en:
Interfacing a topological insulator (TI) with an s-wave superconductor (SC) is a promising material platform that offers the possibility to realize a topological superconductor through which Majorana-based topologically protected qubits can be engineered. In our computational study of the prototypical SC/TI interface between Nb and Bi₂Te₃, we identify the benefits and possible bottlenecks of this potential Majorana material platform. Bringing Nb in contact with the TI film induces charge doping from the SC to the TI, which shifts the Fermi level into the TI conduction band. For thick TI films, this results in band bending leading to the population of trivial TI quantum-well states at the interface. In the superconducting state, we uncover that the topological surface state experiences a sizable superconducting gap-opening at the SC/TI interface, which is furthermore robust against fluctuations of the Fermi energy. We also show that the trivial interface state is only marginally proximitized, potentially obstructing the realization of Majorana-based qubits in this material platform.
This dataset contains the data for the DFT-based calculations for interfaces between the s-wave superconductor Nb and the topological insulator Bi₂Te₃ which is discussed in the publication referenced below.

en: Dataset of proximity induced superconductivity in a topological insulator

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

superconductivity

display_name: superconductivity

topological-materials

display_name: topological-materials

topological-superconductor

display_name: topological-superconductor


$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

density-functional-theory

display_name: density-functional-theory


en: Nuclear magnetic resonance (NMR) chemical shifts are a direct probe of local atomic environments and can be used to determine the structure of solid materials. However, the substantial computational cost required to predict accurate chemical shifts is a key bottleneck for NMR crystallography. We recently introduced ShiftML, a machine-learning model of chemical shifts in molecular solids, trained on minimum-energy geometries of materials composed of C, H, N, O, and S that provides rapid chemical shift predictions with density functional theory (DFT) accuracy. Here, we extend the capabilities of ShiftML to predict chemical shifts for both finite temperature structures and more chemically diverse compounds, while retaining the same speed and accuracy. For a benchmark set of 13 molecular solids, we find a root-mean-squared error of 0.47 ppm with respect to experiment for 1H shift predictions (compared to 0.35 ppm for explicit DFT calculations), while reducing the computational cost by over four orders of magnitude.

en: A machine learning model of chemical shifts for chemically and structurally diverse molecular solids

machine-learning

display_name: machine-learning

snsf

display_name: snsf


en:
With increasing interest in Majorana physics for possible quantum bit applications, a large interest has been developed to understand the properties of the interface between a s-type superconductor and a topological insulator. Up to this point the interface analysis was mainly focused on in-situ prepared Josephson junctions, which consist of two coupled single interfaces or to ex-situ fabricated single interface devices. In our work we utilize a novel fabrication process, combining selective area growth and shadow evaporation which allows the characterization of a single in-situ fabricated Nb/(Bi0.15Sb0.85)2Te3 nano interface. The resulting high interface transparency, is apparent by a zero bias conductance increase by a factor of 1.7. Furthermore, we present a comprehensive differential conductance analysis of our single in-situ interface for various magnetic fields, temperatures and gate voltages. Additionally, density functional theory calculations of the superconductor/topological insulator interface are performed in order to explain the peak-like shape of our differential conductance spectra and the origin of the observed smearing of conductance features.

This dataset contains the DFT and experimental raw data discussed in the associated publication.

en: Single in-situ interface characterization composed of niobium and a selectively grown topological insulator nanoribbon

experiment

display_name: experiment

majorana

display_name: majorana

transport

display_name: transport


en: Single in-situ interface characterization composed of niobium and a selectively grown (Bi1-xSbx)2Te3 topological insulator nanoribbon


en: Characterization of single in situ prepared interfaces composed of niobium and a selectively-grown (Bi1-xSbx)2Te3 topological insulator nanoribbon


en: The accurate description of the structural and thermodynamic properties of ferroelectrics has been one of the most remarkable achievements of Density Functional Theory (DFT). However, running large simulation cells with DFT is computationally demanding, while simulations of small cells are often plagued with non-physical effects that are a consequence of the system's finite size. Therefore, one is often forced to use empirical models that describe the physics of the material in terms of effective interaction terms, that are fitted using the results from DFT, to perform simulations that do not suffer from finite size effects. In this study we use a machine-learning (ML) potential trained on DFT, in combination with accelerated sampling techniques, to converge the thermodynamic properties of Barium Titanate (BTO) with first-principles accuracy and a full atomistic description. Our results indicate that the predicted Curie temperature depends strongly on the choice of DFT functional and system size, due to the presence of emergent long-range directional correlations in the local dipole fluctuations. Our findings demonstrate how the combination of ML models and traditional bottom-up modeling allow one to investigate emergent phenomena with the accuracy of first-principles calculations and the large size and time scales afforded by empirical models.

en: Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling

dielectric-correlations

display_name: dielectric-correlations

ferroelectrics

display_name: ferroelectrics

finite-size-effects

display_name: finite-size-effects

hybrid-dft-ml

display_name: hybrid-dft-ml

machine-learning-potentials

display_name: machine-learning-potentials

marvel

display_name: marvel

metadynamics

display_name: metadynamics

phase-transitions

display_name: phase-transitions

sinergia

display_name: sinergia


en:
Functionalizing Silicon Nanowires (SiNWs) through covalent attachment of organic molecules offers diverse advantages, including surface passivation, introduction of new functionalities, and enhanced material performance in applications like electronic devices and biosensors. Given the wide range of available functional molecules, systematic large-scale screening is crucial. Therefore, we developed an automated computational workflow using Python scripts in conjunction with the AiiDa framework to explore structural configurations of functional molecules adsorbed onto silicon surfaces. This workflow generates multiple adhesion configurations corresponding to different binding orientations using surface and functional molecule structures as inputs.
This dataset contains data related to the structural optimization of molecules with single, double, and triple carbon-carbon bonds attached to the nanowire surface in various adhesion configurations. We describe the chemisorption on SiNWs using the slab models for the Si facets since our reference are samples with diameters of SiNWs around 50 nm, while the quantum confinement effects are important for diameters below 10 nm.  For each configuration, structural characterization was conducted by calculating quantities including the bond distance between the two carbons closest to the surface and their respective bond angle relative to the z-axis, the carbon-silicon bond distance and its respective bond angle relative to the z-axis, along with the molecule's rotation angle in the xy plane. The values obtained are summarized in the main folder. The version v1 of dataset contains data related to the Si(111) surface and alkanes, alkenes, and alkynes with lengths from C2 to C10. The dataset will be extended to characterize the Si110 surface of the nanowire and explore longer molecules ranging from C12 to C18. For each system the most stable configuration will be identified, and the analysis of the electronic properties will be conducted.

en: Density functional theory study of silicon nanowires functionalized by grafting organic molecules

aliphatic-molecules

display_name: aliphatic-molecules

dft

display_name: dft

nanowires

display_name: nanowires

silicon

display_name: silicon

surface-adsorption

display_name: surface-adsorption


en:
We present a sulfur K-edge X-ray absorption near-edge structure (XANES) database of 18 crystalline and 48 amorphous Lithium-Phosphorous-Sulfur (LPS) compounds. The database contains a total of 2681 XANES spectra of symmetrically inequivalent absorbing S sites. Structures were taken from Materials Cloud entry 2022.17 (archive.materialscloud.org/record/2022.17) and were originally generated by systematically removing Li, P and S atoms from known crystal structures using an evolutionary algorithm and an artificial neural network based interatomic potential. The details of this procedure can be found in Guo et al. (see references below). From this data set, low-energy structures were selected for spectral simulations. The excited electron and core hole method as implemented in VASP 6.2.1 was used to compute the XANES spectra for each symmetrically inequivalent Sulfur atom. The details of the VASP simulations can be found in the associated manuscript.

Acknowledgements: We acknowledge financial support by the U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, Contract No. DE-SC0012704. These results used the computational resources of the Center for Functional Nanomaterials and the Scientific Data and Computing Center, a component of the Computational Science Initiative, at Brookhaven National Laboratory under the Contract No. DE-SC0012704. We also acknowledge computing resources from Columbia University's Shared Research Computing Facility project, which is supported by NIH Research Facility Improvement Grant 1G20RR030893-01, and associated funds from the New York State Empire State Development, Division of Science Technology and Innovation (NYSTAR) Contract C090171, both awarded April 15, 2010.

en: Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes

amorphous-lps

display_name: amorphous-lps

lps

display_name: lps

vasp

display_name: vasp

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


en: Charge transport plays a crucial role in manifold potential applications of two-dimensional materials, including field effect transistors, solar cells, and transparent conductors. At most operating temperatures, charge transport is hindered by scattering of carriers by lattice vibrations. Assessing the intrinsic phonon-limited carrier mobility is thus of paramount importance to identify promising candidates for next-generation devices. Here we provide a framework to efficiently compute the  drift and Hall carrier mobility of two-dimensional materials through the Boltzmann transport equation by relying on a Fourier-Wannier interpolation. Building on a recent formulation of long-range contributions to dynamical matrices and phonon dispersions [Phys. Rev. X 11, 041027 (2021)], we extend the approach to electron-phonon coupling including the effect of dynamical dipoles and quadrupoles. We identify an unprecedented contribution associated with the Berry connection that is crucial to preserve the Wannier-gauge covariance of the theory. This contribution is not specific to 2D crystals, but also concerns the 3D case, as we demonstrate via an application to bulk SrO. We showcase our method on a wide selection of relevant monolayers ranging from SnS₂ to MoS₂, graphene, BN, InSe, and phosphorene. We also discover a non-trivial temperature evolution of the Hall hole mobility in InSe whereby the mobility increases with temperature above 150~K due to the mexican-hat electronic structure of the InSe valence bands. Overall, we find that dynamical quadrupoles are essential and can impact the carrier mobility in excess of 75%.

en: Long-range electrostatic contribution to the electron-phonon couplings and mobilities of two-dimensional and bulk materials

ab-initio

display_name: ab-initio

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

transport

display_name: transport


materialscloud

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

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

marvel

display_name: marvel


en: Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We successfully interface off-the-shelf battery cycling hardware with the computational workflow management software AiiDA, allowing us to control experiments, while ensuring trust in the data by tracking its provenance. We design user interfaces compatible with this tooling, which span the inventory, experiment design, and result analysis stages. Other features, including monitoring of workflows and import of externally generated and legacy data are also implemented. Finally, the full software stack required for this work is made available in a set of open-source packages.

materialscloud

en: Materials Cloud

en: A bridge between trust and control: Computational workflows meet automated battery cycling

aurora

display_name: aurora

battery

display_name: battery

big-map

display_name: big-map

cycling

display_name: cycling

premise

display_name: premise

robot

display_name: robot

workflow

display_name: workflow


en: This database contains the neural network potential (NNP) model and training data for aqueous ZnCl₂ solutions from 1 m to 30 m. The NNP model can be used to compute total energies and atomic forces, with one of its major applications being large-scale molecular dynamics (MD) simulations. The model was trained using DeePMD-kit v2.2.1, with training data generated through an active learning approach implemented in DP-GEN. The energies and forces in the training set were obtained from density functional theory (DFT) calculations using the SCAN exchange-correlation functional performed using Quantum ESPRESSO. Further details on the ab initio calculation procedures and model training methodology are available in the associated manuscript (see reference below).

en: Resolving the solvation structure and transport properties of aqueous zinc electrolytes from salt-in-water to water-in-salt using neural network potential

envi

en: Environment

deepmd

display_name: deepmd

neural-network-potential

display_name: neural-network-potential

water

display_name: water

water-in-salt

display_name: water-in-salt


en: Organic solvents are widely used in polymer synthesis, despite their use lengthening purification steps and generating chemical waste. All-dry synthesis techniques, such as initiated Chemical Vapour Deposition (iCVD) polymerization, eliminate the use of solvents, however, only a narrow palette of material properties is accessible. Inspired by the principles of solvent engineering in solution synthesis, we report a strategy to broaden this palette by vapour-phase complexing (namely, vapour-phase solvation) mediated by hydrogen-bonding. Broad ranges of polymer chain length, as well as the mechanical strength and variety of film surface morphology are demonstrating using this strategy. We further achieve an unprecedented solvation modality; more specifically, interfacial solvation. The molecular interactions, locations of solvation, and kinetics of the coupled solvation-adsorption-polymerization process are investigated using molecular dynamic simulations and experimental validation of a theoretical kinetic model. The strategy can be applied to various methacrylate and vinyl monomers. Solvation in all-dry polymerization offers a new degree of freedom in polymer design and synthesis with improved environmental benignness, pointing to accelerated discovery of polymer thin films by simply introducing active solvents in the vapour phase. Here we provided the code for data processing to the result of the molecular dynamic simulation.

en: Engineering solvation in initiated Chemical Vapor Deposition (iCVD) for process-property control

icvd

display_name: icvd

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

polymer-thin-film

display_name: polymer-thin-film


en: Mn₅Si₃ displays an unusual and complex magnetic ground state, which is considered to be the origin of the anomalous transport and thermodynamic properties that it exhibits. We report the magnetic exchange couplings of the noncollinear antiferromagnetic phase of Mn₅Si₃ using inelastic neutron scattering measurements and density functional theory calculations. We determine the ground-state spin configuration and compute its magnon dispersion relations which are in good agreement with the ones obtained experimentally. Furthermore, we investigate the evolution of the spin texture under the application of an external magnetic field to demonstrate theoretically the multiple field-induced phase transitions that Mn₅Si₃ undergoes. Finally, we model the stability of some of the material's magnetic moments under a magnetic field and we find that very susceptible magnetic moments in a frustrated arrangement can be tuned by the field. This data set contains the data relevant to perform the DFT calculations with the JuKKR code, the spin dynamics simulations with the Spirit code, the spin-wave calculations with the SWIS code, and the neutron scattering experimental data.

en: Complex magnetic structure and spin waves of the noncollinear antiferromagnet Mn₅Si₃

antiferromagnetism

display_name: antiferromagnetism

frustration

display_name: frustration

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

intersect

display_name: intersect

noncollinear-magnetism

display_name: noncollinear-magnetism

spin-waves

display_name: spin-waves


marvel

display_name: marvel


en: Fatigue failure of metallic structures is of great concern to industrial applications. A material will not be able to practically useful if it is prone to fatigue failure. To take the advantage of lately emerged high entropy alloys (HEAs) for designing novel fatigue-resistant alloys, we compiled a fatigue database of HEAs from the literature reported till the yearend of 2021. The database is subdivided into three categories, i.e., low-cycle fatigue (LCF), high-cycle fatigue (HCF), and fatigue crack growth rate (FCGR), which contains 15, 23, and 28 distinct data records, respectively. Each data record in any of three categories is characteristic of a summary, which is comprised of alloy composition, key fatigue properties, and additional information influential to or interrelated with fatigue (e.g., material processing history, phase constitution, grain size, uniaxial tensile properties, and fatigue testing conditions), and an individual dataset, which makes up the original fatigue testing curve.

en: Fatigue database of high entropy alloys

fatigue-crack-growth-rate

display_name: fatigue-crack-growth-rate

high-cycle-fatigue

display_name: high-cycle-fatigue

high-entropy-alloy

display_name: high-entropy-alloy

low-cycle-fatigue

display_name: low-cycle-fatigue


fatigue-database-of-high-entropy-alloys

title_for_slug: fatigue-database-of-high-entropy-alloys


en: Co-based catalysts are promising candidates to replace the Ir/Ru-based oxides for the oxygen evolution reaction (OER) in an acidic environment. However, both the reaction mechanism and the active species in an acidic environment remain unclear. In this study, by combining surface-sensitive soft X-ray adsorption spectroscopy (sXAS) characterization with electrochemical analysis, we unveil the truly active Co species for acidic OER. Surfaces composed of only high-spin CoII are found to be not active due to their unfavorable deprotonation to form CoIII-OH species. In contrast, we show that the presence of low-spin CoIII is essential to promote surface reconstruction and catalysis of OER. The correlation between OER activity and Co oxidation/spin state represents a breakthrough in the structure-activity relationship of Co-based catalysts for acidic OER, which interestingly does not hold in the alkaline and neutral environments. These findings not only help to design efficient acidic OER catalysts, but also deepen the understanding of the reaction mechanism.

en: Unveiling the pH-dependent structure-activity relationship of Co-based catalysts toward oxygen evolution reaction

acidic-oxygen-evolution

display_name: acidic-oxygen-evolution

co-oxidation-state

display_name: co-oxidation-state

cobalt-based-catalyst

display_name: cobalt-based-catalyst

spin-state

display_name: spin-state

structure-activity-relationship

display_name: structure-activity-relationship


en:
We use a combination of symmetry analysis, phenomenological modeling, and first-principles density functional theory to explore the interplay between the magnetic ground state and the detailed atomic structure in the hexagonal rare-earth manganites. We find that the magnetic ordering is sensitive to a breathing mode distortion of the Mn and O ions in the ab plane, which is described by the K1 mode of the high-symmetry structure. Our density functional calculations of the magnetic interactions indicate that this mode particularly affects the single-ion anisotropy and the interplanar symmetric exchanges. By extracting the parameters of a magnetic model Hamiltonian from our first-principles results, we develop a phase diagram to describe the magnetic structure as a function of the anisotropy and exchange interactions. This in turn allows us to explain the dependence of the magnetic ground state on the identity of the rare-earth ion and on the K1 mode.
The attached files contain VASP input files and bash scripts for running all the DFT calculations in the manuscript.

en: Influence of the triangular Mn-O breathing mode on magnetic ordering in multiferroic hexagonal manganites

ab-initio

display_name: ab-initio

dftu

display_name: dftu

erc

display_name: erc


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:
Functionalizing Silicon Nanowires (SiNWs) through covalent attachment of organic molecules offers diverse advantages, including surface passivation, introduction of new functionalities, and enhanced material performance in applications like electronic devices and biosensors. Given the wide range of available functional molecules, systematic large-scale screening is crucial. Therefore, we developed an automated computational workflow using Python scripts in conjunction with the AiiDa framework to explore structural configurations of functional molecules adsorbed onto silicon surfaces. This workflow generates multiple adhesion configurations corresponding to different binding orientations using surface and functional molecule structures as inputs.
This dataset contains data related to the structural optimization of molecules with single, double, and triple carbon-carbon bonds attached to the nanowire surface in various adhesion configurations. We describe the chemisorption on SiNWs using the slab models for the Si facets since our reference are samples with diameters of SiNWs around 50 nm, while the quantum confinement effects are important for diameters below 10 nm.  For each configuration, structural characterization was conducted by calculating quantities including the bond distance between the two carbons closest to the surface and their respective bond angle relative to the z-axis, the carbon-silicon bond distance and its respective bond angle relative to the z-axis, along with the molecule's rotation angle in the xy plane. The values obtained are summarized in the main folder. The version v1 of dataset contains data related to the Si(111) surface and alkanes, alkenes, and alkynes with lengths from C2 to C10. This version (v2) extends the dataset to moieties from C12 to C18. The dataset will be extended to characterize the Si(110) surface of the nanowire. For each system the most stable configuration will be identified, and the analysis of the electronic properties will be conducted.

en: Density functional theory study of silicon nanowires functionalized by grafting organic molecules

aliphatic-molecules

display_name: aliphatic-molecules

dft

display_name: dft

nanowires

display_name: nanowires

silicon

display_name: silicon

surface-adsorption

display_name: surface-adsorption


en:
Molecular dynamics simulations provide a wealth of data whose in-depth analysis can be computationally demanding and, sometimes, even unnecessary. Dimensionality reduction techniques are thus routinely employed to simplify and improve the interpretation of trajectories focusing on specific subsets of the system's atoms; a key issue, in this context, is to determine the optimal resolution level, i.e. the smallest number of atoms needed to preserve the largest information content from the full atomistic trajectory. Here, we introduce the protein optimal resolution identification method (PROPRE), an unsupervised approach built on information theory principles that determines the smallest number of atoms that need to be retained to attain a synthetic yet informative description of a protein. By applying the method to a protein dataset and two particular case studies, we show that this number is typically between 1.5 and 2 times the number of residues in a protein; nonetheless, the degree of conformational variability of the system influences the specific number importantly, in that a broader range of large-scale conformations correlates with fewer retained sites. The PROPRE method is implemented in efficient and user-friendly python scripts, which are made available for download on a github repository.
Here, the raw data employed for the preparation of the associated manuscript are made available.

materialscloud

en: Materials Cloud

en: The optimal resolution level of a protein is an emergent property of its structure and dynamics

coarse-graining

display_name: coarse-graining

model-resolution

display_name: model-resolution

molecular-dynamics

display_name: molecular-dynamics


en: Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We successfully interface off-the-shelf battery cycling hardware with the computational workflow management software AiiDA, allowing us to control experiments, while ensuring trust in the data by tracking its provenance. We design user interfaces compatible with this tooling, which span the inventory, experiment design, and result analysis stages. Other features, including monitoring of workflows and import of externally generated and legacy data are also implemented. Finally, the full software stack required for this work is made available in a set of open-source packages.

en: A bridge between trust and control: Computational workflows meet automated battery cycling

envi

en: Environment

aurora

display_name: aurora

battery

display_name: battery

big-map

display_name: big-map

cycling

display_name: cycling

marvel

display_name: marvel

premise

display_name: premise

robot

display_name: robot

workflow

display_name: workflow


en: Understanding the phononic origin of the infrared dielectric properties of yttria (Y₂O₃) and other rare-earth sesquioxides (RE₂O₃) is a fundamental task in the search of appropriate RE₂O₃ materials that serve particular infrared optical applications. We herein investigate the infrared dielectric properties of RE₂O₃ (RE = Y, Gd, Ho, Lu) using DFT-based phonon calculations and Lorentz oscillator model. The abundant IR-active optical phonon modes that are available for effective absorption of photons result in high reflectance of RE₂O₃, among which four IR-active modes originated from large distortions of REO₆ octahedra are found to contribute dominantly to the phonon dielectric constants. Particularly, the present calculation method by considering one-phonon absorption process is demonstrated with good reliability in predicting the infrared dielectric parameters of RE₂O₃ at the far-infrared as well as the vicinity of mid-infrared region, and the potential cutoff frequency/wavelength of its applicability is disclosed as characterized by the maximum frequency of IR-active longitudinal phonon modes. The results deepen the understanding on infrared dielectric properties of RE₂O₃, and aid the computational design of materials with appropriate infrared properties.

en: Phononic origin of the infrared dielectric properties of RE₂O₃ (RE = Y, Gd, Ho, Lu) compounds

first-principles

display_name: first-principles

infrared

display_name: infrared

phonon

display_name: phonon

sesquioxides

display_name: sesquioxides


en:
Functionalizing Silicon Nanowires (SiNWs) through covalent attachment of organic molecules offers diverse advantages, including surface passivation, introduction of new functionalities, and enhanced material performance in applications like electronic devices and biosensors. Given the wide range of available functional molecules, systematic large-scale screening is crucial. Therefore, we developed an automated computational workflow using Python scripts in conjunction with the AiiDa framework to explore structural configurations of functional molecules adsorbed onto silicon surfaces. This workflow generates multiple adhesion configurations corresponding to different binding orientations using surface and functional molecule structures as inputs.
This dataset contains data related to the structural optimization of molecules with single, double, and triple carbon-carbon bonds attached to the nanowire surface in various adhesion configurations. We describe the chemisorption on SiNWs using the slab models for the Si facets since our reference are samples with diameters of SiNWs around 50 nm, while the quantum confinement effects are important for diameters below 10 nm.  For each configuration, structural characterization was conducted by calculating quantities including the bond distance between the two carbons closest to the surface and their respective bond angle relative to the z-axis, the carbon-silicon bond distance and its respective bond angle relative to the z-axis, along with the molecule's rotation angle in the xy plane. The values obtained are summarized in the main folder. The version v1 of dataset contains data related to the Si(111) surface and alkanes, alkenes, and alkynes with lengths from C2 to C10. The version v2 extends the dataset to moieties from C12 to C18. This version (v3) extends the dataset with new configurations for moieties from C2 to C18. The dataset will be extended to characterize the Si(110) surface of the nanowire. For each system the most stable configuration will be identified, and the analysis of the electronic properties will be conducted.

aliphatic-molecules

display_name: aliphatic-molecules

dft

display_name: dft

nanowires

display_name: nanowires

silicon

display_name: silicon


en:
Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and singlet exciton size for the discovery of singlet fission materials. We show that uncertainty in the model predictions can control how far the genetic optimization moves away from previously known molecules.
Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom-rich mesoionic compounds as acceptors for charge-transfer mediated singlet fission. When included in larger conjugated donor-acceptor systems, these units exhibit strong localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells. As the proposed candidates are composed of fragments from synthesized molecules, they are likely synthetically accessible.

en: Inverse design of singlet fission materials with uncertainty-controlled genetic optimization

genetic-algorithm

display_name: genetic-algorithm

inverse-design

display_name: inverse-design

machine-learning

display_name: machine-learning

singlet-fission

display_name: singlet-fission

uncertainty-quantification

display_name: uncertainty-quantification


en: The AV₃Sb₅ kagome superconductors series are of intense interest due to their diverse and intricate properties. The breaking of time-reversal symmetry (TRS) in the normal state of these superconductors stands as a significant feature, yet the extent to which this effect can be tuned remains uncertain. Here, we employ a unique low-energy muon spin rotation technique combined with local field numerical analysis to study the TRS breaking response as a function of depth from the surface in single crystals of RbV₃Sb₅ with charge order and Cs(V0.86Ta0.14)₃Sb₅ without charge order. In the bulk (specifically above 30 nm from the sur face) of RbV₃Sb₅, we have detected a notable increase in the internal field width experienced by the muon ensemble. This increase occurs within the charge ordered state. Intriguingly, the muon spin relaxation rate is significantly enhanced near the surface of RbV₃Sb₅ (specifically within a depth range of 30-40 nm from the surface), and this effect commences at temperatures significantly higher than the onset of charge order. Conversely, in Cs(V0.86Ta0.14)₃Sb₅, which lacks charge order, we do not observe a similar enhancement in the internal field width, neither in the bulk nor at the surface. These observations indicate a strong connection between charge order and TRS breaking on one hand, and on the other hand, suggest that TRS breaking can be manipulated to occur prior to long-range charge order. This research offers compelling evidence for depth-dependent magnetism in AV₃Sb₅ superconductors in the presence of charge order. Such findings are likely to illuminate the intricate microscopic mechanisms that underpin the TRS breaking phenomena in these materials.

en: Depth-dependent time reversal symmetry breaking response in the charge-ordered kagome material RbV₃Sb₅

kagome-metal

display_name: kagome-metal

muon-spin-spectroscopy

display_name: muon-spin-spectroscopy


en: Singlet fission (SF) is a two-step process in which a singlet splits into two triplets throughout the so-called correlated triplet-pair (1TT) state. Intramolecular SF (iSF) materials in particular, have attracted growing interest as they can be easily implemented in single junction solar cells and boost their power conversion efficiency. Still, the potential of iSF materials such as polymers and oligomers for photovoltaic applications has been partially hindered by their ability to go beyond the 1TT intermediate and generate free triplets, which mechanism remains poorly understood. In this work, the main aspects governing the 1TT dissociation in donor-acceptor copolymers and the key features that optimize this process are exposed. First, we show that both thermodynamics and kinetics play a crucial role in the intramolecular triplet-pair separation and second, we uncover the inherent flexibility of the donor unit as the fundamental ingredient to optimize them simultaneously. Overall, these results provide a better understanding of the intramolecular 1TT dissociation process and establish a new paradigm for the development of novel iSF active materials.

en: Optimizing the thermodynamics and kinetics of the triplet-pair dissociation in donor-acceptor copolymers for intramolecular singlet fission

donor-acceptor-copolymers

display_name: donor-acceptor-copolymers

electronic-structure

display_name: electronic-structure

epfl

display_name: epfl

marie-curie-fellowship

display_name: marie-curie-fellowship


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: Extensive machine-learning assisted research has been dedicated to predicting band gaps for perovskites, driven by their immense potential in photovoltaics. Yet, the effectiveness is often hampered by the lack of high-quality band-gap datasets, particularly for perovskites involving d orbitals. In this work, we consistently calculate a large dataset of band gaps with a high level of accuracy, which is rigorously validated by experimental and state-of-the-art GW band gaps. Leveraging this achievement, our machine-learning derived descriptor exhibits exceptional universality and robustness, proving effectiveness not only for single and double, halide and oxide perovskites regardless of the underlying atomic structures, but also for hybrid organic-inorganic perovskites. With this approach, we comprehensively explore up to 15,659 materials, unveiling 14 unreported lead-free perovskites with suitable band gaps for photovoltaics. Notably, MASnBr₃, FA₂SnGeBr₆, MA₂AuAuBr₆, FA₂AuAuBr₆, FA₂InBiCl₆, FA₂InBiBr₆, and Ba₂InBiO₆ stand out with direct band gaps, small effective masses, low exciton binding energies, and high stabilities.

en: High-quality data enabling universality of band-gap descriptor and discovery of photovoltaic perovskites

band-gap-descriptor

display_name: band-gap-descriptor

high-quality-dataset

display_name: high-quality-dataset

hybrid-functional

display_name: hybrid-functional

photoboltaic-perovskites

display_name: photoboltaic-perovskites


materialscloud

en: Materials Cloud


en: Dimensionality provides a clear fingerprint on the dispersion of infrared-active, polar-optical phonons. For these phonons, the local dipoles parametrized by the Born effective charges drive the LO-TO splitting of bulk materials; this splitting actually breaks down in two-dimensional materials. Here, we develop the theory for one-dimensional (1D) systems -nanowires, nanotubes, and atomic and polymeric chains. Combining an analytical model with the implementation of density-functional perturbation theory in 1D boundary conditions, we show that the dielectric splitting in the dispersion relations collapses as x²log(x) at the zone center. The dielectric properties and the radius of the 1D materials are linked by the present work to these red shifts, opening infrared and Raman characterization avenues.

en: Infrared-active phonons in one-dimensional materials and their spectroscopic signatures

ab-initio

display_name: ab-initio

density-functional-theory

display_name: density-functional-theory

marvel

display_name: marvel

one-dimensional-materials

display_name: one-dimensional-materials

phonons

display_name: phonons

raman

display_name: raman


en: The reliable ab-initio description of strongly correlated materials is a long-sought capability in condensed matter physics. The GW+EDMFT method is a promising scheme, which provides a self-consistent description of correlations and screening, and does not require user-provided parameters. In order to test the reliability of this approach we apply it to the experimentally well characterized perovskite compound Ca₂RuO₄, in which a temperature-dependent structural deformation drives a paramagnetic metal-insulator transition. Our results demonstrate that the nonlocal polarization and self-energy components introduced by GW are essential for setting the correct balance between interactions and bandwidths, and that the GW+EDMFT scheme produces remarkably accurate predictions of the electronic properties of this strongly correlated material.

en: Fully ab-initio electronic structure of Ca₂RuO₄

first-principles

display_name: first-principles

gw-method

display_name: gw-method

snsf

display_name: snsf

strongly-correlated-systems

display_name: strongly-correlated-systems


envi

en: Environment

electronic-structure

display_name: electronic-structure


en: We perform extensive molecular dynamics simulations of an ensemble of realistic microgel particles in swollen conditions in a wide range of packing fractions ζ. We compare neutral and charged microgels, where we consider charge distribution adherent to experimental conditions. Through a detailed analysis of singleparticle behavior, we are able to identify the different regimes occurring upon increasing concentration: from shrinking to deformation and interpenetration, always connecting our findings with available experimental observations. We then link these single-particle features with the collective behavior of the suspension, finding evidence of a structural reentrance that has no counterpart in the dynamics. Hence, while the maximum of the radial distribution function displays a nonmonotonic behavior with increasing ζ, the dynamics, quantified by the microgels' mean-squared displacement, always slows down. This behavior, at odds with the simple Hertzian model, can be described by a phenomenological multi-Hertzian model, which takes into account the enhanced internal stiffness of the core. However, also this model fails when deformation enters into play, whereby more realistic many-body models are required. Thanks to our analysis, we are able to unveil the key physical mechanisms, shrinking and deformation, giving rise to the structural reentrance that holds up to very large packing fractions. We further identify key similarities and differences between neutral and charged microgels, for which we detect at high enough ζ the fusion of charged shells, previously invoked to explain key experimental findings, and responsible for the structural reentrance. Overall, our study establishes a powerful framework to uncover the physics of microgel suspensions, paving the way to tackle different regimes, e.g., high temperature, and internal architectures, such as for hollow and ultralow-cross-linked microgels, where experimental evidence is still limited.

en: Numerical study of neutral and charged microgel suspensions: from single-particle to collective behavior

collective-behavior

display_name: collective-behavior

elasticity

display_name: elasticity

microgels

display_name: microgels


en: Melt post-condensation, thermal, and thermo-oxidative degradation of a cyclo-aliphatic polyamide were studied through time-resolved rheometry (TRR). The implemented TRR elucidates structural changes occurring during two concurrent phenomena, namely melt post-condensation and thermal/thermo-oxidative degradation, during the time sweep in a parallel plate rheometer. TRR measurements were conducted on neat polyamide under nitrogen (inert/non-oxidative) and air (oxidative) environment at 3 % strain amplitude and a range of frequencies between 0.1 and 100 rad/sec for two hours. At temperatures of 260, 270, and 275 °C, a dual-stage time-dependent growth in viscoelastic properties was observed under an oxidative environment. Thermo-oxidative degradation of polyamide melt 270 °C was shown to occur from the exposed sample edge, continuing inwards, effectively reducing the radius of the unoxidized polymer melt by 6.4 %. A modeling approach using MATLAB is presented to interpret and differentiate the rheological behavior of edge oxidized gel and bulk polymer within the volume of the sample melt. The code removes the effects of the strain limiter and the edge gelation from experimental measurements in predicting the material properties.

en: Predicting the influence of edge oxidation in parallel-plate rheometry

edge-oxidation

display_name: edge-oxidation

experimental

display_name: experimental

matlab

display_name: matlab


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

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

phase-field-spinodal-decomposition

display_name: phase-field-spinodal-decomposition


en: In stark contrast to inorganic quantum wells, hybrid quantum wells based on metal-organic semiconductors are characterized by relatively soft lattices. In the latter, excitonic states are deeply affected by coupling with optical phonons. A detailed understanding of the lattice role in exciton dynamics is therefore essential to improve the optoelectronic performance of these materials. Beyond 2D metal halide perovskites, layered metal-organic chalcogenides (MOCs) are an air-stable, underexplored material class hosting complex excitonic phenomena that could be exploited as photodetectors, light emitting devices and ultrafast photoswitches. Here, we elucidate the role of lattice phonons in the optical transitions at different temperatures in the prototypical MOC [AgSePh]∞. We detect coherent exciton-phonon coupling by pump-probe transient absorption spectroscopy, dominated by a Fröhlich interaction with optical phonons at 7 and 12 meV. Through a concerted use of ab initio calculations, linear and Raman spectroscopies, we reveal a distinct phonon anharmonicity clearly manifests in a non-trivial temperature-dependent Stokes shift, deeply impacting the excitonic photoluminescence. The temperature-dependent absorption and photoluminesce data further reveals a Huang-Rhys parameter of about 1.7 suggesting a strong exciton-phonon coupling in both optical transitions. Finally, angle-resolved Raman spectroscopy hints at a distinct anisotropy in the phonon coupling process. Our results indicate shared characters in the exciton-phonon interaction for MOCs and the 2D perovskites, either implying shared polar character of the inorganic sublattices and/or a more important role of the hybrid quantum well superlattice structure.

en: Anharmonic exciton-phonon coupling in metal-organic chalcogenides hybrid quantum wells

exciton-phonon-coupling

display_name: exciton-phonon-coupling

metal-organic-chalcogenides

display_name: metal-organic-chalcogenides

raman-spectroscopy

display_name: raman-spectroscopy


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

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

auxiliary-density-matrix-method

display_name: auxiliary-density-matrix-method

high-performance-computing

display_name: high-performance-computing

hybrid-dft

display_name: hybrid-dft

k-point-sampling

display_name: k-point-sampling

method-development

display_name: method-development

periodic-hartree-fock

display_name: periodic-hartree-fock


materialscloud

en: Materials Cloud


en: We present the NaviCatGA package, a versatile genetic algorithm capable of optimizing molecular catalyst structures using well-suited fitness functions to achieve a set of targeted properties. The flexibility and generality of this tool are demonstrated with two examples: i) Ligand optimization and exploration for Ni-catalyzed aryl-ether cleavage manipulating SMILES and using a fitness function derived from molecular volcano plots, ii) multiobjective (i.e., activity/selectivity) optimization of bipyridine N.N'-dioxide Lewis basic organocatalysts for the asymmetric propargylation of benzaldehyde from 3D molecular fragments. We show that evolutionary optimization, enabled by NaviCatGA, is an efficient way of accelerating catalyst discovery that bypasses combinatorial scaling issues and incorporates compelling chemical constraints.

en: Genetic optimization of homogeneous catalysts

catalysis

display_name: catalysis

homogeneous-catalysis

display_name: homogeneous-catalysis

optimization

display_name: optimization

organocatalysis

display_name: organocatalysis

volcano-plot

display_name: volcano-plot


en:
The Materials Cloud three-dimensional database is a curated set of relaxed three-dimensional crystal structures based on raw CIF data taken from the external experimental databases MPDS, COD and ICSD. The raw CIF data have been imported, cleaned and parsed into a crystal structure; their ground-state has been computed using the SIRIUS-enabled pw.x code of the Quantum ESPRESSO distribution, and tight tolerance criteria for the calculations using the SSSP protocols.

This entire procedure is encoded into an AiiDA workflow which automates the process while keeping full data provenance. Here, since the original source data of the ICSD and MPDS databases are copyrighted, only the provenance of the final SCF calculation on the relaxed structures can be made publicly available.

The MC3D ID numbers come from a list of unique "parent" stoichiometric structures that has been created and curated from a collection of these experimental databases. Once a parent structure has been optimized using density-functional theory, it is made public and added to the online Discover section of the Materials Cloud (as mentioned, copyright might prevent publishing the original parent). Note that since not all structures have been calculated, some ID numbers are missing from the public version of the database. The full ID of each structure also contains as an appended modifier the functional that was used in the calculations. Since the ID number points to the same unique parent, mc3d-1234/pbe and mc3d-1234/pbesol have the same starting point, but have been then relaxed according to their respective functionals.

en: Materials Cloud three-dimensional crystals database (MC3D)

aiida

display_name: aiida

database

display_name: database

density-functional-theory

display_name: density-functional-theory

marvelhpc

display_name: marvelhpc

max

display_name: max

pasc

display_name: pasc

prace

display_name: prace


en: Prismatic slip of the screw  dislocation in magnesium at temperatures ≳ 150 K is understood to be governed by double-cross-slip of the stable basal screw through the unstable prism screw and back to the basal screw, with the activation energy controlled by the formation energy of two basal-basal kinks. However, atomistic studies of the double-kink process predict activation energies roughly twice those derived experimentally. Here, a new mechanism of prism glide is proposed, analyzed theoretically, and demonstrated qualitatively and quantitatively via direct molecular dynamics (MD) simulations. The new mechanism is intrinsically 3d, and involves the nucleation of a single kink at the junction where a 3d prismatic dislocation loop transitions from the basal screw segment to non-screw prismatic character. The relevant kink energies are calculated using recently-developed Neural-Network Potentials (NNPs) for Mg that show good agreement versus DFT for basal and prism  dislocations, enabling a parameter-free analytic model for the activation barrier. Direct MD simulations show both operation of the precise proposed mechanism and a stress-dependent activation barrier that agrees reasonably with the analytic model. Predictions of dislocation velocity compare very well with in-situ TEM data, and macroscopic strength versus temperature can also be understood. Overall, the new intrinsically 3d mechanism for dislocation glide due to single kink nucleation rather than double-kink nucleation explains key features of the prismatic slip in Mg and may have broader applicability in other metals where kink nucleation processes control thermally-activated flow.

en: Mechanism of thermally-activated prismatic slip in Mg

envi

en: Environment

magnesium

display_name: magnesium

marvel

display_name: marvel

neural-network-potential

display_name: neural-network-potential

prismatic-slip

display_name: prismatic-slip


en: Weyl nodes can be classified into zero-dimensional (0D) Weyl points, 1D Weyl nodal lines, and 2D Weyl nodal surfaces (WNS), which possess finite Chern numbers. Up to date, the largest Chern number of WPs identified in Weyl semimetals is 4, which is thought to be a maximal value for linearly crossing points in solids. On the other hand, whether the Chern numbers of nonzero-dimensional linear crossing Weyl nodal objects have one upper limit is still an open question. In this work, combining angle-resolved photoemission spectroscopy with density-functional theory calculations, we show that the chiral crystal AlPt hosts a cube-shaped charged WNS which is formed by the linear crossings of two singly degenerate bands. Different from conventional Weyl nodes, the cube-shaped nodal surface in AlPt is enforced by nonsymmorphic chiral symmetries and time-reversal symmetry rather than accidental band crossings, and it possesses a giant Chern number |C|=26. Moreover, our results and analysis prove that there is no upper limit for the Chern numbers of such kind of 2D Weyl nodal object. This record includes the data in the related paper Phys. Rev. B 105, 115118 – Published 14 March 2022.

en: Giant Chern number of a Weyl nodal surface without upper limit

giant-chern-number

display_name: giant-chern-number

unpaired-weyl-point

display_name: unpaired-weyl-point

weyl-nodal-surface

display_name: weyl-nodal-surface

weyl-nodal-wall

display_name: weyl-nodal-wall


en: Magnetic materials can display many solutions to the electronic-structure problem, corresponding to different local or global minima of the energy functional. In Hartree-Fock or density-functional theory different single-determinant solutions lead to different magnetizations, ionic oxidation states, hybridizations, and inter-site magnetic couplings. The vast majority of these states can be fingerprinted through their projection on the atomic orbitals of the magnetic ions. We have devised an approach that provides an effective control over these occupation matrices, allowing us to systematically explore the landscape of the potential energy surface. We showcase the emergence of a complex zoology of self-consistent states; even more so when semi-local density-functional theory is augmented - and typically made more accurate - by Hubbard corrections. Such extensive explorations allow to robustly identify the ground state of magnetic systems, and to assess the accuracy (or not) of current functionals and approximations

en: The energy landscape of magnetic materials

high-throughput

display_name: high-throughput

openmodel

display_name: openmodel


en:
Quantum spin Hall insulators (QSHIs) are a class of topological materials that has been extensively studied during the past decade. One of their distinctive features is the presence of a finite band gap in the bulk and gapless, topologically protected edge states that are spin-momentum locked. These materials are characterized by a Z₂ topological order where, in the 2D case, a single topological invariant can be even or odd for a trivial or a topological material, respectively. Thanks to their interesting properties, such as the realization of dissipationless spin currents, spin pumping and spin filtering, they are of great interest in the field of electronics, spintronics and quantum computing. In this work we perform an high-throughput screening of QSHI starting from a set of 783 2D exfoliable materials (603 after removing materials with lanthanides), recently identified from a systematic screening of the ICSD, COD, and MPDS databases (MC2D). In this screening we identify 4 Z₂ topological insulators, 3 of which already known in the literature, and 7 direct gap metals with the potential of becoming QSHIs under a reasonable external perturbation.

All the data are also displayed in the dedicated DISCOVER section "2D topological insulators" https://www.materialscloud.org/discover/2dtopo/dashboard/ptable

en: A complementary screening for quantum spin Hall insulators in 2D exfoliable materials

dft

display_name: dft

quantum-espresso

display_name: quantum-espresso

quantum-spin-hall

display_name: quantum-spin-hall

topological-insulator

display_name: topological-insulator


en: In contrast to inorganic quantum wells, hybrid quantum wells (HQWs) based on metal-organic semiconductors are characterized by relatively soft lattices, in which excitonic states can strongly couple to lattice phonons. Therefore, understanding the lattice's impact on exciton dynamics is essential for harnessing the optoelectronic potential of HQWs. Beyond 2D metal halide perovskites, layered metal-organic chalcogenides (MOCs) are an air-stable, underexplored material class hosting room-temperature excitons that could be exploited as photodetectors, light emitting devices, and ultrafast photoswitches. Here, we elucidate the role of phonons in the optical transitions of the prototypical MOC [AgSePh]∞. Impulsive stimulated Raman scattering (ISRS) allows us to detect coherent exciton oscillations driven by Fröhlich interaction with low-energy optical phonons. Steady state absorption and Raman spectroscopies reveal a strong exciton-phonon coupling (Huang-Rhys parameter ~1.7) and its anharmonicity, manifested as non-trivial temperature-dependent Stokes shift. Our ab initio calculations support these observations, hinting at an anharmonic behavior of the low-energy phonons < 200 cm⁻¹. Our results untangle complex exciton-phonon interactions in MOCs, establishing an ideal testbed for room-temperature many-body phenomena.

en: Anharmonic exciton-phonon coupling in metal-organic chalcogenides hybrid quantum wells

exciton-phonon-coupling

display_name: exciton-phonon-coupling

metal-organic-chalcogenides

display_name: metal-organic-chalcogenides

raman-spectroscopy

display_name: raman-spectroscopy


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

en: Materials Cloud

en: Learning the electronic density of states in condensed matter

cosmo-epfl

display_name: cosmo-epfl

snsf

display_name: snsf


marvel

display_name: marvel


en: In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. We show that when coupled with quantum machine learning (QML) based surrogate methods the computational burden can be alleviated such that QMC shows clear potential to undergird the formation of high quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: The fixed node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons set based QML (AQML) models. Numerical evidence presented includes converged DMC results for over one thousand small organic molecules with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules with 9 heavy atoms to validate the AQML predictions. Numerical evidence collected for 𝛥-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies  with near chemical accuracy throughout chemical space. In this archive, we present DMC energies for over one thousand small organic molecules with up to 5 heavy atoms, and 50 medium sized organic molecules with 9 heavy atoms, as well as energies computed at cheaper levels of theory such as HF, DFT, MP2 and CCSD(T).

en: Fixed node diffusion Monte Carlo energies for over one thousand small organic molecules

chemical-space

display_name: chemical-space

quantum-machine-learning

display_name: quantum-machine-learning

quantum-monte-carlo

display_name: quantum-monte-carlo


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

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

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

charged-excitations

display_name: charged-excitations

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure

koopmans-spectral-functionals

display_name: koopmans-spectral-functionals

orbital-density-dependent-functionals

display_name: orbital-density-dependent-functionals


envi

en: Environment


en: Thin film organic photovoltaics (OPVs) aspire to extract solar energy in a green, high efficiency, and cost-effective pathway, offering a sustainable solution to energy and ecosystem. We take the effort of developing low ecological harmful solvent processing of OPV devices and mini-modules, to strengthen the environmental benefits of OPV. New formula utilizing safer solvents enhanced with additives affords an optimal laboratory device efficiency of 20% as well as superior operational and thermal stability. Mini-module shows an efficiency up to 17.6% in upscaled processing, representing the highest performance for green solvent OPV development. Suitable nanoscaled phase separation is obtained in together with a micron-scale surface wrinkle pattern from the new developed processing strategy. Thus efficient photon-to-electron conversion at nano-interfaces and light extraction in broader receiving angles is achieved, which allows for more flexible installation geometries in building-integrated applications.

en: Organic solar cells with eco-friendly preparation to achieve an efficiency of 20%

long-term-lifetime

display_name: long-term-lifetime

organic-solar-cells

display_name: organic-solar-cells

sustainability

display_name: sustainability


en: Prismatic slip in magnesium at temperatures T ≲ 150 K occurs at ∼ 100 MPa independent of temperature, and jerky flow due to large prismatic dislocation glide distances is observed; this athermal regime is not understood. In contrast, the behavior at T ≳ 150 K is understood to be governed by a thermally-activated double-cross-slip of the stable basal screw dislocation through an unstable or weakly metastable prism screw configuration and back to the basal screw. Here, a range of neural network potentials (NNPs) that are very similar for many properties of Mg including the basal-prism-basal cross-slip path and pro- cess, are shown to have an instability in prism slip at a potential-dependent critical stress. One NNP, NNP-77, has a critical instability stress in good agreement with experiments and also has basal-prism-basal transition path energies in very good agreement with DFT results, making it an excellent potential for understanding Mg prism slip. Full 3d simulations of the expansion of a prismatic loop using NNP-77 then also show a transition from cross-slip onto the basal plane at low stresses to prismatic loop expansion with no cross- slip at higher stresses, consistent with in-situ TEM observations. These results reveal (i) the origin and prediction of the observed unstable low-T prismatic slip in Mg and (ii) the critical use of machine-learning potentials to guide discovery and understanding of new important metallurgical behavior.

en: A low-temperature prismatic slip instability in Mg understood using machine learning potentials

magnesium

display_name: magnesium

minimum-energy-path

display_name: minimum-energy-path

neural-network-potential

display_name: neural-network-potential

prismatic-slip

display_name: prismatic-slip


en: A key strategy to design environmental barrier coatings has been focused on doping multiple rare-earth principal components into β-type rare-earth disilicates (RE2Si2O7) to achieve versatile property optimization. However, controlling the phase formation capability of (nRExi)2Si2O7 remains a crucial challenge, due to the complex polymorphic phase competitions and evolutions led by different RE3+ combination. Herein by fabricating twenty-one model (REI0.25REII0.25REIII0.25REIV0.25)2Si2O7 compounds, we find that their formation capability can be evaluated by the ability to accommodate configurational randomness of multiple RE3+ cations in β-type lattice while preventing the β-to-γ polymorphic transformation. The phase formation and stabilization are controlled by the average RE3+ radius and the deviations of different RE3+ combinations. Subsequently, based on high-throughput density-functional-theory calculations, we propose that the configurational entropy of mixing is a reliable descriptor to predict the phase formation of β-type (nRExi)2Si2O7. The results may accelerate the design of novel (nRExi)2Si2O7 materials with tailored compositions and controlled polymorphic phases.

en: Phase formation capability of β-(REI0.25REII0.25REIII0.25REIV0.25)2Si2O7: towards compositional design of multiple rare earth principal components

density-functional-theory

display_name: density-functional-theory

environmental-barrier-coating

display_name: environmental-barrier-coating

experimental

display_name: experimental

high-entropy-ceramics

display_name: high-entropy-ceramics

phase-formation

display_name: phase-formation

rare-earth-disilicate

display_name: rare-earth-disilicate


en: A key strategy to design environmental barrier coatings focuses on doping multiple rare-earth principal components into β-type rare-earth disilicates (RE2Si2O7) to achieve versatile property optimization. However, controlling the phase formation capability of (nRExi)2Si2O7 remains a crucial challenge, due to the complex polymorphic phase competitions and evolutions led by different RE3+ combination. Herein, by fabricating twenty-one model (REI0.25REII0.25REIII0.25REIV0.25)2Si2O7 compounds, we find that their formation capability can be evaluated by the ability to accommodate configurational randomness of multiple RE3+ cations in β-type lattice while preventing the β-to-γ polymorphic transformation. The phase formation and stabilization are controlled by the average RE3+ radius and the deviations of different RE3+ combinations. Subsequently, based on high-throughput density-functional-theory calculations, we propose that the configurational entropy of mixing is a reliable descriptor to predict the phase formation of β-type (nRExi)2Si2O7. The results may accelerate the design of (nRExi)2Si2O7 materials with tailored compositions and controlled polymorphic phases.

en: Phase formation capability and compositional design of β-phase multiple rare-earth principal component disilicates


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 widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (e.g. atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the applicability of the prediction rigidities to an even broader class of atomistic modeling problems.
This record contains all the data used for the analyses conducted in the associated work published in Faraday Discussions.

en: Prediction rigidities for data-driven chemistry

machine-learning

display_name: machine-learning

ml-model-robustness

display_name: ml-model-robustness

prediction-rigidity

display_name: prediction-rigidity

uncertainty-quantification

display_name: uncertainty-quantification


materialscloud

en: Materials Cloud


en: Augmented plane wave methods enable an efficient description of atom-centered or localized features of the electronic density, circumventing high energy cutoffs and thus prohibitive computational costs of pure plane wave formulations. To complement existing implementations for ground-state properties and excitation energies, we present the extension of the Gaussian and augmented plane wave method to excited-state nuclear gradients within the Tamm–Dancoff approximation of time-dependent density functional theory and its implementation in the CP2K program package. Benchmarks for a test set of 35 small molecules demonstrate that maximum errors in the nuclear forces for excited states of singlet and triplet spin multiplicity are smaller than 0.1 eV/Å. The method is furthermore applied to the calculation of the zero-phonon line of defective hexagonal boron nitride. This spectral feature is reproduced with an error of 0.6 eV in comparison to GW–Bethe–Salpeter reference computations and 0.4 eV in comparison to experimental measurements. Accuracy assessments and applications thus demonstrate the potential use of the outlined developments for large-scale applications on excited-state properties of extended systems. The data in this record are supplementary data to the published article with DOI 10.1021/acs.jctc.4c00614.

en: Excited-state forces with the Gaussian and augmented plane wave method for the Tamm–Dancoff approximation of time-dependent density functional theory

augmented-plane-wave-methods

display_name: augmented-plane-wave-methods

gaussian-basis-sets

display_name: gaussian-basis-sets


en: Complex oxide functionality, such as ferroelectricity, magnetism or superconductivity is often achieved in epitaxial thin-film geometries. Oxygen vacancies tend to be the dominant type of defect in these materials but a fundamental understanding of their stability and electronic structure has so far mostly been established in the bulk or strained bulk, neglecting interfaces and surfaces present in a thin-film geometry. We investigate here, via density functional theory calculations, oxygen vacancies in the model system of a SrMnO3 (SMO) thin film grown on a SrTiO3 (STO) (001) substrate. Structural and electronic differences compared to bulk SMO result mainly from undercoordination at the film surface. The changed crystal field leads to a depletion of subsurface valence-band states and transfer of this charge to surface Mn atoms, both of which strongly affect the defect chemistry in the film. The result is a strong preference of oxygen vacancies in the surface region compared to deeper layers. Finally, for metastable oxygen vacancies in the substrate, we predict a spatial separation of the defect from its excess charge, the latter being accommodated in the film but close to the substrate boundary. These results show that surface and interface effects lead to significant differences in stability and electronic structure of oxygen vacancies in thin-film geometries compared to the (strained) bulk.

en: Surface and interface effects in oxygen deficient SrMnO3 thin films grown on SrTiO3

crystal-field

display_name: crystal-field

cscs

display_name: cscs

interface

display_name: interface

oxygen-vacancy

display_name: oxygen-vacancy

snsf

display_name: snsf

surface

display_name: surface

thin-film

display_name: thin-film


envi

en: Environment


en: In the upcoming process to overcome the limitations of the standard von Neumann architecture, synaptic electronics is gaining a primary role for the development of in-memory computing. In this field, Ge-based compounds have been proposed as switching materials for nonvolatile memory devices and for selectors. By employing the classical molecular dynamics, we study the structural features of both the liquid states at 1500 K and the amorphous phase at 300 K of Ge-rich and Se-rich chalcogenides binary Ge_x Se_1–x systems in the range 0.4 ≤ x ≤ 0.6. The simulations rely on a model of interatomic potentials where ions interact through steric repulsion, as well as Coulomb and charge–dipole interactions given by the large electronic polarizability of Se ions. Our results indicate the formation of temperature-dependent hierarchical structures with short-range local orders and medium-range structures, which vary with the Ge content. Our work demonstrates that nanosecond-long simulations, not accessible via ab initio techniques, are required to obtain a realistic amorphous phase from the melt. Our classical molecular dynamics simulations are able to describe the profound structural differences between the melt and the glassy structures of GeSe chalcogenides. These results open to the understanding of the interplay between chemical composition, atomic structure, and electrical properties in switching materials.

en: Hierarchical short- and medium-range order structures in amorphous Ge_x Se_1–x for selectors applications

amorphous

display_name: amorphous

chalcogenides

display_name: chalcogenides

intersect

display_name: intersect

molecular-dynamics

display_name: molecular-dynamics


en:
Data for journal article.
We provide the input and outputs of the AGWX suite (see article) used in the work. Also, we add the plots of the obtained Green's function for all densities, k-points, and frequencies.

en: A unified Green's function approach for spectral and thermodynamic properties from algorithmic inversion of dynamical potentials

computational-materials-science

display_name: computational-materials-science

dyson-equation

display_name: dyson-equation

embedding-theory

display_name: embedding-theory

homogeneous-electron-gas

display_name: homogeneous-electron-gas

many-body-perturbation-theory-gw

display_name: many-body-perturbation-theory-gw


en: The α/β interface is central to the microstructure and mechanical properties of titanium alloys. We investigate the structure, thermodynamics and migration of the coherent and semicoherent Ti α/β interfaces as a function of temperature and misfit strain via molecular dynamics (MD) simulations, thermodynamic integration and an accurate, DFT-trained Deep Potential. The structure of an equilibrium semicoherent interface consists of an array of steps, an array of misfit dislocations, and coherent terraces. Analysis determines the dislocation and step (disconnection) array structure and habit plane. The MD simulations show the detailed interface morphology dictated by intersecting disconnection arrays. The steps are shown to facilitate α/β interface migration, while the misfit dislocations lead to interface drag; the drag mechanism is different depending on the direction of interface migration. These results are used to predict the nature of α phase nucleation on cooling through the α-β phase transition.

en: Coherent and semicoherent interfaces in titanium: structure, thermodynamics, migration

free-energy

display_name: free-energy

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

titanium-interfaces

display_name: titanium-interfaces


en: Organic semiconductors with high-spin ground states are fascinating because they could enable fundamental understanding on the spin-related phenomenon in light element and provide opportunities for organic magnetic and quantum materials. Although high-spin ground states have been observed in some quinoidal type small molecules or doped organic semiconductors, semiconducting polymers with high-spin at their neutral ground state are rarely reported. Here we report three high-mobility semiconducting polymers with different spin ground states. We show that polymer building blocks with small singlet-triplet energy gap (ΔES-T) could enable small ΔES-T gap and increase the diradical character in copolymers. We demonstrate that the electronic structure, spin density, and solid-state interchain interactions in the high-spin polymers are crucial for their ground states. Polymers with a triplet ground state (S = 1) could exhibit doublet (S = 1/2) behavior due to different spin distributions and solid-state interchain spin-spin interactions. Besides, these polymers showed outstanding charge transport properties with high hole/electron mobilities and can be both n- and p-doped with superior conductivities. Our results demonstrate a rational approach to high-mobility semiconducting polymers with different spin ground states.

en: High-mobility semiconducting polymers with different spin ground states

high-mobility-polymers

display_name: high-mobility-polymers

high-spin-materials

display_name: high-spin-materials

organic-semiconductor

display_name: organic-semiconductor


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:
Despite the high promise of Li-metal-based batteries, its commercialization has been hampered due to the formation of dendrites that lead to mechanical instability,
energy loss and eventual internal short circuits. The provided dataset consists of the phase-field simulation results for investigating the effect of applied overpotential and operating temperature on dendrite growth. These data are used for elucidating the correlation of overpotential and temperature with the surface modulation during electrodeposition. The simulation cell consists of a Li metal anode and 1M LiPF6 in EC:DMC (1:1), and the electrodeposition process was simulated under the applied overpotential ranging from 0.30 V to 0.44 V with a 0.02 V increment at temperatures from 268 K and 333 K with a 5 K increment. The data contains order parameter, chemical potential and overpotential. The supplied Python script can compute the surface tortuosity, average and maximum Li heights and dendrite height at a given temperature, overpotential and time step.

en: Phase-field investigation of lithium electrodeposition under different applied overpotentials and operating temperatures

anode

display_name: anode

big-map

display_name: big-map

dendrites

display_name: dendrites

interfaces

display_name: interfaces

lithium

display_name: lithium

phase-field

display_name: phase-field


materialscloud

en: Materials Cloud


en:
Dynamical potentials appear in many advanced electronic-structure methods, including self-energies from many-body perturbation theory, dynamical mean-field theory, electronic-transport formulations, and many embedding approaches. Here, we propose a novel treatment for the frequency dependence, introducing an algorithmic inversion method that can be applied to dynamical potentials expanded as sum-over-poles. This approach allows for an exact solution of Dyson-like equations at all frequencies via a mapping to a matrix diagonalization, and provides simultaneously frequency-dependent (spectral) and frequency-integrated (thermodynamic) properties of the Dyson-inverted propagators.
The transformation to a sum-over-poles is performed introducing n-th order generalized Lorentzians as an improved basis set to represent the spectral function of a propagator.
Numerical results for the homogeneous electron gas at the G0W0 level are provided to argue for the accuracy and efficiency of such unified approach.
In this record, we provide the input and outputs of the AGWX suite (see article) used in the work. Also, we add the plots of the obtained Green's function for all densities, k-points, and frequencies.

computational-materials-science

display_name: computational-materials-science

marvel

display_name: marvel

max

display_name: max

snsf

display_name: snsf


homogeneous-electron-gas

display_name: homogeneous-electron-gas

many-body-perturbation-theory-gw

display_name: many-body-perturbation-theory-gw


en:
If one carries out a molecular simulation of N particles using periodic boundary conditions, linear momentum is conserved and hence the number of degrees of freedom is set to 3N-3. In most programs, this number of degrees of freedom is the default setting. However, if one carries out a molecular simulation in an external field, one needs to ensure that degrees of freedom are changed from this default setting to 3N, as in an external field the velocity of the center of mass can change. Using the correct degrees of freedom is important in calculating the temperature and in some algorithms to simulate at constant temperature.
For sufficiently large systems, the difference between 3N and 3N-3 is negligible in the way. However, there are systems in which the comparison with experimental data requires molecular dynamics simulations of a small number of particles. In this work, we illustrate the effect of an incorrect setting of degrees of freedom in molecular dynamic simulations studying the diffusion properties of guest molecules in nanoporous materials. We show that previously published results have reported a surprising diffusion dependence on the loading, which could be traced back to an incorrect setting of the degrees of freedom. As the correct settings are convoluted and counter-intuitive in some of the most commonly used molecular dynamics programs, we carried out a systematic study on the consequences of the various commonly used (incorrect) settings. Our conclusion, is that for systems smaller than 50 particles the results are most likely unreliable as these are either performed at an incorrect temperature or the temperature is incorrectly used in some of the results.
Furthermore, a novel and efficient method to calculate diffusion coefficients of guest molecules into nanoporous materials at zero loading conditions is introduced.

en: On the effects of the degrees of freedom on calculating diffusion properties in nanoporous materials

diffusion

display_name: diffusion

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nanoporous-materials

display_name: nanoporous-materials

prace

display_name: prace

zeolites

display_name: zeolites


en: The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the energy requirement is reduced by as much as a factor of two using the correct heat capacity.

en: A data-science approach to predict the heat capacity of nanoporous materials

heat-capacity

display_name: heat-capacity

machine-learning

display_name: machine-learning

mof

display_name: mof

thermal-properties

display_name: thermal-properties

zeolite

display_name: zeolite


envi

en: Environment


en: Li–S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulphide (LiPS) shuttling. Computational screening of LiPS adsorption on single-atom catalyst (SAC) substrates is of great aid to the design of Li–S batteries which are robust against the LiPS shuttling from the cathode to the anode and the electrolyte. To facilitate this process, we develop a machine learning (ML) protocol to accelerate the systematic mapping of dominant local energy minima found with calculations based on the density functional theory (DFT), and, in turn, fast screening of LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for LiPS adsorbed on graphene decorated with a Fe–N4–C SAC. We identify minima whose binding energies are better or on par with the one previously reported in the literature. We then move to analyse the adsorption trends on Zn–N4–C SAC and observe similar adsorption strength and behaviour with the Fe–N4–C SAC, highlighting the good predictive power of our protocol. Our approach offers a comprehensive and computationally efficient alternative to conventional approaches studying LiPS adsorption

en: Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches

batteries

display_name: batteries

generalised-convex-hull

display_name: generalised-convex-hull

minima-search

display_name: minima-search