum paraelectric behavior and find that, while complete replacement of oxygen-16 by oxygen-18 has a surprisingly small effect, experimentally accessible strains can induce large changes. Finally, we collect the various choices for the phonon mass that have been introduced in the literature. We ide tify those that are most physically meaningful by comparing them with our results that avoid such a choice through the use of mass-weighted coordinates.

en: Ferroelectric, quantum paraelectric, or paraelectric? Calculating the evolution from BaTiO3 to SrTiO3 to KTaO3 using a single-particle quantum mechanical description of the ions

envi

en: Environment

dft

display_name: dft

ferroelectricity

display_name: ferroelectricity

quantum-paraelectricity

display_name: quantum-paraelectricity


en: The concept of order in disordered materials is the key to controlling the mechanical, electrical, and chemical properties of amorphous compounds widely exploited in industrial applications and daily life. Rather, it is far from being understood. Here, we propose a multi-technique numerical approach to study the order/disorder of amorphous materials on both the short- and the medium-range scale. We combine the analysis of the disorder level based on chemical and physical features with their geometrical and topological properties, defining a previously unexplored interplay between the different techniques and the different order scales. We applied this scheme to amorphous GeSe and GeSeTe chalcogenides, showing a modulation of the internal disorder as a function of the stoichiometry and composition: Se-rich systems are less ordered than Ge-rich systems at the short- and medium-range length scales. The present approach can be easily applied to more complex systems containing three or more atom types without any a priori knowledge about the system chemical–physical features, giving a deep insight into the understanding of complex systems.

en: Multi-technique approach to unravel the (dis)order in amorphous materials

amorphous

display_name: amorphous

chalcogenides

display_name: chalcogenides

disorder

display_name: disorder

intersect

display_name: intersect

molecular-dynamics

display_name: molecular-dynamics

openmodel

display_name: openmodel


en: We show that hematite, 𝛼-Fe₂O₃, below its Morin transition, has a ferroic ordering of rank-5 magnetic triakontadipoles on the Fe ions. In the absence of spin-orbit coupling, these are the lowest-order ferroically aligned magnetic multipoles, and they give rise to the g-wave non-relativistic spin splitting in hematite. We find that the ferroically ordered magnetic triakontadipoles result from the simultaneous antiferroic ordering of the charge hexadecapoles and the magnetic dipoles, providing a route to manipulating the magnitude and the sign of the magnetic triakontadipoles as well as the spin splitting. Furthermore, we find that both the ferroic ordering of the magnetic triakontadipoles and many of the spin-split features persist in the weak ferromagnetic phase above the Morin transition temperature.

en: Non-relativistic ferromagnetotriakontadipolar order and spin splitting in hematite

erc

display_name: erc

multipoles

display_name: multipoles


en: Verification and validation of methods and first-principles software are at the core of computational solid-state physics but are too rarely addressed. We compare four first-principles codes: Abinit, Quantum ESPRESSO, EPW, ZG, and three methods: (i) the Allen-Heine-Cardona theory using density functional perturbation theory (DFPT), (ii)  the Allen-Heine-Cardona theory using Wannier function perturbation theory (WFPT), and (iii) an adiabatic non-perturbative frozen-phonon method. For these cases, we compute the real and imaginary parts of the electron-phonon self-energy in diamond and BAs, including dipoles and quadrupoles when interpolating. We find excellent agreement between software that implements the same formalism as well as good agreement between the DFPT and WFPT methods. Importantly, we find that the Deybe-Waller term is momentum dependent which impacts the mass enhancement, yielding approximate results when using the Luttinger approximations. Finally, we compare the electron-phonon spectral functions between Abinit and EPW and find excellent agreement even away from the band edges.

en: Zero-point renormalization of the bandgap, mass enhancement, and spectral functions: Validation of methods and verification of first-principles codes

ab-initio

display_name: ab-initio

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

verification-and-validation

display_name: verification-and-validation

zero-point-renormalization

display_name: zero-point-renormalization


en: Efficient O₂ reduction to H₂O₂, vital for energy conversion and environmental cleanup, relies on precise control of heterogeneous catalysts interacting with reaction species. Through high-throughput density functional theory calculations, consisting of 369 single atom catalysts, we identified the polarized descriptor (electric dipole strength) on two-dimensional carbon materials, revealing insights into the catalytic effect of support polarization. Surprisingly, this descriptor exhibits advanced scaling relationships towards H₂O₂ synthesis, incorporating factors such as active metals, coordination environments, and surface curvatures, highlighting its widespread significance. Furthermore, it demonstrates reliable predictability for O₂ adsorption in dynamic water environments, with optimal reactivity observed within the range of -1.40 to -1.00 e·Å, as confirmed by dynamic and static simulations of the 2e- pathway of O₂ reduction. In essence, these findings offer valuable insights for the rational design of electrocatalysts tailored for selective O₂ reduction.

en: Scaling relations and dynamical predictiveness of electric dipole strength on 2e- ORR catalytic property

density-functional-theory-calculations

display_name: density-functional-theory-calculations

electric-dipole-strength

display_name: electric-dipole-strength

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction

support-polarization

display_name: support-polarization


en:
We performed a computational study using Density Functional Theory calculations on a copper-graphene system. A global minima search was performed using the Minima Hopping algorithm to find multiple graphene isomers that can appear on different substrates. As a model of copper substrate, we used a 112 copper atom cluster and as a model of graphene flake, we used 40 carbon atoms on top of the copper surface. The system was placed in vacuum to guarantee no interaction between images.

The dataset contains coordinates of graphene flake isomers on crystalline and amorphous Cu substrates.

en: Comparative study of defects in graphene flake grown on amorphous and crystalline copper surfaces

copper

display_name: copper

density-functional-theory

display_name: density-functional-theory

graphene

display_name: graphene

graphene-growth

display_name: graphene-growth

metal-substrate

display_name: metal-substrate


en: The phase of the quantum-mechanical wave function can encode a topological structure with wide-ranging physical consequences, such as anomalous transport effects and the existence of edge states robust against perturbations. While this has been exhaustively demonstrated for electrons, properties associated with the elementary quasiparticles in magnetic materials are still underexplored. Here, we show theoretically and via inelastic neutron scattering experiments that the bulk ferromagnet Mn₅Ge₃ hosts gapped topological Dirac magnons. Although inversion symmetry prohibits a net Dzyaloshinskii-Moriya interaction in the unit cell, it is locally allowed and is responsible for the gap opening in the magnon spectrum. This gap is predicted and experimentally verified to close by rotating the magnetization away from the c-axis. Hence, Mn₅Ge₃ is the first realization of a gapped Dirac magnon material in three dimensions. Its tunability by chemical doping or by thin film nanostructuring defines an exciting new platform to explore and design topological magnons.

en: Topological magnons driven by the Dzyaloshinskii-Moriya interaction in the centrosymmetric ferromagnet Mn₅Ge₃

dzyaloshinskii-moriya-interaction

display_name: dzyaloshinskii-moriya-interaction

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

marvel

display_name: marvel

spin-waves

display_name: spin-waves

topological-magnon

display_name: topological-magnon


en: Understanding electronic interactions in high-temperature superconductors is an outstanding challenge. In the widely studied cuprate materials, experimental evidence points to strong electron-phonon (e-ph) coupling and broad photoemission spectra. Yet, the microscopic origin of this behavior is not fully understood. Here we study e-ph interactions and polarons in a prototypical parent (undoped) cuprate, La₂CuO₄ (LCO), by means of first-principles calculations. Leveraging parameter-free Hubbard-corrected density functional theory, we obtain a ground state with band gap and Cu magnetic moment in nearly exact agreement with experiments. This enables a quantitative characterization of e-ph interactions. Our calculations reveal two classes of longitudinal optical (LO) phonons with strong e-ph coupling to hole states. These modes consist of Cu-O plane bond-stretching and bond-bending as well as vibrations of apical O atoms. The hole spectral functions, obtained with a cumulant method that can capture strong e-ph coupling, exhibit broad quasiparticle peaks with a small spectral weight (Z≈0.25) and pronounced LO-phonon sidebands characteristic of polaron effects. Our calculations predict features observed in photoemission spectra, including a 40-meV peak in the e-ph coupling distribution function not explained by existing models. These results show that the universal strong e-ph coupling found experimentally in lanthanum cuprates is an intrinsic feature of the parent compound, and elucidates its microscopic origin.

materialscloud

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

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

en: Materials Cloud

en: First-principles electron-phonon interactions and polarons in the parent cuprate La₂CuO₄

dftu

display_name: dftu

electron-phonon-coupling

display_name: electron-phonon-coupling

hubbard-u

display_name: hubbard-u

parent-cuprate

display_name: parent-cuprate

phonons

display_name: phonons

polarons

display_name: polarons

spectral-function

display_name: spectral-function