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

en: Confinement effects and acid strength in Zeolites

acid-base

display_name: acid-base

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics

zeolites

display_name: zeolites


$oid: OBSOLETE

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


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

en: Structure-property maps with kernel principal covariates regression

dimensionality-reduction

display_name: dimensionality-reduction

epfl

display_name: epfl

erc

display_name: erc

kernel-methods

display_name: kernel-methods

machine-learning

display_name: machine-learning

materials-science

display_name: materials-science

max

display_name: max

snsf

display_name: snsf


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

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

ab-initio

display_name: ab-initio

boltzmann-transport

display_name: boltzmann-transport

electron-drag

display_name: electron-drag

electron-phonon-coupling

display_name: electron-phonon-coupling

electron-transport

display_name: electron-transport

intersect

display_name: intersect

phonon-drag

display_name: phonon-drag

phonon-transport

display_name: phonon-transport

thermoelectricity

display_name: thermoelectricity


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

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

density-functional-theory

display_name: density-functional-theory

molecular-dynamics

display_name: molecular-dynamics

zeolites

display_name: zeolites


en: Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help accelerate the discovery of new alloys. We present two datasets: 1) chemical composition, and 2) mechanical properties for predominantly wrought aluminum alloys. The first dataset contains 13,358 entries on aluminum alloy compositions extracted from academic literature and US patents using text processing techniques, in addition to 93 wrought aluminum alloys which are already registered with the Aluminum Association. The second dataset contains 1,268 entries on mechanical properties for aluminum alloys, where each entry is associated with a particular wrought series designation, extracted from tables in academic literature.

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

aluminum

display_name: aluminum

chemical-composition

display_name: chemical-composition

materials-discovery

display_name: materials-discovery

mechanical-properties

display_name: mechanical-properties

metals-and-alloys

display_name: metals-and-alloys


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


experimental

display_name: experimental


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

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

flowmol

display_name: flowmol

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

python-pickle

display_name: python-pickle

software-input-files

display_name: software-input-files


en: The importance of reference frame for pressure at the liquid-vapour interface


dimensionality-reduction

display_name: dimensionality-reduction

epfl

display_name: epfl

erc

display_name: erc

kernel-methods

display_name: kernel-methods

machine-learning

display_name: machine-learning

materials-science

display_name: materials-science

max

display_name: max

snsf

display_name: snsf


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

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

cerium-oxide-surfaces

display_name: cerium-oxide-surfaces

excess-charge-localization

display_name: excess-charge-localization

facet-dependent-stability

display_name: facet-dependent-stability

lattice-relaxations

display_name: lattice-relaxations

oxygen-vacancies

display_name: oxygen-vacancies


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

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

experimental

display_name: experimental

large-area-device

display_name: large-area-device

nonfused-ring-electron-acceptor

display_name: nonfused-ring-electron-acceptor

organic-solar-cells

display_name: organic-solar-cells

record-efficiency

display_name: record-efficiency


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

materialscloud

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

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

en: Materials Cloud

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

cineca

display_name: cineca

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

linear-response

display_name: linear-response

liouville-lanczos-approach

display_name: liouville-lanczos-approach

magnons

display_name: magnons

marvel

display_name: marvel

quantum-espresso

display_name: quantum-espresso

spin-orbit-coupling

display_name: spin-orbit-coupling

spin-wave-spectra

display_name: spin-wave-spectra


envi

en: Environment

max

display_name: max


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

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

density-functional-theory

display_name: density-functional-theory

foster-boys-centers

display_name: foster-boys-centers

machine-learning

display_name: machine-learning


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

en: Superconductivity in antiperovskites

anti-perovskites

display_name: anti-perovskites

electron-phonon-coupling

display_name: electron-phonon-coupling

superconductivity

display_name: superconductivity


superconductivity-in-antiperovskites

title_for_slug: superconductivity-in-antiperovskites


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

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

dft

display_name: dft

gw

display_name: gw

negf

display_name: negf


en: Skutterudites are crystals with a cagelike structure that can be augmented with filler atoms ("rattlers"), usually leading to a reduction in thermal conductivity that can be exploited for thermoelectric applications. Here, we leverage the recently introduced Wigner formulation of thermal transport to elucidate the microscopic physics underlying heat conduction in skutterudites, showing that filler atoms can drive a crossover from the Boltzmann to the Wigner regimes of thermal transport, i.e., from particlelike conduction to wavelike tunneling. At temperatures where the thermoelectric efficiency of skutterudites is largest, wavelike tunneling can become comparable to particlelike propagation. We define a Boltzmann deviation descriptor able to differentiate the two regimes and relate the competition between the two mechanisms to the materials' chemistry, providing a design strategy to select rattlers and identify optimal compositions for thermoelectric applications.

en: Crossover from Boltzmann to Wigner thermal transport in thermoelectric skutterudites

phonons

display_name: phonons

thermal-conductivity

display_name: thermal-conductivity

thermoelectrics

display_name: thermoelectrics


marvel

display_name: marvel


en: Achieving 19% efficiency in nonfused ring electron acceptor solar cells via solubility hysteresis sequential condensation strategy

experimental

display_name: experimental

large-area-device

display_name: large-area-device

nonfused-ring-electron-acceptor

display_name: nonfused-ring-electron-acceptor

organic-solar-cells

display_name: organic-solar-cells

record-efficiency

display_name: record-efficiency


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

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

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

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

message-passing

display_name: message-passing

pasc

display_name: pasc

reproducibility

display_name: reproducibility


machine-learning

display_name: machine-learning


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

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

aiidalab

display_name: aiidalab

graphene-nanoribbons

display_name: graphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis

scanning-probe-microscopy

display_name: scanning-probe-microscopy

snsf

display_name: snsf


materialscloud

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

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

dft

display_name: dft


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

materialscloud

en: Materials Cloud

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

aqua-complex

display_name: aqua-complex

graph-neural-network

display_name: graph-neural-network

spectroscopy

display_name: spectroscopy


envi

en: Environment


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

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

experimental

display_name: experimental

graphene-nanoplatelets

display_name: graphene-nanoplatelets

raman

display_name: raman


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

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

diffuse-double-layer

display_name: diffuse-double-layer

electrochemical-system

display_name: electrochemical-system

finite-element-method

display_name: finite-element-method

semi-analytical-solution

display_name: semi-analytical-solution


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

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

compton-scattering

display_name: compton-scattering

density-functional-theory

display_name: density-functional-theory

erc

display_name: erc

horizon-europe

display_name: horizon-europe

magneto-electric-toroidal-moment

display_name: magneto-electric-toroidal-moment


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

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

electronic-properties

display_name: electronic-properties

marvel

display_name: marvel

on-surface-synthesis

display_name: on-surface-synthesis

stm

display_name: stm


dft

display_name: dft


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


en:
The high-throughput molecular exploration and screening of organic electronic materials often starts with either a 'top-down' mining of existing repositories, or the 'bottom-up' assembly of fragments based on predetermined rules and known synthetic templates. In both instances, the datasets used are often produced on a case-by-case basis, and require the high-quality computation of electronic properties and extensive user input: curation in the top-down approach, and the construction of a fragment library and introduction of rules for linking them in the bottom-up approach. Both approaches are time-consuming and require significant computational resources. Here, we generate a top-down set named FORMED consisting of 117K synthesized molecules containing their optimized structures, associated electronic and topological properties and chemical composition, and use these structures as a vast library of molecular building blocks for bottom-up fragment-based materials design. A tool is developed to automate the coupling of these building block units based on their available Csp2-H bonds, thus providing a fundamental link between the two philosophies of dataset construction. Statistical models are trained on this dataset and a subset of the resulting hybrid top-down/bottom-up compounds (selected dimers), which enable on-the-fly prediction of key ground state (frontier molecular orbital gaps) and excited state (S1 and T1 energies) properties from molecular geometries with high accuracy across all known p-block organic compound space.
With access to ab initio-quality optical properties in hand, it is possible to apply this bottom-up pipeline using existing compounds as molecular building blocks to any materials design campaign. To illustrate this, we construct and screen over a million molecular candidates (predicted dimers) for efficient intramolecular singlet fission, the leading candidates of which provide insight into the structural features that may promote this multiexciton-generating process.

en: Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design

crystal-structures

display_name: crystal-structures

donor-acceptor-copolymers

display_name: donor-acceptor-copolymers

optical-properties

display_name: optical-properties

organic-molecules

display_name: organic-molecules

photophysical-properties

display_name: photophysical-properties


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

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

en: Interface polarization in heterovalent core/shell nanocrystals

experimental

display_name: experimental

heterostructured-nanocrystal

display_name: heterostructured-nanocrystal

interface-polarization

display_name: interface-polarization


materialscloud

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

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


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

en: Materials Cloud

en: Electronic structure and magnetism of pristine and Fe-doped α-MnO₂ from density-functional theory with extended Hubbard functionals

band-gap

display_name: band-gap

bond-angles

display_name: bond-angles

bond-length

display_name: bond-length

crystal-structure

display_name: crystal-structure

density-functional-theory

display_name: density-functional-theory

density-of-states

display_name: density-of-states

dftu

display_name: dftu

dftuv

display_name: dftuv

formation-energy

display_name: formation-energy

hubbard-parameters

display_name: hubbard-parameters

hubbard-projectors

display_name: hubbard-projectors

interstitial-doping

display_name: interstitial-doping

magnetic-moment

display_name: magnetic-moment

orthogonalized-atomic-orbitals

display_name: orthogonalized-atomic-orbitals

oxidation-state

display_name: oxidation-state

self-interactions

display_name: self-interactions

spin-configuration

display_name: spin-configuration

substitutional-doping

display_name: substitutional-doping


en: In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for an increased accuracy in the calculations. Here, we present a dataset of calculations for 175k solid-state materials obtained with two improved functionals: PBE for solids (that yields consistently better geometries than the PBE) and SCAN (probably the best all-around functional at the moment). Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for more reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBE for solids, and SCAN.

en: A new dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals

envi

en: Environment

high-throughput

display_name: high-throughput

pbe-for-solids

display_name: pbe-for-solids

scan

display_name: scan


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

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

deacon-process

display_name: deacon-process

oxygen-induced-de-chlorination-process

display_name: oxygen-induced-de-chlorination-process

peroxo-surface-species

display_name: peroxo-surface-species

reduced-ceria

display_name: reduced-ceria


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

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

band-gap

display_name: band-gap

bond-angles

display_name: bond-angles

bond-length

display_name: bond-length

crystal-structure

display_name: crystal-structure

density-functional-theory

display_name: density-functional-theory

density-of-states

display_name: density-of-states

dftu

display_name: dftu

dftuv

display_name: dftuv


en: The mapped gaussian process (MGP) force-field used to elucidate the surface alloying of Cu-Zn. The force-field is made based on first-principles data by using machine-learning technique called Gaussian Process as implemented in FLARE package (https://github.com/mir-group/flare). Active and on-the-fly learning were employed to build the database efficiently. The simulation reveals atomistic details of the alloying process, i.e., the incorporation of deposited Zn adatoms to the Cu substrate. The surface alloying is found to start at upper and lower terraces near the step edge, which emphasize the role of steps and kinks in the alloying. The incorporation of Zn at the middle terrace was found at the later stage of the simulation.

en: The mapped gaussian process (MGP) force-field of Cu-Zn surface alloy

cu-zn-surface

display_name: cu-zn-surface

first-principles

display_name: first-principles

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


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

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

electron-phonon-coupling

display_name: electron-phonon-coupling

kerr-angle

display_name: kerr-angle

transport

display_name: transport


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

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

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

amorphous

display_name: amorphous

disorder

display_name: disorder

intersect

display_name: intersect

order

display_name: order

software

display_name: software


materialscloud

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

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


en: On-surface synthesis serves as a powerful approach to construct π-conjugated carbon nanostructures that are not accessible by conventional wet chemistry. Nevertheless, this method has been limited by the types and numbers of available on-surface transformations. While the majority of successful cases exploit thermally triggered dehalogenative carbon–carbon coupling and cyclodehydrogenation, rearrangement of appropriate functional moieties has received limited research attention. In a recent work, we describe the unprecedented interchain coupling and thermally induced skeleton rearrangement  of (dihydro)indeno[2,1-b]fluorene (IF) polymers on an Au(111) surface under ultrahigh vacuum conditions, leading to different ladder polymers as well as fully fused graphene nanoribbon segments containing pentagonal and heptagonal rings. Au-coordinated nanoribbons are also observed. All structures are unambiguously characterized by high-resolution scanning probe microscopy. The results provide an avenue to fabricating a wider variety of π-conjugated polymers and carbon nanostructures comprising nonhexagonal rings as well as rarely explored organometallic nanoribbons. This record contains data of the calculations that support our results.

en: On-surface interchain coupling and skeletal rearrangement of indenofluorene polymers

cscs

display_name: cscs

dft

display_name: dft

indenofluorene

display_name: indenofluorene

non-contact-atomic-force-microscopy

display_name: non-contact-atomic-force-microscopy

snsf

display_name: snsf

stm

display_name: stm


materialscloud

en: Materials Cloud


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

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

cationic-intermixing

display_name: cationic-intermixing

experimental

display_name: experimental

magnetic-topological-insulator

display_name: magnetic-topological-insulator


envi

en: Environment


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

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

ab-initio

display_name: ab-initio

carbon-nanotubes

display_name: carbon-nanotubes

terahertz-detector

display_name: terahertz-detector


en: In the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for an increased accuracy in the calculations. Here, we present a dataset of calculations for 175k solid-state materials obtained with two improved functionals: PBE for solids (that yields consistently better geometries than the PBE) and SCAN (probably the best all-around functional at the moment). Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for more reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBE for solids, and SCAN.

en: A new dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals

density-functional-theory

display_name: density-functional-theory

high-throughput

display_name: high-throughput

pbe-for-solids

display_name: pbe-for-solids

scan

display_name: scan


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

en: Jahn-Teller effect in the cubic fullerides A₃C₆₀

cubic-fullerides

display_name: cubic-fullerides

elastic-coupling

display_name: elastic-coupling

jahn-teller-effect

display_name: jahn-teller-effect

phonons

display_name: phonons


en:
**Data for Properties of α-Brass Nanoparticles. 1. Neural Network Potential Energy Surface**
Jan Weinreich, Anton Römer, Martín Leandro Paleico, and Jörg Behler

53 841 reference structures of alpha brass (less 40 % Zn) with following split
 - 4009 brass clusters
 - 8492 molten brass bulk structures
 - 8964 copper slabs, and 16 878 brass slabs
 - 5377 copper bulk structures
 - 10 121 brass bulk structures have been included.

53 841 total energies and 8 903 340 force components. The ranges of values for the energies and force components to be fitted have a width of about 2 eV/atom and 15 eV/Å, respectively. However, some structures may have slightly higher Zn content as discussed in Fig 3 (https://arxiv.org/abs/2001.10906)

The archive contains an easily usable npz file as well as the original input.data file used to fit the potential energy surface. Additionally a Jupyter notebook describes in great detail how the data was converted to the npz format and how to read the data e.g. for subsequent use with python. In addition an example VASP calculation was added to provide detailed information about how the reference data was calculated with DFT.

DETAILS:
- DFT PB VASP-5.3 target accuracy of the total energy few meV/atom
- Convergence tests with respect to the number of k-points showed that in order to fulfill this criterion a k-point grid of 12 × 12 × 12 is needed for a conventional four-atom copper fcc unit cell with a lattice constant of about 3.63 Å along with a plane wave cutoff energy of 500 eV and projector augmented wave potentials.(61,63)
- Larger systems have been calculated using an adapted k-point grid corresponding to the same k-point density. The Γ-point centered k-point grids have been constructed employing the Monkhorst–Pack scheme. For surface calculations, 4–14 layer slabs with a total vacuum thickness of at least 8 Å have been used.
- In the case of cluster calculations, which have also been treated in a periodic setup, the periodic images of the clusters have been separated by at least 8 Å in all three spatial directions. The convergence of very large clusters with diameters of d ≈ 22 Å, which have been used to include specific atomic environments in the data set, has been extensively tested and we found that using the Γ-point only is sufficient to reach the required convergence level.

en: Properties of α-brass nanoparticles. 1. Neural network potential energy surface

alloys

display_name: alloys

brass

display_name: brass

database

display_name: database

dft

display_name: dft

machine-learning

display_name: machine-learning

neural-networks

display_name: neural-networks

potential-energy-surfaces

display_name: potential-energy-surfaces


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

en: Synthesis and characterization of [7]triangulene

erc

display_name: erc

graphene-flagship

display_name: graphene-flagship

nanographene

display_name: nanographene

snsf

display_name: snsf

triangulene

display_name: triangulene


materialscloud

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

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


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

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

density-functional-theory

display_name: density-functional-theory

high-throughput

display_name: high-throughput

pbe-for-solids

display_name: pbe-for-solids

scan

display_name: scan


materialscloud

en: Materials Cloud


en: Hydrogen (H) embrittlement in multicomponent austenitic alloys is a serious limitation to their application in many environments. Recent experiments show that the High-Entropy Alloy (HEA) CoCrFeMnNi absorbs more H than 304 Stainless Steel but is less prone to embrittlement while the HEA CoCrFeNi is not embrittled under comparable conditions. As a first step toward understanding H embrittlement, here a comprehensive first-principles study of H absorption, surface, and fracture energies in the presence of H is presented for 304 Stainless Steel, 316 Stainless Steel, CoCrFeNi, and CoCrFeMnNi. A collinear paramagnetic model of the magnetic state is used, which is likely more realistic than previous proposed magnetic states. All alloys have a statistical distribution of H absorption sites. Hence, at low concentrations, H is effectively trapped in the lattice making it more difficult for H to segregate to defects or interfaces. Agreement with experimental H solubilities across a range of chemical potentials can be achieved with minor fitting of the average H absorption energy. The (111) surface energies for 0, 50, and 100% H surface coverage are very similar across all alloys. The fracture energies for two representative thermodynamic conditions are then determined. SS304 and CoCrFeNi are found to have the lowest fracture energies, but experiments suggest rather different embrittlement tendencies. These results indicate that differences in H embrittlement across these austenitic alloys are not due solely to differences in H absorption or H-reduced fracture energy, thus requiring more sophisticated concepts than those recently found successful for fcc Ni.

en: First principles study of the effect of hydrogen in austenitic stainless steels and high entropy alloys

complex-alloys

display_name: complex-alloys

h-absorption

display_name: h-absorption

hydrogen-embrittlement

display_name: hydrogen-embrittlement

surface-energy

display_name: surface-energy


envi

en: Environment


en: The development of next generation perovskite-based optoelectronic devices relies critically on the understanding of the interaction between charge carriers and the polar lattice in out-of-equilibrium conditions. While it has become increasingly evident for CsPbBr3 perovskites that the Pb-Br framework flexibility plays a key role in their light-activated functionality, the corresponding local structural rearrangement has not yet been unambiguously identified. In this work, we demonstrate that the photoinduced lattice changes in the system are due to a specific polaronic distortion, associated with the activation of a longitudinal optical phonon mode at 18 meV by electron-phonon coupling, and we quantify the associated structural changes with atomic-level precision. Key to this achievement is the combination of time-resolved and temperature-dependent studies at Br K-edge and Pb L3-edge X-ray absorption with refined ab-initio simulations, which fully account for the screened core-hole final state effects on the X-ray absorption spectra. From the temporal kinetics, we show that carrier recombination reversibly unlocks the structural deformation at both Br and Pb sites. The comparison with the temperature-dependent XAS results rules out thermal effects as the primary source of distortion of the Pb-Br bonding motif during photoexcitation. Our work provides a comprehensive description of the CsPbBr3 perovskites photophysics, offering novel insights on the light-induced response of the system and its exceptional optoelectronic properties.

en: Quantifying photoinduced polaronic distortions in inorganic lead halide perovskites nanocrystals

electron-phonon-coupling

display_name: electron-phonon-coupling

frohilch-polarons

display_name: frohilch-polarons

halide-perovskites

display_name: halide-perovskites

marvel

display_name: marvel

time-dependent-x-ray-absorption

display_name: time-dependent-x-ray-absorption


en: Understanding the catalyst compositional and structural features that control selectivity is of uttermost importance to target desired products in chemical reactions. In this joint experimental–computational work, we leverage tailored Cu/ZnO precatalysts as a material platform to identify the intrinsic features of methane-producing and ethanol-producing CuZn catalysts in the electrochemical CO2 reduction reaction (CO2RR). Specifically, we find that Cu@ZnO nanocrystals, where a central Cu domain is decorated with ZnO domains, and ZnO@Cu nanocrystals, where a central ZnO domain is decorated with Cu domains, evolve into Cu@CuZn core@shell catalysts that are selective for methane (∼52%) and ethanol (∼39%), respectively. Operando X-ray absorption spectroscopy and various microscopy methods evidence that a higher degree of surface alloying along with a higher concentration of metallic Zn improve the ethanol selectivity. Density functional theory explains that the combination of electronic and tandem effects accounts for such selectivity. These findings mark a step ahead towards understanding structure–property relationships in bimetallic catalysts for the CO2RR and their rational tuning to increase selectivity towards target products, especially alcohols.

en: Elucidating the structure-dependent selectivity towards methane and ethanol of CuZn in the CO2 electroreduction using tailored Cu/ZnO precatalysts

cu

display_name: cu

cuzn

display_name: cuzn

dft

display_name: dft

epfl

display_name: epfl

erc

display_name: erc


snsf

display_name: snsf


en: This dataset, consisting of 29 videos, has been used for the publication 'High-Throughput Activity Screening and Sorting of Single Catalyst Particles with a Droplet Microreactor Using Dielectrophoresis': "A droplet microreactor has been developed for fluorescence activated sorting of catalyst particles using dielectrophoresis. Fluid Catalytic Cracking (FCC) particles stained with styrene derivatives are analyzed with the developed analytical platform and sorted based on catalytic activity. Highly active and low-to-moderately active catalyst particles are sorted out, using 4-fluorostyrene or 4-methoxystyrene as a probe respectively. The FCC particles are encapsulated in liquid droplets, where the fluorescent FCC particles activate the dielectrophoretic sorter, and are sorted within 200 ms."

en: High-throughput activity screening and sorting of single catalyst particles with a droplet microreactor using dielectrophoresis

dielectrophoretic-sorting

display_name: dielectrophoretic-sorting

experimental

display_name: experimental

fluid-catalytic-cracking-catalyst

display_name: fluid-catalytic-cracking-catalyst


en:
In order to identify novel substrate materials, we developed a high-throughput bond breaking algorithm. This algorithm takes a three-dimensional crystal as input, systematically breaks bonds, and checks if the bonding network has been reduced to two periodic directions. We apply this algorithm to Materials Project database and identify 4,693 symmetrically unique cleaved surfaces across 2,133 crystals. We then characterize the thermodynamic stability of these cleaved surfaces using the DFT software VASP, characterizing 3,991 surfaces as potential substrates with energy comparable to the experimentally used substrates (0001) AlN, ZnO, and CdS.

This repository contains the structure files, setting files, pseudopotential choices, bulk precursor structure and MaterialsProject ID, and thermodynamic data for the substrates considered in this work.

en: Computational synthesis of substrates by crystal cleavage

high-throughput

display_name: high-throughput

lattice-matching

display_name: lattice-matching

substrate

display_name: substrate

vasp

display_name: vasp


en: Through the use of the piecewise-linearity condition of the total energy, we correct the self-interaction for the study of polarons by constructing nonempirical functionals at the semilocal level of theory. We consider two functionals, the γDFT and the μDFT functionals, both of which are based on the addition of a weak local  potential to the semilocal Hamiltonian to enforce the piecewise-linearity condition. We show that the resulting polaron properties are in good agreement with reference hybrid functional calculations. This supports the use of semilocal functionals for calculating polaron properties.

en: Nonempirical semilocal density functionals for correcting the self-interaction of polaronic states

density-functional-theory

display_name: density-functional-theory

piecewise-linearity

display_name: piecewise-linearity

polarons

display_name: polarons

self-interaction

display_name: self-interaction


materialscloud

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

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


en: By combining metal nodes and organic linkers, an  infinite number of metal organic frameworks (MOFs) can be designed in silico. When making new databases of such hypothetical MOFs, we need to assure that they not only contribute towards the growth of the count of structures but also add different chemistry to existing databases. In this work, we designed a database of ~20,000 hypothetical MOFs which are diverse in terms of their chemical design space—metal nodes, organic linkers, functional groups and pore geometries. Using Machine Learning techniques, we visualized and quantified the diversity of these structures. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications---post combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post combustion carbon capture) and MOF-5 (for hydrogen storage).

en: Diversifying databases of metal organic frameworks for high-throughput computational screening

diversity

display_name: diversity

machine-learning

display_name: machine-learning

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-simulations

display_name: molecular-simulations


materialscloud

en: Materials Cloud

erc

display_name: erc


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

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

casino-quantum-monte-carlo

display_name: casino-quantum-monte-carlo

quantum-espresso

display_name: quantum-espresso


envi

en: Environment


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

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

ab-initio

display_name: ab-initio

molecular-dynamics

display_name: molecular-dynamics


en:
A comprehensive description of photo-chemical processes in materials, comprising spin-forbidden processes such as intersystem crossing and phosphorescence, implies to take into account spin-orbit coupling. We present an efficient implementation of a perturbative spin-orbit coupling correction for the Tamm-Dancoff approximation of linear-response time-dependent density functional theory within a mixed Gaussian and plane waves framework relying on spin-orbit coupling corrected pseudopotentials. The implementation is validated for a benchmark set of small aromatic molecules, with mean errors in excitation energies and spin-orbit coupling matrix elements being in the range of 0.1 - 0.6 eV and 1.0 to 13.8 cm⁻¹ in comparison to density functional theory and density functional theory multi-reference configuration interaction reference results. Computational timings are given for a bismuth-containing metal-organic framework.
The data in this record are supplementary data to the manuscript linked below.

en: Perturbative spin-orbit couplings for the simulation of extended framework materials

spin-orbit-coupling

display_name: spin-orbit-coupling


en: This entry provides original trajectories of on-the-fly probability enhanced sampling (OPES) and of molecular dynamics simulations and images of nudged-elastic band (NEB) calculation. Jupyter notebooks are provided for a NEB profile, NEB collective variables, free energy surface of the OPES simulations, hole transfer of the OPES simulations, proton transfer mechanisms of of the OPES simulations, water density profiles of MD simulations, radial distribution functions of MD simulations, reweight of Bi-O bonds of OPES simulations, coordination numbers of surface Bi atoms and hydrogen-bond analysis of proton transfer.

en: Data and analyses for first proton-coupled electron transfer of water oxidation at the BiVO₄-water interface

machine-learning-potentials

display_name: machine-learning-potentials

on-the-fly-probability-sampling

display_name: on-the-fly-probability-sampling

opes

display_name: opes

proton-couple-electron-transfer

display_name: proton-couple-electron-transfer

reweighting

display_name: reweighting


en: Unlocking the potential of topological order within many-body spin systems has long been a central pursuit in the realm of quantum materials. Despite extensive efforts, the quest for a versatile platform enabling site-selective spin manipulation, essential for tuning and probing diverse topological phases, has persisted. Here, we utilize on-surface synthesis to construct spin-1/2 alternating-exchange Heisenberg (AH) chains with antiferromagnetic couplings J1 and J2 by covalently linking Clar's goblets -- nanographenes each hosting two antiferromagnetically-coupled unpaired electrons. In a recent work, utilizing scanning tunneling microscopy, we exert atomic-scale control over the spin chain lengths, parities and exchange-coupling terminations, and probe their magnetic response by means of inelastic tunneling spectroscopy. Our investigation confirms the gapped nature of bulk excitations in the chains, known as triplons. Besides, the triplon dispersion relation is successfully extracted from the spatial variation of tunneling spectral amplitudes. Furthermore, depending on the parity and termination of chains, we observe varying numbers of in-gap S=1/2 edge spins, enabling the determination of the degeneracy of distinct topological ground states in the thermodynamic limit-either 1, 2, or 4. By monitoring interactions between these edge spins, we identify the exponential decay of spin correlations. Our experimental findings, corroborated by theoretical calculations, present a phase-controlled many-body platform, opening promising avenues toward the development of spin-based quantum devices. The record contains data and codes that support the results discussed in the manuscript.

en: Tunable topological phases in nanographene-based spin-½ alternating-exchange Heisenberg chains

magnetic-nanographene

display_name: magnetic-nanographene

spin-chian

display_name: spin-chian

topological-phase-transition

display_name: topological-phase-transition


en: The choice of the ideal material employed in selector devices is a tough task both from the theoretical and experimental side, especially due to the lack of a synergistic approach between techniques able to correlate specific material properties with device characteristics. Using a material-to-device multiscale technique, a reliable protocol for an efficient characterization of the active traps in amorphous GeSe chalcogenide is proposed. The resulting trap maps trace back the specific features of materials responsible for the measured findings, and connect them to an atomistic description of the sample. The metrological approach can be straightforwardly extended to other materials and devices, which is very beneficial for an efficient material-device codesign and the optimization of novel technologies.

en: Device-to-materials pathway for electron traps detection in amorphous GeSe-based selectors

amorphous

display_name: amorphous

chalcogenides

display_name: chalcogenides

device-to-materials

display_name: device-to-materials

electronic-structure

display_name: electronic-structure

intersect

display_name: intersect

openmodel

display_name: openmodel

selectors

display_name: selectors


materialscloud

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

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


en:
The high-throughput molecular exploration and screening of organic electronic materials often starts with either a 'top-down' mining of existing repositories, or the 'bottom-up' assembly of fragments based on predetermined rules and known synthetic templates. In both instances, the datasets used are often produced on a case-by-case basis, and require the high-quality computation of electronic properties and extensive user input: curation in the top-down approach, and the construction of a fragment library and introduction of rules for linking them in the bottom-up approach. Both approaches are time-consuming and require significant computational resources. Here, we generate a top-down set named FORMED consisting of 117K synthesized molecules containing their optimized structures, associated electronic and topological properties and chemical composition, and use these structures as a vast library of molecular building blocks for bottom-up fragment-based materials design. A tool is developed to automate the coupling of these building block units based on their available Csp2-H bonds, thus providing a fundamental link between the two philosophies of dataset construction. Statistical models are trained on this dataset and a subset of the resulting hybrid top-down/bottom-up compounds (selected dimers), which enable on-the-fly prediction of key ground state (frontier molecular orbital gaps) and excited state (S1 and T1 energies) properties from molecular geometries with high accuracy across all known p-block organic compound space.
With access to ab initio-quality optical properties in hand, it is possible to apply this bottom-up pipeline using existing compounds as molecular building blocks to any materials design campaign. To illustrate this, we construct and screen over a million molecular candidates (predicted dimers) for efficient intramolecular singlet fission, the leading candidates of which provide insight into the structural features that may promote this multiexciton-generating process.

en: Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design

crystal-structures

display_name: crystal-structures

donor-acceptor-copolymers

display_name: donor-acceptor-copolymers

marvel

display_name: marvel

optical-properties

display_name: optical-properties

organic-molecules

display_name: organic-molecules

photophysical-properties

display_name: photophysical-properties


en: We present a numerical tool for solving the non-relativistic Kohn-Sham problem for spherically-symmetric atoms. It treats the Schrödinger equation as an integral equation relying heavily on convolutions. The solver supports different types of exchange-correlation functionals including screened and long-range corrected hybrids. We implement a new method for treating range separation based on the complementary error function kernel. The present tool is applied in spin-restricted non-relativistic total energy calculations of atoms. A comparison with ultra-precise reference data[Cinal, JOMC 58, 1571 (2020)] shows a 14-digit agreement for Hartree-Fock results. We provide further benchmark data obtained with 5 different exchange-correlation functionals: VWN5 (the local-density approximation), PBE (the generalized gradient approximation), PBE0 and B3LYP (hybrids with a Fock exchange) and LC-BLYP (hybrid with a long-range corrected exchange).

materialscloud

en: Materials Cloud

en: Total energies of atoms from integral-equation radial solver

atoms

display_name: atoms

density-functional-theory

display_name: density-functional-theory

hartree-fock

display_name: hartree-fock

high-precision

display_name: high-precision

hybrids

display_name: hybrids


envi

en: Environment


en: We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict Faradaic Efficiencies for 11 CO2RR products, as a function of the applied voltage at which the reaction takes place, and the relative amounts of non equivalent surface sites, distinguished according to their nominal coordination. A satisfactory model accuracy is found only when discriminating data according to their provenance. On one hand, this result points at a qualitative agreement across reported experimental CO2RR  trends for single-crystal surfaces with well-defined terminations. On the other, this finding hints at the presence of differences in nominally identical catalysts and/or CO2RR measurements, which result in quantitative disagreement between experiments.

en: What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO2 reduction? A data-driven retrospective

catalysis

display_name: catalysis

cu

display_name: cu

gaussian-process

display_name: gaussian-process


en: Two-dimensional topological insulators are characterized by an insulating bulk and conductive edge states protected by the nontrivial topology of the bulk electronic structure. They remain robust against moderate disorder until Anderson localization occurs and destroys the topological phase. Interestingly, disorder can also induce a topological phase—known as a topological Anderson insulator—starting from an otherwise pristine trivial phase. While topological invariants are generally regarded as global quantities, we argue that space-resolved topological markers can act as local order parameters, revealing the role of fluctuations and correlations in the local topology under Anderson disorder and vacancies. With this perspective, we perform numerical simulations of disorder-driven topological phase transitions in the Haldane and Kane-Mele models, using supercells with both open and periodic boundary conditions. We find that short-scale fluctuations of topological markers vanish upon coarse-graining, except at the topological phase transition, where their correlation length peaks and large-scale fluctuations remain.

en: Fluctuations and correlations of local topological order parameters in disordered two-dimensional topological insulators

anderson-disorder

display_name: anderson-disorder

fluctuations-and-correlations

display_name: fluctuations-and-correlations

haldane-model

display_name: haldane-model

kane-mele-model

display_name: kane-mele-model

local-topology

display_name: local-topology

topological-order-parameters

display_name: topological-order-parameters

vacancies

display_name: vacancies


en: Ideal magnetic frustration forms the basis for the emergence of exotic quantum spin states that are entirely nonmagnetic. Such singlet spin states are the defining feature of the Shastry-Sutherland model, and of its faithful materials realization in the quantum antiferromagnet SrCu₂(BO₃)₂. To address these states on ultrafast timescales, despite their lack of any microscopic order parameter, we introduce a nonlinear magnetophononic mechanism to alter the quantum spin dynamics by driving multiple optical phonon modes coherently and simultaneously. We apply intense terahertz pulses to create a nonequilibrium modulation of the magnetic interactions that breaks the ideal frustration of SrCu₂(BO₃)₂, such that previously forbidden physics can be driven in a coherent manner. Specifically, this driving populates a purely magnetic excitation, the singlet branch of the two-triplon bound state, by resonance with the difference frequency of two pumped phonons. Our results demonstrate how light-driven phonons can be used for the ultrafast and selective manipulation of interactions in condensed matter, even at frequencies far from those of the pump spectrum, offering valuable additional capabilities for the dynamical control of quantum many-body phenomena.

en: Ultrafast frustration-breaking and magnetophononic driving of singlet excitations in a quantum magnet

magnetophononic

display_name: magnetophononic

quantum-magnetic-materials

display_name: quantum-magnetic-materials

snsf

display_name: snsf

strongly-correlated-electrons

display_name: strongly-correlated-electrons

tera-hertz-pump-and-probe

display_name: tera-hertz-pump-and-probe


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

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

machine-learning

display_name: machine-learning

nmr

display_name: nmr

resolution

display_name: resolution


en: Predicting the thermal conductivity of glasses from first principles has hitherto been a prohibitively complex problem. In fact, past works have highlighted challenges in achieving computational convergence with respect to length and/or time scales using either the established Allen-Feldman or Green-Kubo formulations, endorsing the concept that atomistic models containing thousands of atoms — thus beyond the capabilities of first-principles calculations — are needed to describe the thermal conductivity of glasses. In addition, these established formulations either neglect anharmonicity (Allen-Feldman) or miss the Bose-Einstein statistics of atomic vibrations (Green Kubo), thus leaving open the question on the relevance of these effects. Here, we present a first-principles formulation to address the thermal conductivity of glasses above the plateau, which can account comprehensively for the effects of structural disorder, anharmonicity, and quantum Bose-Einstein statistics. The protocol combines the Wigner formulation of thermal transport with convergence-acceleration techniques, and is validated in vitreous silica using both first-principles calculations and a quantum-accurate machine-learned interatomic potential. We show that models of vitreous silica containing less than 200 atoms can already reproduce the thermal conductivity in the macroscopic limit and that anharmonicity negligibly affects heat transport in vitreous silica. We discuss the microscopic quantities that determine the trend of the conductivity at high temperature, highlighting the agreement of the calculations with experiments in the temperature range above the plateau where radiative effects remain negligible (50<∼T <∼450 K).

en: Thermal conductivity of glasses above the plateau: first-principles theory and applications

atomic-vibrations

display_name: atomic-vibrations

first-principles

display_name: first-principles

thermal-transport

display_name: thermal-transport

vitreous-silica

display_name: vitreous-silica