en: Understanding the structure of glassy materials represents a tremendous challenge for both experiments and computations. Despite decades of scientific research, for instance, the structural origin of the density anomaly in silica glasses is still not well understood. Atomistic simulations based on molecular dynamics (MD) produce atomically resolved structure, but extracting insights about the role of disorder in the density anomaly is challenging. Here, we propose to quantify the topological differences between structural arrangements from MD trajectories using a graph-theoretical approach, such that structural differences in silica glasses that exhibit density anomaly can be captured. To balance the accuracy and speed of the MD simulations, we utilized force matching potentials to generate the silica glass structures. This approach involves casting all-atom glass configurations as networks, and subsequently applying a graph-similarity metric (D-measure). Calculated D-measure values are then taken as the topological distances between two configurations. By measuring the topological distances of configurations in silica glass simulated structures across a range of temperatures, distinct structural features could be observed at temperatures higher than the fictive temperature. In addition to quantifying structural changes in the full simulation box, we compared topological distances between local atomic environments in the glass and crystalline silica phases. Evidence from this approach suggests that more coesite-like local structures, which are less symmetric, emerge in silica glasses when density is at a minimum during the heating process.

en: Graph theory-based structural analysis on density anomaly of silica glass

force-matching-potential

display_name: force-matching-potential

graph-theory

display_name: graph-theory

molecular-dynamics

display_name: molecular-dynamics

silica-glass

display_name: silica-glass


$oid: OBSOLETE

en:
Modeling ferroelectric materials from first principles is one of the successes of density-functional theory, and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic polarization. We demonstrate the development and application of an integrated machine learning (ML) model that describes on the same footing structural, energetic and functional properties of barium titanate (BaTiO₃), a prototypical ferroelectric. The model uses ab initio calculations as reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio modeling. The ML model allows us to thoroughly probe the static and dynamical behavior of BaTiO₃ across its phase diagram, without the need to introduce a coarse-grained description of the ferroelectric transition. Furthermore, we apply the polarization model to calculate dielectric response properties of the material in a fully ab-initio manner.
This archive provides all the relevant data and input files that were used to fit the ML interatomic potential and the polarization model used in this work, along with the relevant Density-Functional Theory calculations that were used for the training set construction and the validation of the ML model. Furthermore, it provides input files and first few snapshots of all the molecular dynamics trajectories needed to investigate the thermodynamics and dielectric properties of BaTiO₃.

en: Thermodynamics and dielectric response of BaTiO₃ by data-driven modeling

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

dielectric-response

display_name: dielectric-response

epfl

display_name: epfl

ferroelectricity

display_name: ferroelectricity

machine-learning

display_name: machine-learning

marvel

display_name: marvel

max

display_name: max

samsung-institute-of-technology-sait

display_name: samsung-institute-of-technology-sait

snsf

display_name: snsf

swissuniversities

display_name: swissuniversities

thermodynamics

display_name: thermodynamics


en: Metal halide perovskites (MHPs) demonstrate an exceptional combination of properties. Rapid progress has extended their application beyond solar cells, light-emitting diodes, photodetectors, and lasers to include memristors, artificial synapse devices, and pressure induced emission. In particular, the vacancy-ordered double perovskite Cs₂TiBr₆ has been identified as a promising material. The effective characterization of MHPs requires accurate and efficient methods for the calculation of electronic structure. Koopmans compliant (KC) functionals are an accurate and computationally efficient alternative to many-body perturbation theory using the GW approximation but have yet only been validated on a small number of simple materials. In this work, KC functionals were applied to the more complex case of Cs₂TiBr₆ and gave a zero-temperature fundamental gap of 4.28 eV, in close agreement with the value of 4.44 eV obtained using the accurate, but more computationally expensive, evGW₀ approach. The temperature-dependent renormalization of the bandgap has also been investigated and found to be significant. Agreement with the experimental optical bandgaps of 1.76–2.0 eV would also require the inclusion of exciton binding energy.

en: Accurate and efficient computation of the fundamental bandgap of the vacancy-ordered double perovskite Cs₂TiBr₆

koopmans-functionals

display_name: koopmans-functionals

metal-halide-perovskites

display_name: metal-halide-perovskites

quasi-particle-self-consistent-gw

display_name: quasi-particle-self-consistent-gw


en: The Fe(CoB)/MgO interface is vital to spintronics as it exhibits the tunneling magnetoresistance (TMR) effect and interfacial perpendicular magnetic anisotropy (PMA) simultaneously. To further enhance TMR and PMA for the development of high-density magnetoresistive random access memory, it is essential to clarify the behavior of Fe atoms interfaced with MgO. This study reveals that the spin and orbital magnetic moments of Fe are enhanced at the Fe/MgO interface. The enhancement in the orbital magnetic moment is much more significant than that predicted by the standard density functional theory. Theoretical calculations based on the orbital polarization correction reproduce this enhancement, the origin of which is attributed to the electron-electron correlation resulting from electron localization at the Fe/MgO interface. The present findings highlight the importance of electro--electron correlation at ferromagnet/oxide interfaces, which has often been disregarded in spintronics, and provide a new perspective in materials design.

en: Electron correlation enhances orbital polarization at a ferromagnetic metal/insulator interface

experimental

display_name: experimental

femgo

display_name: femgo

interface

display_name: interface

magnetic-anisotropy

display_name: magnetic-anisotropy

spintronics

display_name: spintronics


en: The precise introduction of nonplanar pores in the backbone of graphene nanoribbon represents a great challenge. In a recent work, we explore a synthetic strategy toward the preparation of nonplanar porous graphene nanoribbon from a predesigned dibromohexabenzotetracene monomer bearing four cove-edges. Successive thermal annealing steps of the monomers indicate that the dehalogenative aryl-aryl homocoupling yields a twisted polymer precursor on a gold surface and the subsequent cyclodehydrogenation leads to a defective porous graphene nanoribbon containing nonplanar [14]annulene pores and five-membered rings as characterized by scanning tunneling microscopy and noncontact atomic force microscopy. Although the C–C bonds producing [14]annulene pores are not achieved with high yield, our results provide new synthetic perspectives for the on-surface growth of nonplanar porous graphene nanoribbons. The record contains data to support the results of our work

en: On-surface synthesis of porous graphene nanoribbons containing nonplanar [14]annulene pores

dft

display_name: dft

graphene-nanoribbons

display_name: graphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis

stm

display_name: stm


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

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

cscs

display_name: cscs

defects

display_name: defects

electronic-transport

display_name: electronic-transport

gnr

display_name: gnr

magnetism

display_name: magnetism

marvel

display_name: marvel

onr

display_name: onr


en: We present an ab initio modeling framework to simulate Majorana transport in 2D semiconducting materials, paving the way for topological qubits based on 2D nanoribbons. By combining density-functional-theory and quantum transport calculations, we show that the signature of Majorana bound states (MBSs) can be found in 2D material systems as zero-energy modes with peaks in the local density-of-states. The influence of spin-orbit coupling and external magnetic fields on Majorana transport is studied for two relevant 2D materials, WSe2 and PbI2. To illustrate the capabilities of the proposed ab initio platform, a device structure capable of hosting MBSs is created from a PbI2 nanoribbon and carefully investigated. These results are compared to InSb nanowires and used to provide design guidelines for 2D topological qubits.

en: Ab initio modeling framework for Majorana transport in 2D materials: towards topological quantum computing

ab-initio

display_name: ab-initio

majorana

display_name: majorana

quantum-transport

display_name: quantum-transport

snsf

display_name: snsf


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

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


en: Electrochemical reduction of CO(2) to value-added chemicals and fuels is a promising strategy to sustain pressing renewable energy demands and address climate change issues. Direct observation of reaction intermediates during the CO(2) reduction reaction will contribute to mechanistic understandings and thus promote the design of catalysts with the desired activity, selectivity, and stability. Herein, we combined in situ electrochemical shell-isolated nanoparticle-enhanced Raman spectroscopy and ab initio molecular dynamics calculations to investigate the CORR process on Cu single-crystal surfaces in various electrolytes. Competing redox pathways and coexistent intermediates of CO adsorption dimerization, oxidation, and hydrogenation, as well as Cu-Oad/Cu-OHad species at Cu-electrolyte interfaces, were simultaneously identified using in situ spectroscopy and further confirmed with isotope-labeling experiments. With AIMD simulations, we report accurate vibrational frequency assignments of these intermediates based on the calculated vibrational density of states and reveal the corresponding species in the electrochemical CO redox landscape on Cu surfaces. Our findings provide direct insights into key intermediates during the reduction of CO(2) and offer a full-spectroscopic tool (40–4,000 cm⁻¹) for future mechanistic studies.

en: In situ spectroelectrochemical probing of CO redox landscape on copper single-crystal surfaces

aimd

display_name: aimd

electrocatalysis

display_name: electrocatalysis

in-situ-raman

display_name: in-situ-raman


en: The prediction of material properties through electronic-structure simulations based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods aiming to solve similar problems is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use any one for a given task. Leveraging recent advances in managing reproducible scientific workflows, we demonstrate how developing common interfaces for workflows that automatically compute material properties can tackle the challenge mentioned above, greatly simplifying interoperability and cross-verification. We introduce design rules for reproducible and reusable code-agnostic workflow interfaces to compute well-defined material properties, which we implement for eleven different quantum engines and use to compute three different material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer's expertise of the quantum engine directly available to non-experts. Full provenance and reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure. All workflows are made available as open-source and come pre-installed with the Quantum Mobile virtual machine, making their use straightforward. This entry contains all data and scripts to reproduce the figures of the corresponding scientific paper.

en: Common workflows for computing material properties using different quantum engines

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).

aiida

display_name: aiida

automated

display_name: automated

common-interfaces

display_name: common-interfaces

dft

display_name: dft

intersect

display_name: intersect

marvelosp

display_name: marvelosp

max

display_name: max

verification

display_name: verification

workflows

display_name: workflows


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

en: Unidirectional Kondo scattering in layered NbS2

materialscloud

en: Materials Cloud

envi

en: Environment

first-principles-calculation

display_name: first-principles-calculation

vasp

display_name: vasp


en: Charge neutrality and their expected itinerant nature makes excitons potential transmitters of information. However, exciton mobility remains inaccessible to traditional optical experiments that only create and detect excitons with negligible momentum. Here, using angle-resolved photoemission spectroscopy, we detect dispersing excitons in quasi-one-dimensional metallic trichalcogenide, TaSe₃. The low density of conduction electrons and low dimensionality in TaSe₃ combined with a polaronic renormalization of the conduction band and the poorly screened interaction between these polarons and photo-induced valence holes leads to various excitonic bound states that we interpret as intrachain and interchain excitons, and possibly trions. The thresholds for the formation of a photo-hole together with an exciton appear as side valence bands with dispersions nearly parallel to the main valence band, but shifted to lower excitation energies. The energy separation between side and main valence bands can be controlled by surface doping, enabling the tuning of certain exciton properties. This document contains the raw data of this project.

en: Multiple mobile excitons manifested as sidebands in quasi-one-dimensional metallic TaSe₃

arpes

display_name: arpes

dispersive-bands-from-excitons

display_name: dispersive-bands-from-excitons

interchain-excitons-and-trions

display_name: interchain-excitons-and-trions

mobile-excitons-in-metal

display_name: mobile-excitons-in-metal


en: The design of open-shell carbon-based nanomaterials is at the vanguard of materials science, steered by their beneficial magnetic properties like weaker spin–orbit coupling than that of transition metal atoms and larger spin delocalization, which are of potential relevance for future spintronics and quantum technologies. A key parameter in magnetic materials is the magnetic exchange coupling (MEC) between unpaired spins, which should be large enough to allow device operation at practical temperatures. In a recent work, we theoretically and experimentally explore three distinct families of nanographenes (NGs) (A, B, and C) featuring majority zigzag peripheries. Through many-body calculations, we identify a transition from a closed-shell ground state to an open-shell ground state upon an increase of the molecular size. Our predictions indicate that the largest MEC for open-shell NGs occurs in proximity to the transition between closed-shell and open-shell states. Such predictions are corroborated by the on-surface syntheses and structural, electronic, and magnetic characterizations of three NGs (A[3,5], B[4,5], and C[4,3]), which are the smallest open-shell systems in their respective chemical families and are thus located the closest to the transition boundary. Notably, two of the NGs (B[4,5] and C[4,3]) feature record values of MEC (close to 200 meV) measured on the Au(111) surface. Our strategy for maximizing the MEC provides perspectives for designing carbon nanomaterials with robust magnetic ground states. This record contains the data for the simulations discussed in our manuscript.

en: Steering large magnetic exchange coupling in nanographenes near the closed-shell to open-shell transition

nanographenes

display_name: nanographenes

open-shell

display_name: open-shell

snsf

display_name: snsf

spectroscopy

display_name: spectroscopy


en: Landau-Lifshitz-Gilbert (LLG) spin-dynamics calculations based on the extended Heisenberg Hamiltonian is an important tool in computational materials science involving magnetic materials. LLG simulations allow to bridge the gap from expensive quantum mechanical calculations with small unit cells to large supercells where the collective behavior of millions of spins can be studied. In this work we present the AiiDA-Spirit plugin that connects the spin-dynamics code Spirit to the AiiDA framework. AiiDA provides a Python interface that facilitates performing high-throughput calculations while automatically augmenting the calculations with metadata describing the data provenance between calculations in a directed acyclic graph. The AiiDA-Spirit interface thus provides an easy way for high-throughput spin-dynamics calculations. The interface to the AiiDA infrastructure furthermore has the advantage that input parameters for the extended Heisenberg model can be extracted from high-throughput first-principles calculations including a proper treatment of the data provenance that ensures reproducibility of the calculation results in accordance to the FAIR principles. We describe the layout of the AiiDA-Spirit plugin and demonstrate its capabilities using selected examples for LLG spin-dynamics and Monte Carlo calculations. Furthermore, the integration with first-principles calculations through AiiDA is demonstrated at the example of gamma-Fe, where the complex spin-spiral ground state is investigated.

en: The AiiDA-Spirit plugin for automated spin-dynamics simulations and multi-scale modelling based on first-principles calculations

density-functional-theory

display_name: density-functional-theory

landau-lifshitz-gilbert-equation

display_name: landau-lifshitz-gilbert-equation

llg

display_name: llg

monte-carlo

display_name: monte-carlo

skyrmion

display_name: skyrmion

spin-dynamics

display_name: spin-dynamics


en:
A semi-automated protocol largely based on AiiDA has been developed and exploited to explore the yet-unsolved crystal structure of the recently-discovered AsTe₃ material with potential thermoelectric applications due to its ultralow thermal conductivity. The workflow involves different steps of increasing precision and computational cost, in a sequential approach to generate, refine and evaluate model AsTe₃ structures based on supercells of elemental-Te from the stand point of As/Te chemical disorder. Our studies shed light on the structural complexity that has prevented, to this day, a direct structure determination of the recently-discovered crystalline AsTe₃ material, despite the new synthesis protocol that has led to its obtention in a pure and fully crystalline phase, by congruent crystallization from the parent AsTe₃ glass. Structural models of crystalline AsTe₃ reveal a composition based on an intergrowth of subnanometric As₂Te₃ and Te domains with potentially defected interfaces that intimately interfere with the electronic properties of this material.
This repository contains data generated in the course of the structure-construction-and-evaluation protocol for subsets of AsTe₃ structural models obtained at different steps of the workflow, with the full provenance included, in the form of an AiiDA archive. Scripts and workchains developped specifically for this study are also privided, along with the atomic structures of the most relevant AsTe₃ models identified, in common file formats for researchers not familiar with AiiDA databases.

en: Insights into the structural complexity and local disorder of crystalline AsTe₃ from semi-automated first-principles modelling

automation

display_name: automation

chemical-disorder

display_name: chemical-disorder

structural-complexity

display_name: structural-complexity

structural-models

display_name: structural-models

thermoelectrics

display_name: thermoelectrics


en: Due to their high accuracy, Double-Hybrid Density functionals emerged to important methods for molecular electronic-structure calculations. The high computational costs of double-hybrid calculations in condensed phase and the lack of efficient gradient implementations thereof inhibit a wide applicability for periodic systems. We present an implementation of gradients for Double-Hybrid functional theory into CP2K. The Auxiliary Density Matrix Method (ADMM) reduces the overhead from the Hartree-Fock calculations providing an efficient and accurate methodology to tackle condensed phase systems. First applications to water containing systems of different densities and molecular crystals pave the way for advanced studies. We present large benchmark systems to discuss the efficiency of our methodology on modern super computing hardware.

en: Double-Hybrid Density functionals for the condensed phase: gradients, stress tensor, and Auxiliary-Density Matrix Method acceleration

analytical-gradients

display_name: analytical-gradients

auxiliary-density-matrix-method

display_name: auxiliary-density-matrix-method

benchmark

display_name: benchmark

condensed-matters

display_name: condensed-matters

cscs

display_name: cscs

double-hybrid-functional

display_name: double-hybrid-functional

electronic-structure-method

display_name: electronic-structure-method

molecular-crystals

display_name: molecular-crystals


en: Singlet fission (SF) is a promising multiexciton-generating process. Its demanding energy splitting criterion - that the S1 energy must be at least twice that of T1 - has limited the range of materials capable of SF. We propose heteroatom oxidation as a robust strategy to achieve sufficient S1/T1 splitting, and demonstrate the potential of this approach for intramolecular SF.

en: Heteroatom oxidation controls singlet-triplet energy splitting in singlet fission building blocks

conjugated-polymers

display_name: conjugated-polymers

donor-acceptor-systems

display_name: donor-acceptor-systems

epfl

display_name: epfl

excited-states

display_name: excited-states

singlet-fission

display_name: singlet-fission


en: The efficient activation of methane and simultaneous water dissociation are crucial in many catalytic reactions on oxide-supported transition metal catalysts. On very low-loaded Ni/CeO₂ surfaces, methane easily fully decomposes, CH₄ -> C + 4H,  and water dissociates, H₂O -> OH + H. However, in important reactions such as the direct oxidation of methane to methanol (MTM), where complex interplay exists between reactants (CH₄, O₂), it is desirable to avoid the complete dehydrogenation of methane to carbon.  Remarkably, the barrier for the activation of C-H bonds in CHx (x= 1-3) species on Ni/CeO₂ surfaces can be manipulated by adding Cu, forming bimetallic NiCu clusters, whereas the ease for cleavage of O-H bonds in water, is not affected by ensemble effects, as obtained from density functional theory-based calculations. CH4 activation occurs only on Ni sites and H₂O activation on both Ni and Cu sites. The MTM reaction pathway for the example of the Ni₃Cu₁/CeO₂ model catalyst predict higher selectivity and a lower activation barrier for methanol production, compared with that for Ni₄-CeO₂. These findings point toward a possible strategy to design active and stable catalysts which can be employed for methane activation and conversions.

en: Tuning selectivity in the direct conversion of methane to methanol: bimetallic synergistic effects on the cleavage of C-H and O-H bonds over NiCu/CeO₂ catalysts

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).

bimetallic

display_name: bimetallic

ceria

display_name: ceria

methane-activation

display_name: methane-activation

water-activation

display_name: water-activation


en: The properties of molecules and materials containing light nuclei are affected by their quantum mechanical nature. Accurate modeling of these quantum nuclear effects requires computationally demanding path integral techniques. Considerable success has been achieved in reducing the cost of such simulations by using generalized Langevin dynamics to induce frequency-dependent fluctuations. Path integral generalized Langevin equation methods, however, have this far been limited to the study of static, thermodynamic properties due to the large perturbation to the system's dynamics induced by the aggressive thermostatting. Here, we introduce a post-processing scheme, based on analytical estimates of the dynamical perturbation induced by the generalized Langevin dynamics, which makes it possible to recover meaningful time correlation properties from a thermostatted trajectory. We show that this approach yields spectroscopic observables for model and realistic systems that have an accuracy comparable to much more demanding approximate quantum dynamics techniques based on full path integral simulations.

en: Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats

materialscloud

en: Materials Cloud

erc

display_name: erc

marvel

display_name: marvel

molecular-dynamics

display_name: molecular-dynamics

nuclear-quantum-effects

display_name: nuclear-quantum-effects

vibrational-spectra

display_name: vibrational-spectra


en:
Why are materials with specific characteristics more abundant than others?
This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space. We highlight here the anomalous abundance of  inorganic compounds whose primitive unit cell contains a number of atoms that is a multiple of four. This occurrence - named here the 'rule of four' - has to our knowledge not previously been reported or studied. Here, we first highlight the rule's existence, especially notable when restricting oneself to experimentally known compounds, and explore its possible relationship with established descriptors of  crystal structures, from symmetries to energies. We then investigate this relative abundance by looking at structural descriptors, both of global (packing configurations) and local (the smooth overlap of atomic positions) nature. Contrary to intuition, the overabundance does not correlate with low-energy or high-symmetry structures; in fact, structures which obey the 'rule of four' are characterized by low symmetries and loosely packed arrangements maximizing the free volume. We are able to correlate this abundance with local structural symmetries, and visualize the results using a hybrid supervised-unsupervised machine learning method.

en: The rule of four: anomalous stoichiometries of inorganic compounds

envi

en: Environment

classification

display_name: classification

inorganic-databases

display_name: inorganic-databases

local-symmetries

display_name: local-symmetries

machine-learning

display_name: machine-learning

soap-vectors

display_name: soap-vectors


en: We present a self-consistent method based on first-principles calculations to determine the magnetic ground state of materials, regardless of their dimensionality. Our methodology is founded on satisfying the stability conditions derived from the linear spin wave theory (LSWT) by optimizing the magnetic structure iteratively. We demonstrate the effectiveness of our method by successfully predicting the experimental magnetic structures of NiO, FePS₃, FeP, MnF₂, FeCl₂, and CuO. In each case, we compared our results with available experimental data and existing theoretical calculations reported in the literature. Finally, we discuss the validity of the method and the possible extensions.

en: Systematic determination of a material's magnetic ground state from first principles

dftu

display_name: dftu

linear-spin-wave-theory

display_name: linear-spin-wave-theory

magnetic-structure

display_name: magnetic-structure


en: Using first-principles techniques, we study the structural, magnetic, and electronic properties of (111)-oriented (LaMnO₃)₂ₙ|(SrMnO₃)ₙ superlattices of varying thickness (n=2,4,6). We find that the properties of the thinnest superlattice (n=2) are similar to the celebrated half-metallic ferromagnetic alloy La2/3Sr1/3MnO₃, with quenched Jahn-Teller distortions. At intermediate thickness (n=4), the a⁻a⁻a⁻ tilting pattern transitions to the a⁻a⁻c⁺ tilting pattern, driven by the lattice degrees of freedom in the LaMnO₃ region. The emergence of the Jahn-Teller modes and the spatial extent needed for their development play a key role in this structural transition. For the largest thickness considered (n=6), we unveil an emergent separation of Jahn-Teller and volume-breathing orders in the ground-state structure with the a⁻a⁻c⁺ tilting pattern, whereas it vanishes in the antiferromagnetic configurations. The ground state of all superlattices is half-metallic ferromagnetic, not affected by the underlying series of structural transitions. Overall, these results outline a thickness-induced crossover between the physical properties of bulk La2/3Sr1/3MnO₃ and bulk LaMnO₃. The dataset includes the fully relaxed atomic positions of the most favorable tilting patterns, as well as the main output files of the electronic structure calculations. Selected files for the density of states are also included.

en: Emergent half-metal with mixed structural order in (111)-oriented (LaMnO₃)₂ₙ|(SrMnO₃)ₙ superlattices

dft

display_name: dft

electronic-properties

display_name: electronic-properties

magnetic-properties

display_name: magnetic-properties

oxide-heterostructures

display_name: oxide-heterostructures


en: Polyelectrolyte complexes (PECs), formed via the self-assembly of oppositely charged polysaccharides, are highly valued for their biocompatibility, biodegradability, and hydrophilicity, offering significant potential for biotechnological applications. However, the complex nature and lack of insight at a molecular level into polyelectrolytes conformation and aggregation often hinders the possibility of achieving an optimal control of PEC systems, limiting their practical applications. To address this problem, an in-depth investigation of PECs microscopic structural organization is required. In this work, for the first time, a hybrid approach that combines experimental techniques with atomistic molecular dynamics simulations is used to elucidate, at a molecular level, the mechanisms underlying the aggregation and structural organization of complexes formed by gellan and chitosan, i.e. PECs commonly used in food technology. This combined analysis reveals a two-step complexation process: gellan initially self-assembles into a double-helix structure, subsequently surrounded and stabilized by chitosan via electrostatic interactions. Furthermore, these results show that complexation preserves the individual conformation and intrinsic functionality of both polyelectrolytes, thereby ensuring the efficacy of the PECs in biotechnological applications.

en: Unveiling the self-assembly process of gellan-chitosan complexes through a combination of atomistic simulations and experiments

chitosan

display_name: chitosan

gellan

display_name: gellan

gels

display_name: gels

polyelectrolyte-complexes

display_name: polyelectrolyte-complexes

polysaccharides

display_name: polysaccharides


en:
Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by weak binding energy and, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors.
To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.

en: Machine-learning accelerated identification of exfoliable two-dimensional materials

binding-energy

display_name: binding-energy

crystal-structure

display_name: crystal-structure

exfoliation

display_name: exfoliation

online-tool

display_name: online-tool

two-dimensional

display_name: two-dimensional


en: Ab initio random structure searching (AIRSS) has been performed for carbon at 10GPa, a pressure at which diamond is expected to be the thermodynamic ground state. A small (~10K structures) and larger (~100K structures) set are provided. To demonstrate a search in a ternary composition space ~100K AIRSS structures for the C+N+H+O system are provided. All structures are generated at random using the buildcell tool of the AIRSS suite, and relaxed to a nearby local minimum in the DFT-PBE total energy using the CASTEP code. Computational details are provided in the REM block of the .res structure files.

en: AIRSS data for carbon at 10GPa and the C+N+H+O system at 1GPa

ab-initio-random-structure-searching

display_name: ab-initio-random-structure-searching

carbon

display_name: carbon


en:
In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to materials science. While most current MLPs are based on environment-dependent atomic energies, the limitations of this locality approximation can be overcome, e.g., in fourth-generation MLPs, which incorporate long-range electrostatic interactions based on an equilibrated global charge distribution. Apart from the considered interactions,  the quality of MLPs crucially depends on the information available about the system, i.e., the descriptors.
In this work we show that including — in addition to structural information — the electrostatic potential arising from the charge distribution in the atomic environments significantly improves the quality and transferability of the potentials. Moreover, the extended descriptor allows to overcome current limitations of two- and three-body based feature vectors regarding artificially degenerate atomic environments.
The capabilities of such an electrostatically embedded fourth-generation high-dimensional neural network potential (ee4G-HDNNP), which is further augmented by pairwise interactions, are demonstrated for NaCl as a benchmark system. Employing a data set containing only neutral and negatively charged NaCl clusters, even small energy differences between different cluster geometries can be resolved, and the potential shows an impressive transferability to positively charged clusters as well as the melt.

en: Accurate fourth-generation machine learning potentials by electrostatic embedding

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).

electrostatics

display_name: electrostatics

machine-learning-potentials

display_name: machine-learning-potentials


en: We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on the known material Cr2Ge2Te6, using density functional theory (DFT) calculations and determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability.

en: Data-driven studies of magnetic two-dimensional materials

materialscloud

en: Materials Cloud

machine-learning

display_name: machine-learning

magnetic-materials

display_name: magnetic-materials

two-dimensional-materials

display_name: two-dimensional-materials


en: Being Wannierizable is not the end of the story for topological insulators. We introduce a family of topological insulators that would be considered trivial in the paradigm set by the tenfold way, topological quantum chemistry, and the method of symmetry-based indicators. Despite having a symmetric, exponentially localized Wannier representation, each Wannier function cannot be completely localized to a single primitive unit cell in the bulk. Such multicellular topology is shown to be neither stable nor fragile, but delicate; i.e., the topology can be nullified by adding trivial bands to either valence or conduction band.

en: Multicellularity of delicate topological insulators

envi

en: Environment

delicate-topology

display_name: delicate-topology

erc

display_name: erc

hopf-insulator

display_name: hopf-insulator

multicellular-wannier-functions

display_name: multicellular-wannier-functions

returning-thouless-pump

display_name: returning-thouless-pump

snsf

display_name: snsf

topological-insulators

display_name: topological-insulators


en: Photochemical reactions of contaminants in snow and ice can be important sources and sinks for various organic and inorganic compounds. Snow contaminants can be found in the bulk ice matrix, in internal liquid-like regions (LLRs), or in quasi-liquid layers (QLLs) at the air-ice interface, where they can readily exchange with the firn air. Some studies have reported that direct photochemical reactions occur faster in LLRs and QLLs than in aqueous solution, while others have found similar rates. Here, we measure the photodegradation rate constants of the three dimethoxybenzene isomers under varying experimental conditions, including in aqueous solution, in LLRs, and at the air-ice interface of nature-identical snow. Relative to aqueous solution, we find modest photodegradation enhancements (3- and 6-fold) in LLRs for two of the isomers, and larger enhancements (15- to 30-fold) at the air-ice interface for all three isomers. We use computational modeling to assess the impact of light absorbance changes on photodegradation rate enhancements at the interface. We find small (2–5 nm) bathochromic (red) absorbance shifts at the interface relative to in solution, which increases light absorption, but this factor only accounts for less than 50 % of the measured rate constant enhancements. The major factor responsible for photodegradation rate enhancements at the air-ice interface appears to be more efficient photodecay: estimated dimethoxybenzene quantum yields are 6- to 24-fold larger at the interface compared to in aqueous solution and account for the majority (51–96 %) of the observed enhancements. Using a hypothetical model compound with an assumed Gaussian-shaped absorbance peak, we find that a shift in the peak to higher or lower wavelengths can have a minor to substantial impact on photodecay rate constants, depending on the original location of the peak and the magnitude of the shift. Changes in other peak properties at the air-ice interface, such as peak width and height (i.e., molar absorptivity) can also impact rates of light absorption and direct photodecay.

en: Enhanced photodegradation of dimethoxybenzene isomers in/on ice compared to in aqueous solution

absorption-spectra

display_name: absorption-spectra

dimethoxybenzene

display_name: dimethoxybenzene

ice

display_name: ice


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

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

epfl

display_name: epfl

photoswitchable-organocatalysis

display_name: photoswitchable-organocatalysis

reversible-steric-shielding

display_name: reversible-steric-shielding

statistical-sampling

display_name: statistical-sampling


en: Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have been successfully synthesized or exfoliated. Here, we search for novel 2D materials that can be easily exfoliated from their parent compounds. Starting from 108423 unique, experimentally known three-dimensional compounds we identify a subset of 5619 that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van-der-Waals density-functional theory, validated against experimental structural data and calculated random-phase-approximation binding energies, allow to identify 1825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 compounds we explore vibrational, electronic, magnetic, and topological properties, identifying 56 ferromagnetic and antiferromagnetic systems, including half-metals and half-semiconductors. This archive entry contains the database of 2D materials (structural parameters, band structures, binding energies, phonons for the subset of the 258 easily exfoliable materials with less than 6 atoms, structures and binding energies for the remaining 1567 materials) together with the provenance of all data and calculations as stored by AiiDA.

en: Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

database

display_name: database

dft

display_name: dft

electronic-bands

display_name: electronic-bands

electronic-properties

display_name: electronic-properties

high-throughput

display_name: high-throughput

magnetic-properties

display_name: magnetic-properties

marvel

display_name: marvel

monolayer

display_name: monolayer

phonons

display_name: phonons

two-dimensional

display_name: two-dimensional

vibrational-properties

display_name: vibrational-properties


en: The ethanol surface reaction over CeO₂ nanooctahedra (NO) and nanocubes (NC), which mainly expose (111) and (100) surfaces, respectively, was studied by means of infrared spectroscopy (TPSR-IR), mass spectrometry (TPSR-MS) and density functional theory (DFT) calculations. TPSR-MS results show that the production of H₂ is 2.4 times higher on CeO₂ -NC than on -NO, which is rationalized starting from the different types of adsorbed ethoxy species controlled by the shape of the ceria particles. Over the CeO₂(111) surface, monodentate type I and II ethoxy species with the alkyl chain perpendicular or parallel to the surface, respectively, were identified. Whereas on the CeO₂(100) surface, bidentate and monodentate type III ethoxy species on the checkerboard O-terminated and on a pyramid of the reconstructed (100) surface, respectively, are found. The more labile surface ethoxy species on each ceria nanoshape, which are the monodentate type I or III ethoxy on CeO₂ -NO and -NC, respectively, react on the surface to give acetate species that decomposes to CO₂ and CH₄, while H₂ is formed via the recombination of hydroxyl species. In addition, the more stable monodentate type II and bidentate ethoxy species on CeO₂ -NO and -NC, respectively, give an ethylenedioxy intermediate the binding of which is facet dependent. On the (111) facet, the less strongly bound ethylenedioxy desorbs as ethylene, whereas on the (100) facet, the more strongly bound intermediate also produces CO₂ and H₂ via formate species. Thus, on the (100) facet an additional pathway towards H₂ formation is found. ESR activity measurements show an enhanced H₂ production on the nanocubes.

en: Shape-controlled pathways in the hydrogen production from ethanol steam reforming over ceria nanoparticles

ceria-nanostructures

display_name: ceria-nanostructures

ethanol-decomposition

display_name: ethanol-decomposition

ethylenedioxy-species

display_name: ethylenedioxy-species

facet-dependent-activity

display_name: facet-dependent-activity

green-hydrogen

display_name: green-hydrogen


en:
Two different heat-transport mechanisms are discussed in solids: in crystals, heat carriers propagate and scatter like particles, as described by Peierls' formulation of Boltzmann transport equation for phonon wavepackets; in glasses, instead, carriers behave wave-like, diffusing via a Zener-like tunneling between quasi-degenerate vibrational eigenstates, as described by the Allen-Feldman equation. Recently, it has been shown that these two conduction mechanisms emerge as limiting cases from a unified transport equation, which describes on an equal footing solids ranging from crystals to glasses; moreover, in materials with intermediate characteristics the two conduction mechanisms coexist, and it is crucial to account for both. Here, we discuss the theoretical foundations of such transport equation as is derived from the Wigner phase-space formulation of quantum mechanics, elucidating how the interplay between disorder, anharmonicity, and the quantum Bose-Einstein statistics of atomic vibrations determines thermal conductivity. This Wigner formulation argues for a preferential phase convention for the dynamical matrix in the reciprocal Bloch representation and related off-diagonal velocity operator's elements; such convention is the only one yielding a conductivity which is invariant with respect to the non-unique choice of the crystal's unit cell and is size-consistent. We rationalize the conditions determining the crossover from particle-like to wave-like heat conduction, showing that phonons below the Ioffe-Regel limit (i.e. with a mean free path shorter than the interatomic spacing) contribute to heat transport due to their wave-like capability to interfere and tunnel. Finally, we show that the present approach overcomes the failures of the Peierls-Boltzmann formulation for materials with ultralow or glass-like thermal conductivity, with case studies of materials for thermal barrier coatings and thermoelectric energy conversion.

Here, we release the crystal structures, interatomic force constants, and computational parameters needed to reproduce the results discussed in the article "Wigner formulation of thermal transport in solids" (arXiv preprint, 2021) by M. Simoncelli, N. Marzari, and F. Mauri.

anharmonicity

display_name: anharmonicity

thermal-conductivity

display_name: thermal-conductivity

wigner-heat-transport-equation

display_name: wigner-heat-transport-equation


en: This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

en: E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en: Topological crystalline insulators (TCIs) are insulating electronic states with nontrivial topology protected by crystalline symmetries. Recently, theory has proposed new classes of TCIs protected by rotation symmetries Ĉ_n, which have surface rotation anomaly evading the fermion doubling theorem, i.e., n instead of 2n Dirac cones on the surface preserving the rotation symmetry. Here, we report the first realization of the Ĉ_2 rotation anomaly in a binary compound SrPb. Our first-principles calculations reveal two massless Dirac fermions protected by the combination of time-reversal symmetry T̂ and Ĉ_2y on the (010) surface. Using angle-resolved photoemission spectroscopy, we identify two Dirac surface states inside the bulk band gap of SrPb, confirming the Ĉ_2 rotation anomaly in the new classes of TCIs. The findings enrich the classification of topological phases, which pave the way for exploring exotic behavior of the new classes of TCIs.

en: Discovery of Ĉ₂ rotation anomaly in topological crystalline insulator SrPb

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).

electronic-structure

display_name: electronic-structure

snsf

display_name: snsf

topological-materials

display_name: topological-materials


en:
The application of machine learning to the modeling of materials and molecules has proven to be extremely successful in accelerating the understanding, design, and characterization of materials. A major factor in this success has been the development of representations of atomic structures that reflect physics-based symmetries of the underlying interactions. Most of the descriptions of atomic properties or even global observables rely on decompositions into atomic contributions that are subsequently learnt in an atom-centered framework. However, many quantities associated with quantum mechanical calculations, such as the single-particle Hamiltonian matrices written in an atomic-orbital basis, are associated with multiple atom-centers.
Following the introduction of equivariant N-center structural descriptors, in the reference below, that generalize the very successful atom-centered density correlation features to the problem of learning properties indexed by N atoms, we present benchmarks showing how the construction can be applied to efficiently learn the matrix elements of the (effective) single-particle Hamiltonian in an atom-centered orbital basis.
In this record, we include the dataset comprising the Fock and overlap matrices in the def2-SVP of 1000 distorted water molecules, up to 4500 ethanol structures, and a subset of QM7-CHNO molecules. We also provide scripts to generate the two-center representations and fit linear and sparse kernel models for the Hamiltonians.

en: Equivariant representations for molecular Hamiltonians

materialscloud

en: Materials Cloud

epfl

display_name: epfl

equivariant-representations

display_name: equivariant-representations

erc

display_name: erc

hamiltonian-learning

display_name: hamiltonian-learning

machine-learning-hamiltonians

display_name: machine-learning-hamiltonians

marvel

display_name: marvel

n-center-representations

display_name: n-center-representations


en: In industrial metal forming processes, the generation of datasets for inline and optical quality assessment is expensive and time-consuming. Within the research project SimKI, conventional metal forming plants were digitalized under use of perception-based sensors in combination with a completely redesigned forming tool. The integration of optical quality observation methods connected with a retrofitting approach of the press tool provides the opportunity to generate an information-feedback loop that predicts part defects prior to their occurrence. The SimKI-method additionally combines conventional statistical measurement methods with AI-based defect detection algorithms that are trained by a) generic datasets of a finite-element simulation, b) real component images of a 3D imaging device, and c) a combination of both. The generated datasets are used to accelerate the training of a DNN-based algorithm in order to identify the position and deviation from the agreed quality. The high degree of innovation is based on obtaining real-time component quality information under use of AI-based optical quality assessment, which in turn provides information to the control algorithm of the smart forming tool.

en: Recursive quality optimization of a smart forming tool under use of perception based hybrid datasets for training of a deep neural network

envi

en: Environment

artificial-intelligence

display_name: artificial-intelligence

defect-detection

display_name: defect-detection

gocator

display_name: gocator


en: High Entropy Alloys (HEAs) are a new broad class of near-random solid solution alloys that can possess some impressive mechanical and physical properties including high stability against grain growth (i.e. low grain boundary (GB) mobility). Here, it is shown that an initially flat GB in an HEA can become spontaneously rough, driven by natural local compositional fluctuations. Roughening lowers the total GB energy and thus can inhibit migration. A parameter-free theoretical framework is developed to demonstrate the energetics and size scales of the roughening in terms of solute/GB interaction energies and GB disconnection energies. Above a critical level of solute/GB interactions, a planar GB is predicted to roughen down to the scale of the GB periodic unit. A similar theory for 1D GBs (minimum periodic length in one direction) is also developed since such geometries are common in atomistic simulations. Specific predictions are made for the symmetric tilt boundaries Σ17 [100] (530) and Σ5 [100] (310) in a model CoCuFeNi alloy and atomistic simulations demonstrate roughening consistent with the theory. Analysis of the stresses needed to drive migration shows how migration can be inhibited or enhanced, rationalizing variations in mobility of GBs in HEAs.

en: Theory of spontaneous grain boundary roughening in high entropy alloys

grain-boundaries

display_name: grain-boundaries

high-entropy-alloys

display_name: high-entropy-alloys

pinning

display_name: pinning

spontaneous-roughening

display_name: spontaneous-roughening


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

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

ab-initio

display_name: ab-initio

catalysis

display_name: catalysis

on-surface-synthesis

display_name: on-surface-synthesis


en: The screening arising from many-body excitations is a crucial quantity for describing ab-sorption and inelastic X-ray scattering (IXS) of materials. Similarly, the electron screening plays a critical role in state-of-the-art approaches for determining the fundamental band gap. However, ab initio studies of the screening in liquid water have remained limited. Here, we use a combined analysis based on the Bethe-Salpeter equation and time-dependent density functional theory. We first show that absorption spectra at near-edge energies are insufficient to assess the accuracy by which the screening is described. Next, when the energy range under scrutiny is extended, we instead find that the IXS spectra are highly sensitive and allow for the selection of the optimal theoretical scheme. This leads to good agreement with experiment over a large range of transferred energies and momenta, and enables establishing the elusive fundamental band gap of liquid water at 9.3 eV.

en: Many-body screening effects in liquid water

dft

display_name: dft

epfl

display_name: epfl

mbpt

display_name: mbpt

snsf

display_name: snsf

water

display_name: water


en: The facet-dependent adsorption of CO on oxidized and reduced CeO₂ single crystal surfaces is reviewed, with emphasis on the effect of CO coverage and the ability of state-of-the-art quantum-mechanical methods to provide reliable energies and an accurate description of the IR vibrational frequency of CO. Comparison with detailed, high-resolution experimental IRRAS data performed on single crystal samples allows the assignment of the different CO vibrational bands observed on all three low-index ceria surfaces. Good agreement is achieved with the hybrid DFT approach with the HSE06 functional and with saturation coverage. It is shown that CO is very sensitive to the structure of cerium oxide surfaces and to the presence of oxygen vacancies. The combined theoretical-experimental approach offers new opportunities for a better characterization of ceria nanoparticles and for unraveling changes occurring during reactions involving CO at higher pressures.

en: Vibrational frequencies of CO bound to all three low-index cerium oxide surfaces: a consistent theoretical description of vacancy-induced changes using density functional theory

co-vibrational-frequency

display_name: co-vibrational-frequency

oxidized-and-reduced-ceria

display_name: oxidized-and-reduced-ceria


en: Photochemical reactions on semiconductors are anisotropic, since they occur with different rates on surfaces of different orientation. Understanding the origin of this anisotropy is crucial to engineering more efficient photocatalysts. In this work, we use hybrid density functional theory (DFT) to identify the surfaces associated with the largest number of photo-generated carriers in different semiconductors. For each material we create a spherical heat map of the probability of optical transitions at different wave vectors. These maps allow to identify the directions associated with the majority of the photo-generated carriers and can thus be used to make predictions about the most reactive surfaces for photochemical applications. Results indicate that it is generally possible to correlate the heat maps with the anisotropy of the bands observed in conventional band-structure plots, as previously suggested. However, we also demonstrate that conventional bands-structure plots do not always provide all the informations and that taking into account the contribution of all possible transitions weighted by their transition dipole moments is crucial to obtain a complete picture.

en: Photochemical anisotropy and direction-dependent optical absorption properties in semiconductors

anisotropy

display_name: anisotropy

cscs

display_name: cscs

hybrid-functionals

display_name: hybrid-functionals

optical-properties

display_name: optical-properties

photocatalysis

display_name: photocatalysis

photochemistry

display_name: photochemistry

semiconductors

display_name: semiconductors


en:
Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic conductors and largely overestimate their thermal conductivity. In this work, we deploy a combination of state-of-the-art methods to calculate the thermal conductivity of a prototypical Li-ion conductor, the Li₃ClO antiperovskite. By leveraging ab initio, machine learning, and forcefield descriptions of interatomic forces, we are able to reveal the massive role of anharmonic interactions and diffusive defects on the thermal conductivity and its temperature dependence, and to eventually embed their effects into a simple rationale which is likely applicable to a wide class of ionic conductors.
In this record, we provide data and scripts to generate the plots supporting our findings. We also provide the machine learning model and the dataset to train it.

en: Temperature- and vacancy-concentration-dependence of heat transport in Li₃ClO from multi-method numerical simulations

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).

batteries

display_name: batteries

heat-transport

display_name: heat-transport

max

display_name: max

solid-state-electrolytes

display_name: solid-state-electrolytes

superionics

display_name: superionics


en: Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work, we present the simplex method that can perform the inverse operation, i.e., calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large dataset of fingerprints, we can find a particular largest simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can be used in a fully automatic way to analyze structures. In this way, we can, for instance, detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting fingerprints on the largest simplex, we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced.

en: Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression

materialscloud

en: Materials Cloud

atomic-environment-selection

display_name: atomic-environment-selection

atomic-fingerprints

display_name: atomic-fingerprints

classification-of-atomic-environments

display_name: classification-of-atomic-environments

dimensionality-reduction

display_name: dimensionality-reduction

largest-volume-simplex

display_name: largest-volume-simplex

machine-learning

display_name: machine-learning

overlap-matrix-fingerprint

display_name: overlap-matrix-fingerprint

quantum-non-local-effects

display_name: quantum-non-local-effects


en:
The uploaded archive provides a ML-ready data set extracted from the juHemd database (see references) augmented with supplemental data for atomic descriptors. Descriptors provided in this data set include structural, magnetic, atomic quantities as well as derived (summed) quantities. In total, 118 possible descriptors are included of which 12 are DFT generated. For each simulation type (LDA/GGA) there is also a data set cleaned from DFT data available.

After data cleaning and preprocessing we extracted 387 LDA calculated magnetic Heusler structures as well as 408 GGA structures which have a full structural and magnetic data set. As we only aim at magnetic compounds, we chose to filter out compounds from the original JuHemd which have at least 0.1 Bohr magneton as total absolute magnetic moment. For each data file there is an existing descriptor file naming all the descriptors included in the data set.

en: ML-ready Curie temperatures and descriptors extracted from the JuHemd database

envi

en: Environment

critical-temperature

display_name: critical-temperature

curie-temperature

display_name: curie-temperature

density-functional-theory

display_name: density-functional-theory

electronic-structure

display_name: electronic-structure

heusler-alloy

display_name: heusler-alloy

judft

display_name: judft

kkr

display_name: kkr

magnetic

display_name: magnetic

magnetic-phase-transition

display_name: magnetic-phase-transition

magnetic-structure

display_name: magnetic-structure

monte-carlo

display_name: monte-carlo


en: We demonstrate the importance of addressing the 𝚪 vertex and thus going beyond the GW approximation for achieving the energy levels of liquid water in many- body perturbation theory. In particular, we consider an effective vertex function in both the polarizability and the self-energy, which does not produce any computational overhead compared with the GW approximation. We yield the band gap, the ionization potential, and the electron affinity in good agreement with experiment and with a hybrid functional description. The achieved electronic structure and dielectric screening further lead to a good description of the optical absorption spectrum, as obtained through the solution of the Bethe–Salpeter equation. In particular, the experimental peak position of the exciton is accurately reproduced.

en: Absolute energy levels of liquid water from many-body perturbation theory with effective vertex corrections

gw

display_name: gw

ionization-potential

display_name: ionization-potential

many-body-perturbation-theory

display_name: many-body-perturbation-theory

vertex

display_name: vertex

water

display_name: water


en: Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often non-transferable and cannot be applied across different reactions. To overcome this drawback, we introduce a general featurization strategy for bidentate ligands that is coupled with an automated feature selection pipeline and Bayesian ridge regression to perform multivariate linear regression modeling. This approach, which is applicable to any reaction, incorporates electronic, steric, and topological features (rigidity/flexibility, branching, geometry, constitution) and is well-suited for early-stage ligand optimization. Using only a limited number of points per dataset, our workflow capably predicts the enantioselectivity of four metal-catalyzed asymmetric reactions. Uncertainty estimates provided by Bayesian ridge regression permit the use of Bayesian optimization to efficiently explore pools of prospective new ligands. Using this procedure, a new library of 312 chiral bidentate ligands was screened to identify promising ligand candidates for a challenging asymmetric oxy-alkynylation reaction.

en: Reaction-agnostic featurization of bidentate ligands for Bayesian ridge regression of enantioselectivity

bidentate-ligands

display_name: bidentate-ligands

catalysis

display_name: catalysis

epfl

display_name: epfl

homogenous-catalysis

display_name: homogenous-catalysis

ligands

display_name: ligands

nccr-catalysis

display_name: nccr-catalysis


en: In this study, we benchmarked various interatomic potentials and force fields in comparison to an ab initio dataset for bulk amorphous alumina. We investigated a comprehensive set of fixed-charge and variable-charge potentials tailored for alumina. We also train a machine learning interatomic potential, using the NequIP framework. Results highlight that the fixed-charge potential by Matsui provides an ideal blend of computational speed and alignment with ab initio findings for stoichiometric alumina. For non-stoichiometric variants, the variable charge potentials, especially ReaxFF, align remarkably well with DFT outcomes. The NequIP ML potential, while superior in some instances and adaptable, might not be the best fit for specific tasks.

en:
Do we really need machine learning interatomic potentials for modeling amorphous metal oxides?
Case study on amorphous alumina by recycling an existing ab-initio database.

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en: We present ab initio calculations of hidden magnetoelectric multipolar order in Cr₂O₃ and its iron-based analogue, α-Fe₂O₃. We show the presence of hidden antiferroically-ordered magnetoelectric multipoles in both the prototypical magnetoelectric material Cr₂O₃, and centrosymmetric α-Fe₂O₃, which has the same crystal structure as Cr₂O₃, but a different magnetic dipolar ordering. In turn, we predict anti-magnetoelectric effects, in which local magnetic dipole moments are induced in opposite directions under the application of an external electric field, to create an additional antiferromagnetic ordering. We confirm the predicted induced moments using first-principles calculations. Our results demonstrate the existence of hidden magnetoelectric multipoles leading to local linear magnetoelectric responses even in centrosymmetric materials, where a net bulk linear magnetoelectric effect is forbidden by symmetry.  This archive contains input files and some output files necessary for creating all the data discussed in our paper with the title: Hidden orders and (anti-)Magnetoelectric Effects in Cr₂O₃ and α-Fe₂O₃.

en: Hidden orders and (anti-)magnetoelectric effects in Cr₂O₃ and α-Fe₂O₃

erc

display_name: erc

magnetoelectricity

display_name: magnetoelectricity

multipoles

display_name: multipoles


en:
In this paper, we propose an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation. Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus improve the convergence of statistical averages at a fraction of the original computational cost, a machine-learned (ML) energy function is trained on FP energies and forces and used in the MD simulations. The ML workflow and MD simulation times (≈200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV. The extension is successfully benchmarked on the same set of alkaline and alkaline-earth ions.
The record includes all molecular-dynamics trajectories, energies and forces used to obtain the solvation energies of alkaline and alkaline-earth ions in water, as reported in Table 2 of referenced paper.

en: Solvation free energies from machine learning molecular dynamics

solvation

display_name: solvation


en: Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg. The data provided here allows for reproducing findings from our work.

en: Machine learning of twin/matrix interfaces from local stress field

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).

magnesium-twinning

display_name: magnesium-twinning


en: Antiferromagnets are normally thought of as materials with compensated magnetic sublattices. This adds to their technological advantages but complicates readout of the antiferromagnetic state. We demonstrate theoretically the existence of a Dzyaloshinskii-Moriya interaction (DMI) which is determined by the magnetic symmetry classes of Cr₂O₃ surfaces with an in-plane magnetic easy axis. The DMI explains a previously predicted out-of-plane magnetization at the nominally compensated surfaces of chromia, leading to a surface-localized canted ferrimagnetism. This is in agreement with magnetotransport measurements and with density functional theory predictions which further allow us to quantify the strength of DMI. The temperature dependence of the transversal resistance for these planes shows distinct behavior in comparison with that of the Cr₂O₃  plane, which we attribute to the influence of DMI. Our work provides a framework to analyze surface-driven phenomena in antiferromagnets, and motivates the use of nominally compensated chromia surfaces for antiferomagnetic spintronics and magnonics. Here, we provide the data for density functional theory and Monte Carlo calculations. Micromagnetic and experimental data for this joint experiment-theory collaboration are archived separately according to EU regulations.

en: Surface-symmetry-driven Dzyaloshinskii-Moriya interaction and canted ferrimagnetism in collinear magnetoelectric antiferromagnet Cr₂O₃

materialscloud

en: Materials Cloud

density-functional-theory

display_name: density-functional-theory

dzyaloshinskii-moriya-interactions

display_name: dzyaloshinskii-moriya-interactions

magnetoelectrics

display_name: magnetoelectrics

magnetotransport

display_name: magnetotransport

micromagnetics

display_name: micromagnetics

surface-magnetization

display_name: surface-magnetization


en: We present a first-principles study of the low-temperature rhombohedral phase of BaTiO₃ using Hubbard-corrected density-functional theory. By employing density-functional perturbation theory, we compute the onsite Hubbard U for Ti(3d) states and the intersite Hubbard V between Ti(3d) and O(2p) states. We show that applying the onsite Hubbard U correction alone to Ti(3d) states proves detrimental, as it suppresses the Ti(3d)-O(2p) hybridization and drives the system towards a cubic phase. Conversely, when both onsite U and intersite V are considered, the localized character of the Ti(3d) states is maintained, while also preserving the Ti(3d)-O(2p) hybridization, restoring the rhombohedral phase of BaTiO₃. The generalized PBEsol+U+V functional yields good agreement with experimental results for the band gap and dielectric constant, while the optimized geometry is slightly less accurate compared to PBEsol. Zone-center phonon frequencies and Raman spectra are found to be significantly influenced by the underlying geometry. PBEsol and PBEsol+U+V provide satisfactory agreement with the experimental Raman spectrum when the PBEsol geometry is used, while PBEsol+U Raman spectrum diverges strongly from experimental data highlighting the adverse impact of the U correction alone in BaTiO₃. Our findings underscore the promise of the extended Hubbard PBEsol+U+V functional with first-principles U and V for the investigation of other ferroelectric perovskites with mixed ionic-covalent interactions.

en: Understanding the role of Hubbard corrections in the rhombohedral phase of BaTiO₃

envi

en: Environment

cscs

display_name: cscs

dftu

display_name: dftu

dftuv

display_name: dftuv

phonons

display_name: phonons

raman-spectra

display_name: raman-spectra


en: The mechanisms underlying the reaction between carbon monoxide (CO) and activated dioxygen on metal oxide substrates to produce CO₂ remain poorly understood, particularly regarding the role of oxygen vacancies and the nature of the activated O₂ adsorbate. In this study, we present experimental findings from infrared reflection-absorption spectroscopy on a model system of bulk monocrystalline CeO₂(111). Contrary to expectations, exposing the reduced surface to dioxygen (O₂) at 80 K does not yield activated oxygen species, such as superoxo or peroxo. Notably, in the presence of adsorbed CO, an unexpected low-temperature oxidation reaction occurs, consuming CO while oxidizing the CeO₂ substrate. Since a direct reaction between impinging O₂ and adsorbed CO is unlikely at these low temperatures, a novel mechanism is proposed. Extensive spin-polarized density functional theory (DFT) calculations reveal that oxygen vacancies play a critical role in this low-temperature CO oxidation. Initially located in the subsurface region (Vss), these vacancies migrate to the surface (Vs) via a concerted interaction with coadsorbed CO and O₂, leading to O₂ activation and the formation of superoxo or peroxo species. Detailed analysis identifies key reaction intermediates and quantifies their adsorption energies and activation barriers. Our findings suggest that the peroxo-mediated pathway, with its lower activation barrier, is more favorable for CO oxidation at low temperatures compared to the carbonate pathway. This study provides valuable insights into the dynamic role of subsurface oxygen vacancies in the activation of gaseous O₂ and CO oxidation mechanisms on CeO₂.

en: Synergistic effects in low-temperature CO oxidation on cerium oxide surfaces

ceria

display_name: ceria

co-oxidation

display_name: co-oxidation

dft

display_name: dft

peroxo-and-superoxo

display_name: peroxo-and-superoxo


en: An extensive dataset consisting of adsorption energies of pernicious impurities present in biomass upgrading processes on common catalysts and support materials has been generated. This work aims to inform catalyst and process development for the conversion of biomass-derived feedstocks to fuels and chemicals. A high-throughput workflow was developed to execute density functional theory calculations for a diverse set of atomic (Al, B, Ca, Cl, Fe, K, Mg, Mn, N, Na, P, S, Si, Zn) and molecular (COS, H₂S, HCl, HCN, K₂O, KCl, NH₃) species on 35 unique surfaces for transition-metal (Ag, Au, Co, Cu, Fe, Ir, Ni, Pd, Pt, Re, Rh, Ru) and metal-oxide (Al₂O₃, MgO, anatase-TiO₂, rutile-TiO₂, ZnO, ZrO₂) catalysts and supports. Approximately 3,000 unique adsorption geometries were obtained. The data record includes structure and calculation output files for each unique adsorbate geometry on each surface.

en: High-throughput dataset of impurity adsorption on common catalysts in biomass upgrading applications

high-throughput

display_name: high-throughput

vasp

display_name: vasp


en: Predicting the electrical properties of organic molecular crystals (OMCs) is challenging due to their complex crystal structures and electron-phonon (e-ph) interactions. Charge transport in OMCs is conventionally categorized into two limiting regimes – band transport, characterized by weak e-ph interactions, and charge hopping due to localized polarons formed by strong e-ph interactions. However, between these two limiting cases there is a less well understood intermediate regime where polarons are present but transport does not occur via hopping. Here we show a many-body first-principles approach that can accurately predict the carrier mobility in OMCs in the intermediate regime and shed light on its microscopic origin. Our approach combines a finite-temperature cumulant method to describe strong e-ph interactions with Green-Kubo transport calculations. We apply this parameter-free framework to naphthalene crystal, demonstrating electron mobility predictions within a factor of 1.5–2 of experiment between 100–300 K. Our analysis reveals that electrons couple strongly with both inter- and intramolecular phonons in the intermediate regime, as evidenced by the formation of a broad polaron satellite peak in the electron spectral function and the failure of the Boltzmann transport equation (BTE). Our study advances quantitative modeling of charge transport in complex organic crystals. This dataset contains input and output files required to reproduce the BTE results presented in our work.

en: Intermediate polaronic charge transport in organic crystals from a many-body first-principles approach

boltzmann-equation

display_name: boltzmann-equation

carrier-mobility

display_name: carrier-mobility

charge-transport

display_name: charge-transport

electron-phonon-coupling

display_name: electron-phonon-coupling

electron-phonon-scattering-rate

display_name: electron-phonon-scattering-rate

first-principles-calculations

display_name: first-principles-calculations

molecular-crystal

display_name: molecular-crystal

naphthalene

display_name: naphthalene

organic-crystal

display_name: organic-crystal

polaron

display_name: polaron


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

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

covalent-organic-frameworks

display_name: covalent-organic-frameworks

drug-delivery

display_name: drug-delivery

metal-organic-frameworks

display_name: metal-organic-frameworks


en:
We developed a general framework for hybrid quantum-classical computing of molecular and periodic embedding calculations based on an orbital space separation of the fragment and environment degrees of freedom. We show its potential by presenting a specific implementation of periodic range-separated DFT coupled to a quantum circuit ansatz, whereby the variational quantum eigensolver and the quantum equation-of-motion approach are used to obtain the low-lying spectrum of the embedded fragment Hamiltonian.
Application of this scheme to study strongly correlated molecular systems and localized electronic states in materials is showcased through the accurate prediction of the optical properties for the neutral oxygen vacancy in magnesium oxide (MgO). Despite some discrepancies in absorption predictions, the method demonstrates competitive performance with state-of-the-art ab initio approaches, particularly evidenced by the accurate prediction of the photoluminescence emission peak.

en: A general framework for active space embedding methods: applications in quantum computing

marvelqs

display_name: marvelqs

qiskit-nature

display_name: qiskit-nature

quantum-embedding

display_name: quantum-embedding

quantum-simulation

display_name: quantum-simulation


en:
Resonant inelastic X-ray scattering (RIXS) can probe localized excitations at selected atoms in materials, including particle-hole transitions from valence to the conduction bands. These transitions are governed by fundamental prop- erties of the corresponding Bloch wave-functions, including orbital and mag- netic degrees of freedom, and quantum geometric properties such as the Berry curvature. In particular, orbital angular momentum (OAM), which is closely linked to the Berry curvature, can exhibit a nontrivial momentum dependence. We demonstrate how information on such OAM textures can be extracted from the circular dichroism in RIXS. Based on accurate modeling with first-principles treatment of the key ingredient – the light-matter interaction – we simulate dichroic RIXS spectra for the prototypical transition metal dichalcogenide MoSe₂ and the two-dimensional topological insulator 1T′-MoS₂ . Guided by an in- tuitive picture for the optical selection rules, we discuss how the momentum- dependent OAM manifests itself in the dichroic RIXS signal if one controls the momentum transfer. Our calculations are performed for typical experimental geometries and parameter regimes, and demonstrate the possibility of observ- ing the predicted circular dichroism in forthcoming experiments. Thus, our work establishes a new avenue to observing Berry curvature and topological states in quantum materials.

The simulated RIXS spectra and the scripts to generate the plots in this work are available in this archive.

en: Probing magnetic orbitals and Berry curvature with circular dichroism in resonant inelastic X-ray scattering

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).

marvel

display_name: marvel

quantum-espresso

display_name: quantum-espresso

two-dimensional

display_name: two-dimensional

wannier-functions

display_name: wannier-functions


en: This dataset includes the full computational workflows of a proof-of-concept study of various possible mechanisms (standard and bifunctional ones) for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) on exfoliated NiOOH as electrocatalyst, including active edge sites (M5) and hydrogen acceptor sites in the same model system. Furthermore, explicit water is included in the model to describe the equilibration of the M-OOH species to M-OOH/eq, a crucial step that enables a bifunctional route to be operative. Additionally, different single Fe-dopant positions (M1, M2, M3, M4, M5, M6 and M7) are considered and four different reaction schemes (S1, S2, S3 and S4) are studied for the OER and the reverse ORR process. The results are relevant in alkaline conditions, where the studied model systems are stable. Certain Fe-dopant positions result in active Ni-edge sites with very low overpotentials provided that water is present within the model system.

en: Oxygen evolution and reduction on Fe‑doped NiOOH

materialscloud

en: Materials Cloud

electrochemical-overpotential

display_name: electrochemical-overpotential

mixed-metal-oxyhydroxides

display_name: mixed-metal-oxyhydroxides

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction