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


$oid: OBSOLETE

en: Since the preliminary work of Anisimov and co-workers, the Hubbard corrected DFT+U functional has been used for predicting properties of correlated materials by applying on-site effective Coulomb interactions to specific orbitals. However, the determination of the Hubbard U parameter has remained under intense discussion despite the multitude of approaches proposed. Here, we define a selection criterion based on the use of polaronic defect states for the enforcement of the piecewise linearity of the total energy upon electron occupation. A good agreement with results from piecewise-linear hybrid functionals is found for the electronic and structural properties of polarons, including the formation energies. The values of U determined in this way are found to give a robust description of the polaron energetics upon variation of the considered state. In particular, we also address a polaron hopping pathway, finding that the determined value of U leads to accurate energetics without requiring a configurational-dependent U. It is emphasized that the selection of U should be based on physical properties directly associated with the orbitals to which U is applied, rather than on more global properties such as band gaps and band widths.  For comparison, we also determine U through a well-established linear-response scheme finding noticeably different values of U and consequently different formation energies. Possible origins of these discrepancies are discussed. As case studies, we consider the self-trapped electron in BiVO₄, the self-trapped hole in MgO, the Li-trapped hole in MgO, and the Al-trapped hole in 𝛼-SiO₂.

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: Hubbard U through polaronic defect states

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

dftu

display_name: dftu

hybrid-functionals

display_name: hybrid-functionals

polarons

display_name: polarons

self-interaction

display_name: self-interaction


en: We establish the sign of the linear magnetoelectric (ME) coefficient, α, in chromia, Cr₂O₃. Cr₂O₃ is the prototypical linear ME material, in which an electric (magnetic) field induces a linearly proportional magnetization (polarization), and a single magnetic domain can be selected by annealing in combined magnetic (H) and electric (E) fields. Opposite antiferromagnetic domains have opposite ME responses, and which antiferromagnetic domain corresponds to which sign of response has previously been unclear. We use density functional theory (DFT) to calculate the magnetic response of a single antiferromagnetic domain of Cr₂O₃ to an applied in-plane electric field at 0 K. We find that the domain with nearest neighbor magnetic moments oriented away from (towards) each other has a negative (positive) in-plane ME coefficient, α⊥, at 0 K. We show that this sign is consistent with all other DFT calculations in the literature that specified the domain orientation, independent of the choice of DFT code or functional, the method used to apply the field, and whether the direct (magnetic field) or inverse (electric field) ME response was calculated. Next, we reanalyze our previously published spherical neutron polarimetry data to determine the antiferromagnetic domain produced by annealing in combined E and H fields oriented along the crystallographic symmetry axis at room temperature. We find that the antiferromagnetic domain with nearest-neighbor magnetic moments oriented away from (towards) each other is produced by annealing in (anti-)parallel E and H fields, corresponding to a positive (negative) axial ME coefficient, α∥, at room temperature. Since α⊥ at 0 K and α∥ at room temperature are known to be of opposite sign, our computational and experimental results are consistent. This dataset contains the input data to reproduce the calculation of the magnetoelectric effect as plotted in Fig. 3 of the manuscript, for Elk, Vasp, and Quantum Espresso.

en: On the sign of the linear magnetoelectric coefficient in Cr₂O₃

dft

display_name: dft

horizon-europe

display_name: horizon-europe

magnetoelectric

display_name: magnetoelectric


en:
The development of novel double-hybrid density functionals offers new levels of accuracy, and is leading to fresh insights into the fundamental properties of matter. Hartree–Fock exact exchange and correlated wave function methods such as MP2 and direct RPA are usually required to build such functionals. Their high computational cost is a concern, and their application to large and periodic systems is therefore limited. In this work, low-scaling methods for HFX, SOS-MP2 and direct RPA energy gradients are developed and implemented in the CP2K software package. The use of the resolution-of-the-identity approximation with a short range metric and atom-centered basis functions lead to sparsity, allowing for sparse tensor contractions to take place. These operations are efficiently performed with the newly developed DBT and DBM libraries, which scale to hundreds of GPU nodes. The resulting methods, RI-HFX, SOS-MP2 and dRPA, were benchmarked on large supercomputers. They exhibit favorable sub-cubic scaling with system size, good strong scaling performance, and GPU acceleration up to a factor of 3. These developments will allow for double-hybrid level calculations of large and periodic condensed phase systems to take place on a more regular basis.

This record contains all CP2K input and output files used for the paper.

en: Sparse tensor based nuclear gradients for periodic Hartree–Fock and low-scaling correlated wave function methods in the CP2K software package: A massively parallel and GPU accelerated implementation.

correlated-wave-function

display_name: correlated-wave-function

gpu

display_name: gpu

hartree-fock

display_name: hartree-fock

hpc

display_name: hpc

low-scaling

display_name: low-scaling

nuclear-gradients

display_name: nuclear-gradients

rpa

display_name: rpa

sparse-tensors

display_name: sparse-tensors


en: Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species beyond "simple" drug-like compounds or molecules comprised of well-defined building blocks (e.g., peptides) is challenging, as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversity. Here, we introduce the OFF–ON (Organic Fragments From Organocatalysts that are Non-modular) database, a repository of 7,869 equilibrium and 67,457 non--equilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible functional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a Local Kernel Regression model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF–ON dataset offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound comprised of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior.

en: From organic fragments to photoswitchable catalysts: the off-on structural repository for transferable kernel-based potentials

free-energy

display_name: free-energy

machine-learning

display_name: machine-learning

photoswitchable-organocatalyst

display_name: photoswitchable-organocatalyst


en:
In the past few years, the theory of thermal transport in amorphous solids has been substantially extended beyond the Allen-Feldman model. The resulting formulation, based on the Green-Kubo linear response or the Wigner-transport equation, bridges this model for glasses with the traditional Boltzmann kinetic approach for crystals. The computational effort required by these methods usually scales as the cube of the number of atoms, thus severely limiting the size range of computationally affordable glass models. Leveraging hydrodynamic arguments, we show how this issue can be overcome through a simple formula to extrapolate a reliable estimate of the bulk thermal conductivity of glasses from finite models of moderate size. We showcase our findings for realistic models of paradigmatic glassy materials.
This repository contains example inputs to compute the hydrodynamic extrapolation of the thermal conductivity of glasses.
Explicit examples are for amorphous silicon. Optimized atomic configurations are provided also for amorphous silica and silicon carbide.

en: Hydrodynamic finite-size scaling of the thermal conductivity in glasses

amorphous-solids

display_name: amorphous-solids

glasses

display_name: glasses

max

display_name: max

size-effects

display_name: size-effects

thermal-transport

display_name: thermal-transport


en: Graph neural networks are a popular deep-learning architecture in applications to materials and molecules, and the most widespread implementations rely on interatomic distances as geometric descriptors. Unfortunately, GNNs based on distances are not complete, i.e. there are geometries, corresponding to molecules and/or periodic structures, that are indistinguishable by the GNN. For these, the corresponding machine-learning models will be unable to learn differences in the properties of the "degenerate" structures. This dataset contains a collection of molecular and solid structures that cannot be discriminated by distance-based graph neural networks, together with example code showing how to parse them and use to demonstrate the shortcomings of this class of machine-learning algorithms.

en: Incompleteness of graph neural networks for points clouds in three dimensions

graph-neural-networks

display_name: graph-neural-networks

marvel

display_name: marvel

point-clouds

display_name: point-clouds


en: Antiferromagnets are promising platforms for transduction and transmission of quantum information via magnons — the quanta of spin waves — and they offer advantages over ferromagnets in regard to dissipation, speed of response, and robustness to external fields. Recently, transduction was shown in a van der Waals antiferromagnets, where strong spin-exciton coupling enables readout of the amplitude and phase of coherent magnons by photons of visible light. This discovery shifts the focus of research to transmission, specifically to exploring the nonlocal interactions that enable magnon wave packets to propagate. Here we demonstrate that magnon propagation is mediated by the long-range dipole-dipole interaction. This coupling is an inevitable consequence of fundamental electrodynamics, and as such, will likely mediate the propagation of spin at long wavelengths in the entire class of van der Waals magnets currently under investigation. Successfully identifying the mechanism of spin propagation provides a set of optimization rules, as well as caveats, that are essential for any future applications of these promising systems.

en: Dipolar spin wave packet transport in a van der Waals antiferromagnet

antiferromagnets

display_name: antiferromagnets

magnetostatic-spin-waves

display_name: magnetostatic-spin-waves

spin-transport

display_name: spin-transport

time-resolved-kerr-microscopy

display_name: time-resolved-kerr-microscopy


en: Here we report a resonant x-ray diffraction (RXD) study at the Cu L₃ edge on the multichiral system CsCuCl₃, exhibiting helical magnetic order in a chiral crystal structure. RXD is a powerful technique to disentangle electronic degrees of freedom due to its sensitivity to electric monopoles (charge), magnetic dipoles (spin), and electric quadrupoles (orbital). We characterize electric quadrupole moments around Cu ascribed to the unoccupied Cu 3d orbital, whose quantization axis is off the basal plane. Detailed investigation of magnetic reflections reveals additional sinusoidal modulations along the principal axis superimposed on the reported helical structure, i.e., a longitudinal conical (helical-butterfly) structure. The out-of-plane modulations imply significant spin-orbit interaction despite S=1/2 of Cu²⁺.

en: Conical spin order with chiral quadrupole helix in CsCuCl₃

experimental

display_name: experimental


en:
Tin-lead halide perovskites with a bandgap near 1.2 electron-volt hold great promise for thin-film photovoltaics. However, the film quality of solution-processed Sn-Pb perovskites is compromised by the asynchronous crystallization behavior between Sn and Pb components, where the crystallization of Sn-based perovskites tends to occur faster than that of Pb. Here we show that the rapid crystallization of Sn is rooted in its stereochemically active lone pair, which impedes coordination between the metal ion and Lewis base ligands in the perovskite precursor. From this perspective, we introduce a noncovalent binding agent targeting the open metal site of coordinatively unsaturated Sn(II) solvates, thereby synchronizing crystallization kinetics and homogenizing Sn-Pb alloying. The resultant single-junction Sn-Pb perovskite solar cells achieve a certified power conversion efficiency of 24.13 per cent. The encapsulated device retains 90 per cent of the initial efficiency after 795 hours of maximum power point operation under simulated one-sun illumination.
We conducted DFT and AIMD simulations to study the structure and interactions of metal iodide-solvent ligand complexes. This archive contains the optimized atomic coordinates of PbI2/SnI2-ligands complexes and the initial and final configurations of AIMD trajectories.

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: Synchronized crystallization in tin-lead perovskite solar cells

halide-perovskite

display_name: halide-perovskite

lead-iodide

display_name: lead-iodide

tin-iodide

display_name: tin-iodide


en:
This data record contains the xyz-style files for 2'707 organic molecular crystals and their 3'202 molecular components. The crystals were initially taken directly from the Cambridge Structure Database, and the relaxations were computed and reported by [1]. This record contains an augmentation of a subset of this data, where we have identified the molecular constituents of each crystal, performed geometric relaxations using the same computational parameters, and identified the constituent functional groups. A full detail of methodology and provenance is included in the ESI of [2].
Each crystal and molecule has been relaxed using Quantum Espresso with the following parameters: the PBE exchange-correlation functional, the D2 dispersion correction, ultrasoft pseudopotentials with GIPAW reconstruction, and an equivalent plane-wave energy cutoff of 60 Ryd. We converged the energies within 1E-4 Ryd and forces below 1E-3 Ryd/Bohr, respectively, using MT-decoupling for the molecules to ensure no self-interaction. Furthermore, we compute the binding energy based on the lowest energy conformed represented in the dataset, ensuring comparability between crystals and co-crystals of similar stoichiometries without needing to obtain the global minimum conformation of each molecule.

en: Lattice energies and relaxed geometries for 2'707 organic molecular crystals and their 3'242 molecular components.

envi

en: Environment

snsf

display_name: snsf

trinity-college

display_name: trinity-college


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:
Multi-band effects in superconducting heterostructures provide a rich playground for unconventional physics. We combine two complementary approaches based on density-functional theory (DFT) and effective low-energy model theory in order to investigate the proximity effect in a gold overlayer on the s-wave superconductor aluminium. We explain both theoretical approaches and intertwine the effective model and DFT analysis. This allows us to predict finite energy superconducting avoided crossings due to the interplay of the Rashba surface state of Au, and hybridization with the electronic structure of superconducting Al. We investigate the nature of the induced superconducting pairing and analyze their mixed singlet-triplet character. Our findings demonstrate the general recipes to explore material systems that exhibit novel finite-energy pairings.

This dataset accompanies a publication where the data is presented and discussed in detail.

en: Proximity-induced Cooper pairing at low and finite energies in the gold Rashba surface state

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

jukkr

display_name: jukkr

spin-orbit-coupling

display_name: spin-orbit-coupling

superconductivity

display_name: superconductivity


en: Multi-junction solar cells pose a particular modeling challenge, and advanced methods are needed to improve their efficiency and reduce their cost. We present here data about the I-V and P-V characteristics gotten using the electrical parameters obtained through an easy method to extract the electrical parameters of multi-junction solar cells using a single-diode model. The proposed technique is based solely on manufacturer data, and we solve the necessary mathematical equations numerically.

en: Curves data of a simple implicit method for parameters estimation of multi-junction solar cells

iv

display_name: iv

multiple-junction

display_name: multiple-junction

parameter-extraction-methods

display_name: parameter-extraction-methods

solar-energy

display_name: solar-energy


en:
A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are developed to categorize materials into five distinct topological types, with the XGBoost model achieving an impressive 85.2% classification accuracy. By conducting generalization tests on different sub-datasets, differences are identified between the original datasets in terms of topological types, chemical elements, unknown magnetic compounds, and feature space coverage. Their impact on model performance is analyzed.
Turning to the simpler binary classification between trivial insulators and nontrivial topological materials, three different approaches are also tested. Key characteristics influencing material topology are identified, with the maximum packing efficiency and the fraction of p valence electrons being highlighted as critical features.

en: Machine learning on multiple topological materials datasets

database

display_name: database

machine-learning

display_name: machine-learning

topological-materials

display_name: topological-materials


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:
This study presents the initial implementation of the Fast INtention-Agnostic LEarning Server (FINALES) in a demonstration of a distributed Materials Acceleration Platform (MAP) including experimental and computational methods and a machine learning (ML)-based optimizer. In this demonstration, the optimizer was configured to minimize the density of the electrolyte solutions while maximizing the viscosity by exploiting experimental and computational results. The tenants (the units connected to FINALES in the MAP) are shortly described in the following:

- Autonomous Synthesis and Analysis of Battery electrolytes (ASAB) setup: an experimental tenant providing density and viscosity data using a densimeter of the type DMA 4100M and a viscometer of type Lovis 2000 both by Anton Paar Germany
- Molecular dynamics tenant: a computational tenant capable of providing radial distribution functions, diffusion coefficients, ionic conductivity, transference numbers, heat capacity and density data to the MAP
- Optimizer: the tenant guiding the optimization by processing the available data and generating requests for electrolyte formulations to be tested subsequently

This dataset accompanies the publication:
Vogler, M., Busk, J., Hajiyani, H., Jørgensen, P. B., Safaei, N., Castelli, I. E., Ramirez, F. F., Carlsson, J., Pizzi, G., Clark, S., Hanke, F., Bhowmik, A. & Stein, H. S. Brokering between tenants for an international materials acceleration platform. Matter 6, 2647–2665 (2023). DOI: https://doi.org/10.1016/j.matt.2023.07.016

en: FINALES (06/2022) – Electrolyte Optimization for Minimum Density and Maximum Viscosity

battery-electrolyte

display_name: battery-electrolyte

big-map

display_name: big-map

conductivity

display_name: conductivity

density

display_name: density

materials-acceleration-platform-map

display_name: materials-acceleration-platform-map

polis

display_name: polis

radial-distribution-function-rdf

display_name: radial-distribution-function-rdf

viscosity

display_name: viscosity


en: Proximity-induced superconductivity in hybrid devices of topological insulators and superconductors offers a promising platform for the pursuit of elusive topological superconductivity and its anticipated applications, such as fault-tolerant quantum computing. To study and harness such hybrid devices, a key challenge is the realization of highly functional material interfaces with a suitable superconductor featuring 2e-periodic parity-conserving transport to ensure a superconducting hard-gap free of unpaired electrons, which is important for Majorana physics. A superconductor well-known for this characteristic is Al, however, its direct integration into devices based on tetradymite topological insulators has so far been found to yield non-transparent interfaces. By focusing on Bi₂Te₃-Al heterostructures, this study identifies detrimental interdiffusion processes at the interface through atomically resolved structural and chemical analysis, and showcase their mitigation by leveraging different interlayers – namely Nb, Ti, Pd, and Pt – between Bi₂Te₃ and Al. Through structural transformation of the interlayer materials (X) into their respective tellurides (XTe₂) atomically-sharp epitaxial interfaces are engineered and further characterized in low-temperature transport experiments on Al-X-Bi₂Te₃-X-Al Josephson junctions and in complementary density functional theory calculations. By demonstrating functional interfaces between Bi₂Te₃ and Al, this work provides key insights and paves the way for the next generation of sophisticated topological devices.

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: Engineering epitaxial interfaces for topological insulator – superconductor hybrid devices with Al electrodes

dft

display_name: dft

epitaxial-interfaces

display_name: epitaxial-interfaces

fleur

display_name: fleur

hybrid-devices

display_name: hybrid-devices

interface-engineering

display_name: interface-engineering

josephson-junctions

display_name: josephson-junctions

jukkr

display_name: jukkr

majorana-platform

display_name: majorana-platform

superconductors

display_name: superconductors

tmdc

display_name: tmdc

topological-insulator

display_name: topological-insulator

topological-superconductivity

display_name: topological-superconductivity


en: The nature of the gap observed at the zone border in the spin excitation spectrum of CrI₃ quasi-two-dimensional single crystals is still controversial. We perform first-principles calculations based on time-dependent density functional perturbation theory, which indicate that the observed gap results from a combination of spin-orbit and interlayer interaction effects. The former give rise to the anisotropic spin-spin interactions that are responsible for its very existence, while the latter determine both its displacement from the K point of the Brillouin zone, due to the in-plane lattice distortions induced by them, and an enhancement of its magnitude, in agreement with experiments and previous theoretical work based on a lattice model.

en: First-principles study of the gap in the spin excitation spectrum of the CrI₃ honeycomb ferromagnet

envi

en: Environment

dirac-cone

display_name: dirac-cone

magnons

display_name: magnons

marvel

display_name: marvel

quantum-espresso

display_name: quantum-espresso

tddft

display_name: tddft


en:
Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be combined explicitly with physically-grounded operations. We present an example of an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on, and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parameterization corresponding to a minimal atom-centered basis. Our results on a comprehensive dataset of hydrocarbons emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency, and providing a blueprint for developing ML-augmented electronic-structure methods.
Here we include the dataset, accompanying the paper linked below, of hydrocarbons including ethane, ethene, butadiene, hexane, hexatriene, isoprene, styrene, polyalkenes (dodecahexaene, tetradecaheptaene, hexadecaoctaene, octadecanonaene, eicosadecaene), aromatics (benzene, azulene, naphthalene, biphenyl), anthracene, beta-carotene, fullerene. We also provide scripts to generate the Fock and overlap matrices in this dataset. The code for machine learning can be found at the Software reference below.

en: Electronic excited states from physically-constrained machine learning

epfl

display_name: epfl

erc

display_name: erc

excited-states

display_name: excited-states

fiamma

display_name: fiamma

hamiltonian

display_name: hamiltonian

lifetimes

display_name: lifetimes

machine-learning

display_name: machine-learning


en: We study self-interaction effects in solvated and strongly-correlated cationic molecular clusters, with a focus on the solvated hydroxyl radical. To address the self-interaction issue, we apply the DC-r²SCAN method, with the auxiliary density matrix approach. Validating our method through simulations of bulk liquid water, we demonstrate that DC-r²SCAN maintains the structural accuracy of r²SCAN while effectively addressing spin density localization issues. Extending our analysis to solvated cationic molecular clusters, we find that the hemibonded motif in the [CH₃S∴CH₃SH]⁺ cluster is disrupted in the DC-r²SCAN simulation, in contrast to r²SCAN that preserves the (three-electron-two-center)-bonded motif. Similarly, for the [SH∴SH₂]⁺ cluster, r²SCAN restores the hemibonded motif through spin leakage, while DC-r²SCAN predicts a weaker hemibond formation influenced by solvent-solute interactions. Our findings demonstrate the potential of DC-r²SCAN combined with the auxiliary density matrix method to improve electronic structure calculations, providing insights into the properties of solvated cationic molecular clusters. This work contributes to the advancement of self-interaction corrected electronic structure theory and offers a computational framework for modeling condensed phase systems with intricate correlation effects.

en: Radicals in aqueous solution: Assessment of density-corrected SCAN functional

aqueous-hydroxyl-radical

display_name: aqueous-hydroxyl-radical

aqueous-sulfanyl-radical

display_name: aqueous-sulfanyl-radical

density-corrected-dft

display_name: density-corrected-dft

density-functional-theory

display_name: density-functional-theory

hydrogen-sulfide

display_name: hydrogen-sulfide

methanethiol

display_name: methanethiol


en: The poor ductility of hcp Mg is attributed to the low activity of non-basal slip systems and so activation of prismatic slip can aid ductility in rolled sheets by providing three additional  Burgers vector slip systems.  Experimental studies show that dilute additions of alloying elements such as Zn and Al leads to softening of prismatic slip at low temperatures but strengthening at higher temperatures.  Here, the role of solute strengthening of prismatic edge dislocations is investigated as a possible explanation for the higher-T strengthening.  Mg-Zn is studied using first-principles inputs in a parameter-free solute strengthening theory.  First-principles DFT is necessary to accurately assess the strong solute chemical interaction energies in the core of the compact edge dislocation.   Such calculations are subtle due to motion of the dislocation in the presence of the solute, and methods to obtain reliable results with acceptable computational cost are discussed.   While interaction energies of Zn in the prism edge core can be quite large (+/- 0.34 eV), the edge solute strengthening of prismatic slip in dilute Mg-Zn remains well below experiments at high temperatures.  However, the large difference (0.68 eV) in Zn/dislocation interaction energies across the core of the edge dislocation suggests strengthening by dynamic strain aging as an explanation for the higher-T strengthening.

en: Solute strengthening of prism edge dislocations in Mg alloys

dft

display_name: dft

edge-solute-strengthening

display_name: edge-solute-strengthening

mg-zn

display_name: mg-zn

prismatic-slip

display_name: prismatic-slip

solutedislocation-interaction-energy

display_name: solutedislocation-interaction-energy


en: The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential can accurately capture silicate-water reactivity. The model was trained on a new dataset comprising 400,000 energies and forces of molecular clusters at the 𝜔-B97XD\def2-TVZP level. To ensure the robustness of the model, we introduce a new and general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. Our trained potential is found to reproduce static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.

en: Learning a reactive potential for silica-water through uncertainty attribution

active-learning

display_name: active-learning

chemical-reactions

display_name: chemical-reactions

free-energy

display_name: free-energy

neural-network-potential

display_name: neural-network-potential

silicates

display_name: silicates


en: The anharmonic infrared spectrum of adsorbed CO is simulated using density functional theory (DFT) to gain insight into the nature of Pd nanoparticles (NPs) supported on ceria. The authors systematically determine how the simulated infrared spectra are affected by CO coverage, NP size (0.5–1.5 nm), NP morphology (octahedral, icosahedral), and metal-support contact angle, by exploring a diversity of realistic models inspired by ab initio molecular dynamics.

en: Identifying the structure of supported metal catalysts using vibrational fingerprints from ab initio nanoscale models

carbon-monoxide

display_name: carbon-monoxide

ceria

display_name: ceria

infrarred

display_name: infrarred

palladium

display_name: palladium

spectroscopy

display_name: spectroscopy


en: Transition metal oxides (e.g., cobalt oxides) often undergo a dynamic surface reconstruction under oxygen evolution reaction (OER) conditions to form the active state, which differs in response to the electrolyte pH. The resulting pH-dependency of OER activity is commonly observed but poorly understood. Herein, we demonstrate that operando X-ray absorption spectroscopy (XAS) characterization enables tracking of the Co oxidation transformation at different pH-directed (hydr)oxide/electrolyte interfaces. Combined with in situ electrochemical analyses, correlations between Co redox dynamics, flat band potential and Co oxidation transformations are established to explain the pH-dependency of OER activity. In alkaline environments, the low flat band potential allows a low-potential Co redox transformation, which in turns favors surface reconstruction. In neutral and acidic environments, an anodic shift of the Co redox transformation increases the OER overpotential, particularly in an acidic environment. The largest OER overpotential, in a neutral environment, is further attributable to the poor polarizability of Co atoms and the slowest Co oxidation transformation with respect to the change in applied potential (or OER current). These findings reveal that the Co oxidation transformation at the interface is the factor directly determining the pH-dependency of OER activity, therefore providing insight into designing efficient OER catalysts in different pH environments.

en: Oxidation of interfacial cobalt controls the pH-dependence of the oxygen evolution reaction

cobalt-oxides

display_name: cobalt-oxides

electrocatalytic-interface

display_name: electrocatalytic-interface

operando-x-ray-adsorption-spectroscopy

display_name: operando-x-ray-adsorption-spectroscopy

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en: Materials demonstrating positive thermal expansion (PTE) or negative thermal expansion (NTE) are quite common, whereas those exhibiting zero thermal expansion (ZTE) are notably scarce. In this work, we identify the mechanical descriptors, namely in-plane tensile stiffness and out-of-plane bending stiffness, that can effectively classify PTE and NTE 2D crystals. By utilizing high throughput calculations and the state-of-the-art symbolic regression method, these descriptors aid in the discovery of ZTE or 2D Invar monolayers with the linear thermal expansion coefficient (LTEC) within ±2×10⁻⁶ K⁻¹ in the middle range of temperatures. Additionally, the descriptors assist the discovery of large PTE and NTE 2D monolayers with the LTEC larger than ±15×10⁻⁶ K⁻¹, which are so-called 2D anti-Invar monolayers. Advancing our understanding of materials with exceptionally low or high thermal expansion is of substantial scientific and technological interest, particularly in developing next-generation electronics at the nanometer even Ångstrom scale.

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: Machine learning enables the discovery of 2D Invar and anti-Invar monolayers

bending-stiffness

display_name: bending-stiffness

linear-coefficient-of-thermal-expansion

display_name: linear-coefficient-of-thermal-expansion

machine-learning

display_name: machine-learning

symbolic-regression

display_name: symbolic-regression

thermal-expansion

display_name: thermal-expansion

zero-thermal-expansion

display_name: zero-thermal-expansion


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

envi

en: Environment

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: Determining the atomic structure of a surface is essential for reliable simulations and in-depth exploration of chemical and atomic-scale physical processes. Using Ge(110) c(8×10) as a case study, this work employs Density Functional Theory (DFT) calculations to examine the role of vibrational entropy in surface reconstruction stability. The Ge(110) c(8×10) unit cell consists of interstitial-based pentamers (Universal Building Block model, UBB) interspersed with regions appearing in STM images as unreconstructed. DFT calculations predict that adding more pentamers lowers the surface energy, contradicting experimental findings. This discrepancy is resolved when vibrational entropy is accounted for and sur-face divacancies are introduced in addition to the UBB pentamers. These divacancies are similar to those proposed earlier in the Tetramer-Heptagonal and Tetragonal Ring (THTR) reconstruction model. The nearest neighbors of the vacancy sites are rebonded as on monatomic step edges. The differences in the vibrational entropy contributed by pentamers, divacancies, and unreconstructed surface stabilize Ge(110) c(8×10) reconstructions with the pentamer density observed experimentally. The presence of divacancies is conceptually consistent with the presence of monatomic steps in Ge(110) "16×2", the most stable reconstruction of this surface.

en: Ge(110) c(8×10) reconstructions stabilized by vibrations

dft

display_name: dft

pbe

display_name: pbe


en:
The presence of spin-orbit coupling or non-collinear magnetic spin states can have dramatic effects on the ground-state and spectral properties of materials, in particular on the band structure. Here, we develop non-collinear Koopmans-compliant functionals based on Wannier functions and density-functional perturbation theory, targeting accurate spectral properties in the quasiparticle approximation. Our non-collinear Koopmans-compliant theory involves functionals of four-component orbitals densities, that can be obtained from the charge and spin-vector densities of Wannier functions. We validate our approach on four emblematic non-magnetic and magnetic semiconductors where the effect of spin-orbit coupling goes from small to very large: the III-IV semiconductor GaAs, the transition-metal dichalcogenide WSe₂, the cubic perovskite CsPbBr₃, and the ferromagnetic semiconductor CrI₃.
The predicted band gaps are comparable in accuracy to state-of-the-art many-body perturbation theory and include spin-dependent interactions and screening effects that are missing in standard diagrammatic approaches based on the random phase approximation. While the inclusion of orbital- and spin-dependent interactions in many-body perturbation theory requires self-screening or vertex corrections, they emerge naturally in the Koopmans-functionals framework.

en: Spin-dependent interactions in orbital-density-dependent functionals: non-collinear Koopmans spectral functionals

metal-halide-perovskite

display_name: metal-halide-perovskite

non-collinear-magnetism

display_name: non-collinear-magnetism

snsf

display_name: snsf

spectral-functionals

display_name: spectral-functionals

spin-dependent-interactions

display_name: spin-dependent-interactions

spin-orbit-coupling

display_name: spin-orbit-coupling

spin-torque

display_name: spin-torque

transition-metal-dichalcogenide

display_name: transition-metal-dichalcogenide


en: In the pursuit of selective conversion of methane directly to methanol in the liquid phase, a common challenge is the concurrent formation of undesirable liquid oxygenates or combustion byproducts. However, we demonstrate that monometallic Pd-CeO2 catalysts, modified by carbon, created by a simple mechanochemical synthesis method exhibit 100% selectivity towards methanol at 75°C, using hydrogen peroxide as oxidizing agent. The solvent-free synthesis yields a distinctive Pd-iC-CeO2 interface, where interfacial carbon (iC) modulates metal-oxide interactions and facilitates tandem methane activation and peroxide decomposition, thus resulting in an exclusive methanol selectivity of 100% with a rate of 117 µmol/gcat at 75°C. Notably, solvent interactions of H2O2 (aq) were found to be critical for methanol selectivity through a DFT-simulated Eley-Rideal-like mechanism. This mechanism uniquely enables the direct conversion of methane into methanol via a solid-liquid-gas process.

en: From Methane to Methanol: Pd-iC-CeO2 Catalysts Engineered for High Selectivity via Mechano-Chemical Synthesis

eley-rideal-mechanism

display_name: eley-rideal-mechanism


en: The design of stealthy hyperuniform (SHU) materials has been a critical topic in realizing bandgap materials without crystalline order. Most previous approaches to constructing SHU materials, such as the collective coordinate method, have assumed the closed system, maintaining the number of particles inside a system during the design process. Here, I develop the concept of evolving wave networks, allowing for the open-system design of disordered materials based on the evolution process. The programs are applied to introduce the concept of evolving wave networks (Code_Set_Fig_2), classify material states according to network parameters (Code_Set_Fig_3), generate the stealthy hyperuniformity (SHU) shielding of existing materials (Code_Set_Fig_4), realize preferential attachment in evolving wave networks (Code_Set_Fig_5), and obtain the following phase diagram of disordered materials (Code_Set_Fig_6).

en: Evolving scattering networks for material classification, stealthy hyperuniform shielding, preferential attachment, and material phase diagram

disordered-material

display_name: disordered-material

evolution

display_name: evolution

network

display_name: network

scattering

display_name: scattering


en: We present a combined density-functional theory and single-site dynamical mean-field theory (DMFT) study of vanadium dioxide (VO₂) using an unconventional set of bond-centered orbitals as the basis of the correlated subspace. VO₂ is a prototypical material undergoing a metal-insulator transition (MIT), hosting both intriguing physical phenomena and the potential for industrial applications. With our choice of correlated subspace basis, we investigate the interplay of structural dimerization and electronic correlations in VO₂ in a computationally cheaper way compared to other state-of-the-art methods, such as cluster DMFT. Our approach allows us to treat the rutile and M1 monoclinic VO₂ phases on an equal footing and to vary the dimerizing distortion continuously, exploring the energetics of the transition between the two phases. The choice of basis presented in this work hence offers a complementary view on the long-standing discussion of the MIT in VO₂ and suggests possible future extensions to other similar materials hosting molecular-orbital-like states.

en: Single-site DFT+DMFT for vanadium dioxide using bond-centered orbitals

dftdmft

display_name: dftdmft


en:
This record contains three datasets and the scripts used to generate figures in "Expanding density-correlation machine learning representations for anisotropic coarse-grained particles." This paper explores the theory and implementation of machine-learning descriptors for ellipsoidal bodies, extending the popular "Smooth Overlap of Atomic Positions" (SOAP) formalism. These case studies serve to demonstrate the different use cases of this technology.  The three datasets are:
- Generated configurations of nematic and smectic liquid crystal systems, with a range of orientational order, characterized by the nematic order parameter
- Dimers of (1, 1.5, 2) ellipsoids at different interaction cutoffs and rotations, with computed Gay-Berne type energies
- Crystalline configurations of planar benzene molecules, with energetics computed  using QuantumEspresso v7.046 using Perdew–Burke–Ernzerhof (PBE) pseudopotentials and cutoff parameters reported by Prandini et al.,  Grimme D3-dispersion correction, and a 3 × 3 Monkhorst–Pack k-point grid.

benzene

display_name: benzene

liquid-crystals

display_name: liquid-crystals

machine-learning

display_name: machine-learning

methods

display_name: methods

quantum-espresso

display_name: quantum-espresso


en:
Seven imidazolium-based ionic liquids (ILs) with different anions and cations were investigated as catholytes for the CO₂ electrocatalytic reduction to CO over silver. A significant activity and stability, but different selectivities for CO₂ reduction or the side H₂ evolution were observed. Density functional theory results show that the role of the IL anions is to tune the ratio between the CO₂ captured and electrochemically converted. Acetate anions (being strong Lewis bases) are more prone to CO₂ capture, enhancing H₂ evolution, while fluorinated anions (being weaker Lewis bases) favour the CO₂ electroreduction. Differently from the hydrolytically unstable 1-butyl-3-methylimidazolium tetrafluoroborate, 1-Butyl-3-Methylimidazolium Triflate was the most promising IL showing the highest Faradaic efficiency to CO (>95%), up to 8h of stable operation at high current rates (-20mA & -60 mA).
These results open the way for a strategic selection of the most suitable IL for the CO₂ electroreduction and its future process scale-up.

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: Understanding imidazolium-based ionic liquids role in the CO₂ electroreduction reaction: experimental and theoretical study

dft

display_name: dft

experimental

display_name: experimental

free-energy

display_name: free-energy


en:
In this work we study isomers of several representative small clusters to find principles for their stability. Our conclusions about the principles underlying the structure of clusters are based on a huge database of 44'000 isomers generated for 59 different clusters on the density functional theory level by Minima Hopping. We explore the potential energy surface of small neutral, anionic and cationic isomers, moving left to right across the third period of the periodic table and varying the number of atoms n and the cluster charge state q (X^q_n, with X={Na, Mg, Al, Si, Ge}, q=-1,0,1,2). We use structural descriptors such as bond lengths and atomic coordination numbers, the surface to volume ratios and the shape factor as well as electronic descriptors such as shell filling and hardness to detect correlations with the stability of clusters. The isomers of metallic clusters are found to be structure seekers with a strong tendency to adopt compact  shapes. However certain numbers of atoms can suppress the formation of a nearly spherical metallic clusters. Small non-metallic clusters typically also do not adopt compact spherical shapes for their lowest energy structures. In both cases spherical jellium models are not any more applicable. Nevertheless for many  structures, that have frequently a high degree of symmetry, the Kohn-Sham eigenvalues are bunched into shells and if the available electrons can completely fill such shells, a particularly stable structure can result. We call such a cluster whose shape gives rise to shells that can be completely filled by the number of available electrons an optimally matched cluster, since both the structure and the number of electrons must be special and match. In this way we can also explain the stability trends for covalent silicon and germanium cluster isomers, whose stability was previously explained by the presence of certain structural motifs. Thus we propose a unified framework to explain trends in the stability of isomers and to predict their structure for a wide range of small clusters.

This data record contains structure coordinates (compressed file all_minima_xyz_structures.tar.gz) of all minima found by the minima hopping method for all neutral and charged clusters, that are 43606 different structures. The python dictionary all_minima_names.npy stores all minima names. The python dictionary all_minima_data.npy stores all structural and electronic properties calculated for all minima. The jupyter notebook all_minima_data_analysis.ipynb helps the reading of the all_minima_names.npy and all_minima_data.npy python dictionaries.

materialscloud

en: Materials Cloud

en: Principles of isomer stability in small clusters

atomic-clusters

display_name: atomic-clusters

cscs

display_name: cscs

snsf

display_name: snsf


en:
We present SIESTA-BdG, an implementation of the simultaneous solution of the Bogoliubov-de Gennes (BdG) and Density Functional Theory (DFT) problem in SIESTA, a first-principles method and code for material simulations which uses pseudopotentials and a localized basis set. This unified approach describes both conventional and unconventional superconducting states, and enables a description of inhomogeneous superconductors and heterostructures. We demonstrate the validity, accuracy, and efficiency of SIESTA-BdG by computing physically relevant quantities (superconducting charge density, band structure, superconducting gap features, density of states) for conventional singlet (Nb, Pb) and unconventional (FeSe) superconductors. We find excellent agreement with experiments and results obtained within the KKR-BdG computational framework. SIESTA-BdG forms the basis for modelling quantum transport in superconducting devices and including - in an approximate fashion - the superconducting DFT potential of Oliveira, Gross, and Kohn.

This dataset contains the calculations performed on bulk Nb and the associated notebooks used to generate figures from this data.

en: Density functional Bogoliubov-de Gennes theory for superconductors implemented in the SIESTA code

envi

en: Environment

aiida

display_name: aiida

aiida-siesta

display_name: aiida-siesta

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

density-functional-theory

display_name: density-functional-theory

horizon-europe

display_name: horizon-europe

marie-curie-fellowship

display_name: marie-curie-fellowship

max

display_name: max

nb

display_name: nb

prace

display_name: prace

siesta

display_name: siesta

superconductivity

display_name: superconductivity


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: Phenalenyl (C₁₃H₉) is an open-shell spin-1/2 nanographene. Using scanning tunneling microscopy (STM) inelastic electron tunneling spectroscopy (IETS), covalently-bonded phenalenyl dimers have been shown to feature conductance steps associated with singlet-triplet excitations of a spin-1/2 dimer with antiferromagnetic exchange. In a recent work, we address the possibility of tuning the magnitude of the exchange interactions by varying the dihedral angle between the two molecules within a dimer. Theoretical methods, ranging from density functional theory calculations to many-body model Hamiltonians solved within different levels of approximation, are used to explain STM-IETS measurements of twisted phenalenyl dimers on a h-BN/Rh(111) surface. By means of first-principles calculations, we also propose strategies to induce sizable twist angles in surface-adsorbed phenalenyl dimers via functional groups, including a photoswitchable scheme. This record contains data that support the results discussed in the manuscript.

en: Conformational tuning of magnetic interactions in coupled nanographenes

dmrg

display_name: dmrg

magnetic-nanographenes

display_name: magnetic-nanographenes

snsf

display_name: snsf

stmsts

display_name: stmsts


en:
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost, and can also be used to deduce useful structure--property relations as they associate simple atomic motifs with complicated macroscopic properties. However, even though there exist practical justifications for these decompositions, only the global quantity is rigorously defined, and thus it is unclear to what extent the atomistic terms predicted by the model can be trusted.  Here, we introduce a quantitative metric, which we call the local prediction rigidity (LPR), to assess how robust the locally decomposed predictions of ML models are. We investigate the dependence of LPR on the details of model training, e.g. composition of the dataset, for several different problems ranging from simple toy models to real chemical systems. We present strategies to systematically enhance the LPR, which can be used to improve the robustness, interpretability, and transferability of the resulting atomistic ML models.
This repository contains datasets and Jupyter notebooks employed to corroborate the results of the above study.

en: Robustness of local predictions in atomistic machine learning models

epsrc

display_name: epsrc

erc

display_name: erc


en: This data presents the characteristics obtained using extracted parameters of multi- junction solar cells using a single diode triple-junction model. The proposed method combines analytical and numerical techniques to solve a nonlinear equation and determine the parameters, including the ideality factor, series resistance, shunt resistance, photocurrent, and saturation current, using manufacturer data and mathematical equations. The results show that the suggested approach achieves satisfactory performance and provide an accurate representation of the multi-junction circuit's parameters. Overall, this study provides a valuable contribution to the field of solar cell parameter extraction and offers insights for further research in this area.

en: Characteristics data for a new parameters extraction method of InGaP/GaAs/Ge multi-junction solar cell

i-v-characteristics

display_name: i-v-characteristics

multi-junction

display_name: multi-junction

pv-parameter-extraction

display_name: pv-parameter-extraction

solar-energy

display_name: solar-energy


en: We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the probability distribution of the off-diagonal elements of a Wishart matrix, we develop a consistent and unbiased estimator for the Seebeck coefficient, whose statistical uncertainty can be arbitrarily reduced in the long-time limit. We assess the efficacy of our method by benchmarking it against extensive equilibrium molecular dynamics simulations conducted on molten CsF using empirical force fields. We then employ this procedure to calculate the Seebeck coefficient of molten NaCl, KCl and LiCl using neural network force fields trained on ab initio data over a range of pressure-temperature conditions.

en: Seebeck coefficient of ionic conductors from Bayesian regression analysis

bayesian

display_name: bayesian

ionic-conductors

display_name: ionic-conductors

seebeck

display_name: seebeck


en: The effect of hydrogen doping on the crystal structure and the electronic state in SmNiO₃ is investigated by means of density-functional theory with a combinatorial structure-generation approach. While 100% of hydrogen doping per Ni atom has been supposed to be responsible for the experimentally observed insulating phase, we found that 50% of hydrogen doping results in an outstandingly stable atomic structure showing the insulating property. The stable crystal structure shows the peculiar layered pattern of charge disproportionation of Ni²⁺ and Ni³⁺ valences together with the strong Jahn-Teller distortion that causes the eg orbital state splitting and opens the band gap.

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: Hydrogen-induced insulating state accompanied by inter-layer charge ordering in SmNiO₃

dft

display_name: dft

hydrogen-doping

display_name: hydrogen-doping


en: The phase change compound Ge₂Sb₂Te₅ (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible transition between the crystalline and amorphous phases induced by Joule heating. The crystallization kinetics of GST225 is a key functional feature for the operation of these devices. We report here on the development of a machine-learned interatomic potential for GST225 that allowed us to perform large scale molecular dynamics simulations (over 10000 atoms for over 100 ns) to uncover the details of the crystallization kinetics in a wide range of temperatures of interest for the programming of the devices. The potential is obtained by fitting with a deep neural network (NN) scheme a large quantum-mechanical database generated within Density Functional Theory. The availability of a highly efficient and yet highly accurate NN potential opens the possibility to simulate phase change materials at the length and time scales of the real devices.

materialscloud

en: Materials Cloud

en: Unraveling the crystallization kinetics of the Ge₂Sb₂Te₅ phase change compound with a machine-learned interatomic potential

crystallization

display_name: crystallization

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potential

display_name: neural-network-potential

phase-change-materials

display_name: phase-change-materials

phase-change-memories

display_name: phase-change-memories


en: Fluctuations of electric fields can change the position of a gate-defined quantum dot in a semiconductor heterostructure. In the presence of magnetic field gradient, these stochastic shifts of electron's wavefunction lead to fluctuations of electron's spin splitting. The resulting spin dephasing due to charge noise limits the coherence times of spin qubits in isotopically purified Si/SiGe quantum dots. We investigate the spin splitting noise caused by such process caused by microscopic motion of charges at the semiconductor-oxide interface. We compare effects of isotropic and planar displacement of the charges, and estimate their densities and typical displacement magnitudes that can reproduce experimentally observed spin splitting noise spectra. We predict that for defect density of 10¹⁰ cm⁻², visible correlations between noises in spin splitting and in energy of electron's ground state in the quantum dot, are expected.

en: Correlations of spin splitting and orbital fluctuations due to 1/f charge noise in the Si/SiGe Quantum Dot

envi

en: Environment

charge-noise

display_name: charge-noise

quantum-dots

display_name: quantum-dots

simulations

display_name: simulations


en: Magnetostriction drives a rhombohedral distortion in the cubic rock salt antiferromagnet MnO at the Nèel temperature TN=118 K.  As an unexpected consequence we show that this distortion acts to localize the site of an implanted muon due to the accompanying redistribution of electron density. This lifts the degeneracy between equivalent sites, resulting in a single observed muon precession frequency.  Above TN, the muon instead becomes delocalized around a network of equivalent sites.  Our first-principles simulations based on Hubbard-corrected density-functional theory and molecular dynamics are consistent with our experimental data and help to resolve a long-standing puzzle regarding muon data on MnO, as well as having wider applicability to other magnetic oxides.

en: Magnetostriction-driven muon localisation in an antiferromagnetic oxide

dftuv

display_name: dftuv

experimental

display_name: experimental

marvel

display_name: marvel


en: We present an ab initio method to calculate the clamped Pockels tensor of ferroelectric materials from density-functional theory (DFT), the modern theory of polarization exploiting the electric-enthalpy functional, and automated first- and second-order finite-difference derivatives of the polarizations and the Hellmann-Feynman forces. Thanks to the functional-independent capabilities of our approach, we can determine the Pockels tensor of tetragonal barium titanate (BTO) beyond the local density approximation (LDA), with arbitrary exchange-correlation (XC) functionals, for example, PBEsol. The latter, together with RRKJ ultra-soft pseudo-potentials (PP) and a supercell exhibiting local titanium off-centering, enables us to stabilize the negative optical phonon modes encountered in tetragonal BTO when LDA and norm-conserving PP are combined. As a result, the correct value range of r51, the largest experimental Pockels coefficient of BTO, is recovered. We also reveal that r51 increases with decreasing titanium off-centering for this material. The lessons learned from the structural, dielectric, and vibrational investigations of BTO will be essential to design next-generation electro-optical modulators based on the Pockels effect.

en: Ab initio functional-independent calculations of the clamped Pockels tensor of tetragonal barium titanate

ab-initio

display_name: ab-initio

aiida-vibroscopy

display_name: aiida-vibroscopy

barium-titanate

display_name: barium-titanate

density-functional-theory

display_name: density-functional-theory

electro-optics

display_name: electro-optics

imaginary-phonons

display_name: imaginary-phonons

pockels

display_name: pockels


en: Summing all Feynman diagrams with quantitative accuracy is a holy grail in theoretical physics. In condensed matter, the lattice vibration (phonon) field couples with the electrons, leading to the formation of entangled electron-phonon (e-ph) states called polarons.  In the intermediate- and strong-coupling regimes common to many conventional and quantum materials, a many-body treatment of polarons requires adding up a large number of e-ph diagrams. Diagrammatic Monte Carlo (DMC) is an efficient method for diagram summation and has been employed to study polarons within simplified e-ph models (Holstein, Frohlich, etc.).  Here we show DMC calculations based on accurate first-principles e-ph interactions, enabling numerically exact results for ground-state and dynamical properties of polarons in real materials, including the polaron formation energy, effective mass, spectral weight, phonon cloud distribution, optical conductivity, and mobility. We demonstrate such DMC calculations in systems with polarons ranging from small (localized) to large (delocalized), including LiF, SrTiO3, and TiO2 rutile and anatase. This advance is enabled by our recently developed technique for compressing first-principles e-ph interaction matrices, together with a matrix-product formalism that mitigates the DMC sign problem from multiple electronic bands. Our work enables precise modeling of e-ph interactions and polarons in coupling regimes ranging from weak to strong, opening doors to studies of transport, linear response, and superconductivity in the strong e-ph coupling regime.

en: Dataset for first-principles diagrammatic Monte Carlo for electron-phonon interactions and polaron

diagrammatic-monte-carlo

display_name: diagrammatic-monte-carlo

first-principles

display_name: first-principles

lif

display_name: lif

many-body-physics

display_name: many-body-physics

polaron

display_name: polaron

strong-e-ph-interactions

display_name: strong-e-ph-interactions


en: Calcium carbonate (CaCO₃) plays a crucial role in the global carbon cycle, and its phase diagram is of significant scientific interest. We used molecular dynamics to investigate selected structural phase transitions of calcium carbonate. Using the Raiteri potential, we explored the structural transitions occurring at the constant pressure of 1 bar, with temperatures ranging from 300 to 2500 K, and at the constant temperature of 1600 K, with pressures ranging from 0 to 13 GPa. With increasing temperature, the transitions between calcite, CaCO₃-IV, and CaCO₃-V were characterized. In the calcite structure, the carbonate ions are ordered in a planar triangular arrangement, alternating with layers of calcium ions. As the temperature increases, the transition from calcite to CaCO₃-IV occurs, leading to partial disordering of the carbonate ions. At higher temperatures, CaCO₃-IV transforms into CaCO₃-V. Through free energy analysis, we classified the latter transition as a continuous phase transition. At a temperature of 2000 K, a 'disordered CaCO₃' structure appears, characterized by low order within the calcium and carbonate sublattices and the free rotation of the carbonate ions. With increasing pressure, two calcium carbonate transformations were observed. At P = 2 GPa, the CaCO₃-V phase undergoes a phase transition into CaCO₃-IV, demonstrating that the model can describe the transition between these two phases as pressure- and temperature-driven. At P = 4.25 GPa, CaCO₃-IV undergoes a phase transition into the CaCO₃-Vb phase. This transition is classified as first-order based on free energy calculations.

en: Structural transitions of calcium carbonate by molecular dynamics simulation

calcium-carbonate

display_name: calcium-carbonate

high-pressure

display_name: high-pressure

high-temperature

display_name: high-temperature

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics

phase-transitions

display_name: phase-transitions


en: High-performance n-type organic electrochemical transistors (OECTs) are essential for logic circuits and sensors. However, the performances of n-type OECTs lag far behind that of p-type ones. Conventional wisdom posits that the LUMO energy level dictates the n-type performance. Herein, we show that engineering the doped state is more critical for n-type OECT polymers. By balancing more charges to the donor moiety, we could effectively switch a p-type polymer to high-performance n-type material. Based on this concept, the polymer, P(gTDPP2FT), exhibits a record high n-type OECT performance with μC* of 54.8 F cm⁻¹ V⁻¹ s⁻¹, mobility of 0.35 cm² V⁻¹ s⁻¹, and response speed of τon/τoff = 1.75/0.15 ms. Calculations and comparison studies show that the conversion is primarily due to the more uniform charges, stabilized negative polaron, enhanced conformation, and backbone planarity at negatively charged states. Our work highlights the critical role of understanding and engineering polymers' doped states.

en: Switching p-type to high-performance n-type organic electrochemical transistors via doped state engineering

doped-state-engineering

display_name: doped-state-engineering

n-type

display_name: n-type

organic-electrochemical-transistor

display_name: organic-electrochemical-transistor


en: Photocatalytic conversion of CO₂ into fuel feed stocks is a promising method for sustainable fuel production. A highly attractive class of materials, inorganic-core@metal–organic-framework heterogeneous catalysts, boasts a significant increase in catalytic performance when compared to the individual materials. However, due to the ever-expanding chemical space of inorganic-core catalysts and metal–organic frameworks (MOFs), identification of these optimal heterojunctions is difficult without appropriate computational screening. In this work, a novel high-throughput screening method of nano-hybrid photocatalysts is presented by screening 65'784 inorganic-core materials and 20'375 MOF-shells for their ability to reduce CO₂ based on their synthesizability, aqueous stability, visible light absorption, and electronic structure; the passing materials were then paired based on their electronic structure to create novel heterojunctions. The results showed 58 suitable inorganic-core materials and 204 suitable MOFs ranging from never-beforesynthesized catalysts to catalysts that have been overlooked for their photocatalytic ability. These materials lay a new foundation of photocatalysts that have passed theoretical requirements and can significantly increase the rate of catalyst discovery.

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: High-throughput screening of nano-hybrid metal–organic-frameworks for photocatalytic CO₂ reduction

coreshell

display_name: coreshell

metal-organic-frameworks

display_name: metal-organic-frameworks

photocatalyst

display_name: photocatalyst


en: The deposited structure models of DNA adsorbed on prisitne PG/p-Si and metal (Au/W) doped PG/p-Si sheets, have been subjected to first principles calculations  based on DFT (PBE+vdW). The calculated binding affinity on PG and p-Si surface by M062X/6-31G* level of theory and adsorption energies by DFT predicts that PG has higher sensitivity towards DNA nucleic bases compared to p-Si with evident changes in their band structure properties.

materialscloud

en: Materials Cloud

en: 2D pentagonal-graphene and pentagonal-silicene sheets engineered for the detection of DNA nucleobases for genetic biomarker: A DFT study

dft

display_name: dft

electronic-structure

display_name: electronic-structure


en:
Using high-throughput automation of ab-initio impurity-embedding simulations we created a database of 3d and 4d transition metal defects embedded into the prototypical topological insulators (TIs) Bi₂Te₃ and Bi₂Se₃. We simulate both single impurities as well as impurity dimers at different impurity-impurity distances inside the topological insulator matrix. We extract changes to magnetic moments, analyze the polarizability of non-magnetic impurity atoms via nearby magnetic impurity atoms and calculate the exchange coupling constants for a Heisenberg Hamiltonian.
We uncover chemical trends in the exchange coupling constants and discuss the impurities' potential with respect to magnetic order in the fields of quantum anomalous Hall insulators. In particular, we predict that co-doping of different magnetic dopants is a viable strategy to engineer the magnetic ground state in magnetic TIs.

en: High-throughput magnetic co-doping and design of exchange interactions in topological insulators

envi

en: Environment

co-doping

display_name: co-doping

jukkr

display_name: jukkr

magnetic-doping

display_name: magnetic-doping

quantum-anomalous-hall

display_name: quantum-anomalous-hall

topological-insulator

display_name: topological-insulator

topological-materials

display_name: topological-materials


en: We present a comprehensive vibrational analysis of hydrogen boride sheet through first-principles calculations, focusing on a variety of local structural configurations including surfaces, edges, and bilayer models - some of which contains vacancy and substitutional defects. A database of vibrational modes and simulated infrared spectra was constructed, revealing distinct spectral features that serve as characteristic fingerprints for different bonding environments. By analyzing mode distributions with respect to coordination environments, we identify the distinct contributions of terminal and bridging hydrogen atoms to specific spectral regions. The results show that structural modifications, such as interlayer stacking and edge termination, have a significant influence on vibrational characteristics, particularly in the intermediate and high frequency regions. The vibrational database enables the precise interpretation of infrared spectra and offers a reliable reference for model selection and peak analysis in both experimental and theoretical studies on hydrogen boride sheet and related materials. This archive contains the database compiled from this study, comprising (i) optimized structure files, (ii) energy and force datasets used to construct the Hessian matrix and (iii) the results from the vibrational analytics, such as simulated IR spectra and mode visualizations. The database was designed for maximum accessibility; consequently, all data are supplied in multiple standard formats.

en: Dataset for vibrational recognition of local structures in hydrogen boride sheets

density-functional-theory

display_name: density-functional-theory

hydrogen-boride-sheets

display_name: hydrogen-boride-sheets

spectroscopy

display_name: spectroscopy

two-dimensional

display_name: two-dimensional


en:
Our study reveals how Gd adatoms and dimers on a superconducting Nb(110) surface induce Yu-Shiba-Rusinov (YSR) states, offering valuable insights into magnetic interactions of rare-earth atoms on superconducting surfaces. By engineering Gd dimers along the [1-10] and [001] directions, we uncover an indirect coupling between the Gd magnetic moments and the Nb substrate via their valence d electrons, leading to significant alterations in the YSR spectrum around the dimers. We further demonstrate the possibility for Néel-type spin-spiral ground states in chains of Gd atoms on Nb(110). These findings highlight the potential of 4f elements like Gd as a promising platform for controlling a spin-spiral ground state, a crucial prerequisite for realizing a topological superconductor that can host Majorana zero modes. The combination of theoretical modeling based on density functional theory, atomistic spin-dynamics simulations and experimental techniques, including scanning tunneling microscopy and spectroscopy, provides a comprehensive understanding of the coupling mechanisms and their impact on the electronic properties of these systems and establishes rare-earth magnets on Nb as a promising platform in the field.

This dataset collects the experimental and theoretical results of this work.

en: Rare-earth atoms on Nb(110) as a platform to engineer topological superconductivity

adatoms

display_name: adatoms

dftu

display_name: dftu

magnetism

display_name: magnetism

majorana-zero-mode

display_name: majorana-zero-mode

rare-earth

display_name: rare-earth

spin-dynamics

display_name: spin-dynamics

spin-spiral

display_name: spin-spiral

stm

display_name: stm

superconductivity

display_name: superconductivity

topological-superconductor

display_name: topological-superconductor


en:
Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time-step integration. These NNPs serve to explore a puzzling solute--solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in quantitative agreement with experiment. These barriers are associated with an ensemble of transition states involving direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example  underscores the critical role dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions.

The files used in our studies are listed below, ensuring reproducibility and providing resources for future studies related to this work.

en: Capturing dichotomic solvent behavior in solute–solvent reactions with neural network potentials

erc

display_name: erc

free-energy

display_name: free-energy

machine-learning

display_name: machine-learning

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


en: Comprehending the structure and dynamics of water is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While reported works show that the ab-initio molecular dynamics (AIMD) can accu- rately portray water's structure, the artificial high temperature (AHT) from 120K to 30K is needed to mimic the quantum nature of hydrogen-bond network from GGA, metaGGA to hybrid functionals. The AHT proves to be an inadequate approach for systems involving aqueous multiphase mixtures, such as water-solid interfaces and aque- ous solutions. This is due to the activation of additional phonons in other phases, which can lead to an overestimation of the dynamics for nearby water molecules. In this work, we find the regularized SCAN (rSCAN) functional can well capture both the structure and dynamics of liquid water at ambient conditions without AHT. Moreover, rSCAN can well match the experimental results of hydration structures for alkali, alkali earth and halide ions. We anticipate that the versatile and accurate rSCAN functional will emerge as a key tool based on ab-initio simulation for investigating chemical processes in aqueous environments.

en: Ab-initio simulation of liquid water without artificial high temperature

aimd

display_name: aimd

density-functional-theory

display_name: density-functional-theory

machine-learning-potential

display_name: machine-learning-potential

metagga

display_name: metagga

water

display_name: water


en: Electronic band structures is a cornerstone of condensed matter physics and materials science. Conventional methods like Wannier interpolation (WI), which are commonly used to interpolate band structures onto dense k-point grids, often encounter difficulties with complex systems, such as those involving entangled bands or topological obstructions. In this work, we introduce the Hamiltonian transformation (HT) method, a novel framework that directly enhances interpolation accuracy by localizing the Hamiltonian. Using a pre-optimized transformation, HT produces a far more localized Hamiltonian than WI, achieving up to two orders of magnitude greater accuracy for entangled bands. Although HT utilizes a slightly larger, nonlocal numerical basis set, its construction is rapid and requires no optimization, resulting in significant computational speedups. These features make HT a more precise, efficient, and robust alternative to WI for band structure interpolation, as further verified by high-throughput calculations.

en: Hamiltonian transformation for accurate and efficient band structure interpolation

band-structure-interpolation

display_name: band-structure-interpolation

hamiltonian-transformation

display_name: hamiltonian-transformation

localized-hamiltonian

display_name: localized-hamiltonian


en:
Two-dimensional (2D) materials have attracted considerable attention due to their remarkable electronic, mechanical and optical properties, making them prime candidates for next-generation electronic and optoelectronic applications. Despite their widespread use in combination with substrates in practical applications, including the fabrication process and final device assembly, computational studies often neglect the effects of substrate interactions for simplicity.
In this record, we provide the results of the computational study of the stable 2D molybdenum-sulfur (Mo-S) structures on a c-cut sapphire (Al₂O₃). In particular, we provide the results of the evolutionary search in the Mo-S / Al₂O₃ (0001) system, the machine learning interatomic potential (MLIP) used for local relaxation of the systems during the evolutionary search together with its training set, post-processing data on electronic and phonon band structures of the stable 2D Mo-S structures, and the predicted stability patterns from the perspective of CVD synthesis.

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: Substrate-aware computational design of two-dimensional materials

crystal-structure-prediction

display_name: crystal-structure-prediction


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

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

marvel

display_name: marvel

parent-cuprate

display_name: parent-cuprate

phonons

display_name: phonons

polarons

display_name: polarons

spectral-function

display_name: spectral-function


en: We use symmetry analysis and density functional theory to show that changes in magnetic order at a surface with respect to magnetic order in the bulk can be generically determined by considering local magnetoelectric responses of the crystal. Specifically, analysis of the atomic-site magnetoelectric responses, or equivalently the corresponding local magnetic multipoles, can be used to predict all surface magnetic modifications arising purely from symmetry lowering via termination of the bulk magnetic order. This analysis applies even in materials with no bulk magnetoelectric response or surface magnetization. We then demonstrate our arguments for two example antiferromagnets, metallic CuMnAs and rock-salt NiO. We find that the (010) and (1-10) surfaces of CuMnAs and NiO respectively exhibit a series of antiferroically, as well as roughness-sensitive, ferroically ordered, modifications of the surface magnetic dipole moments, via canting or changes in sublattice magnitude, consistent with the bulk ordering of the magnetic multipoles. Our findings demonstrate a universal bulk-boundary correspondance allowing the general prediction of minimal possible surface and interface magnetic modifications, even in non-magnetoelectric materials. Furthermore, it paves the way for more accurate interpretations of a wide variety of surface-sensitive measurements.

en: Local magnetoelectric effects as predictors of surface magnetic order

envi

en: Environment

dft

display_name: dft

magnetism

display_name: magnetism

surface-science

display_name: surface-science

symmetry-analysis

display_name: symmetry-analysis


en:
Phase change materials are the most promising candidates for the realization of artificial synapsis for neuromorphic computing. Different resistance levels corresponding to analogic values of the
synapsis conductance can be achieved by modulating the size of an amorphous region embedded in its crystalline matrix. Recently, it has been proposed that a superlattice made of alternating layers of the phase change compound Sb₂Te₃ and of the TiTe₂ confining material allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. In this work, we consider to substitute Sb₂Te₃ with the Ge₂Sb₂Te₅ prototypical phase change compound that should feature better data retention. By exploiting molecular dynamics simulations with a machine learning interatomic potential, we have investigated the crystallization kinetics of Ge₂Sb₂Te₅ nanoconfined in geometries mimicking Ge₂Sb₂Te₅/TiTe₂ superlattices. It turns out that nanoconfinement induces a slight reduction in the crystal growth velocities with respect to the bulk, but also an enhancement of the nucleation rate due to heterogeneous nucleation. The results support the idea of investigating Ge₂Sb₂Te₅/TiTe₂ superlattices for applications in neuromorphic devices with improved data retention. The effect on the crystallization kinetics of the addition of van der Waals interaction to the interatomic potential is also discussed.

en: Simulation of the crystallization process of Ge₂Sb₂Te₅ nanoconfined in superlattice geometries for phase change memories

crystallization

display_name: crystallization

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potential

display_name: neural-network-potential

neuromophic-computing

display_name: neuromophic-computing

phase-change-materials

display_name: phase-change-materials


en: Promoting hydrogen (H) insertion into and extraction from metal oxides plays a crucial role in tuning their properties. In our recent work, we found that the oxygen vacancy ordering in Sr(Fe,Co)Oy (SFCO) thin films can be stabilized by increasing the Co content and revealed that this stabilization is key to promoting the H insertion into SFCO oxides. This archive provides the computational inputs and crystal structures to elucidate the energies of oxygen reduction and hydrogen insertion reactions for SFCO perovskite oxides.

en: Stabilization of oxygen vacancy ordering and electrochemical-proton-insertion-and- extraction-induced large resistance modulation in strontium iron cobalt oxides Sr(Fe,Co)Oy

dftu

display_name: dftu

hydrogen

display_name: hydrogen

thermodynamics

display_name: thermodynamics