en:
This repository contains the complete dataset and associated software supporting the publication:
“In-Situ Ptychographic Nanotomography Captures Activation, Mobility, and Deactivation of Supported Catalysts”

The data were acquired using in-situ ptychographic X-ray computed nanotomography (PXCT) and used to investigate structural evolution of Pd-on-silica catalysts under methane oxidation conditions.
Experiments were performed at the cSAXS beamline of the SLS.

The dataset includes raw diffraction data, reconstructed projections, 3D electron density tomograms, and analysis/reconstruction codes.

Codes:
cxs_software_ptychography/  Contains ptychographic reconstruction routines (phase retrieval, scan handling, preprocessing).
cxs_software_tomography/    Contains tomographic reconstruction and 3D volume processing tools.

Raw Data
S03000-03999_ID ...     Raw diffraction datasets acquired during in-situ scanning nanotomography experiments

Reconstructed Data
analysis_S03000-03999 ...    Includes phase and/or amplitude porjection reconstructions
reconstructed_tomograms    Volumetric datasets derived from ptychographic projections used for analysis

en: The PSI Data Catalog is the central repository directory for scientific metadata generated at the Paul Scherrer Institut (PSI). Powered by SciCat—a community-driven, open-source metadata management framework co-developed by PSI—the catalog provides discoverability and standardized access pathways to petabyte-scale datasets produced across PSI’s large-scale research facilities, including the Swiss Light Source (SLS), the Swiss Spallation Neutron Source (SINQ), and the SwissFEL X-ray free-electron laser.

en: PSI Data Catalog

en: Data and Software Supporting: In-Situ Ptychographic Nanotomography Captures Activation, Mobility, and Deactivation of Supported Catalysts

heterogeneous-catalysis

display_name: heterogeneous-catalysis

x-ray-ptychography

display_name: x-ray-ptychography

x-ray-tomography

display_name: x-ray-tomography


$oid: OBSOLETE

en:
Double hydroxide perovskites with magnetic transition-metal ions were recently identified as a unique class of materials that combine magnetic frustration with correlated proton disorder - a prerequisite for quantum-disordered fluctuating magnetic ground states resembling spin liquids. Here we present the results of muon spin relaxation (muSR) measurements carried out on fully deuterated samples of the double hydroxyperovskites CuSn(OH)6 (S=1/2) and MnSn(OH)6 (S=5/2) over the temperature range 0.053-50 K. The absence of any long-range magnetic order is confirmed down to 0.053 K. We observe no oscillations of the muon asymmetry down to the lowest temperature. The muon relaxation rates show a continuous increase with decreasing temperature, indicating persistent spin fluctuations in both compounds. Spin correlations are consistent with homogeneous spin dynamics. These observations reinforce the assertion that both compounds have a quantum-dynamic magnetic ground state that is consistent with a spin-liquid-like phase stabilized by proton disorder.
This dataset contains the corresponding muon spin spectroscopy raw time histograms, measured on CuSn(OD)6 and MnSn(OD)6.

approval_status: approved

de: PSI Data Catalog

fr: PSI Data Catalog

it: PSI Data Catalog

political_level: other

state: active

type: organization

de: PSI Open Data Provider

en: Dataset: Spin-liquid-like ground states in the double hydroxyperovskites CuSn(OD)6 and MnSn(OD)6 evidenced by muSR spectroscopy

type: dataset

key: guid

key: uri

value: DCAT Harvester SciCat

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

flame

display_name: flame

smus

display_name: smus


en: Low-energy muSR (LE-muSR) enables depth-resolved studies of magnetic and electronic properties from the surface into the near-surface region, but the measured transverse-field asymmetry is not an intrinsic constant but depends on implantation energy and beamline conditions. Following an upgrade of the single-muon tagging system at the LEM beamline at PSI in 2023, updated asymmetry calibrations are required. Here, we present updated reference measurements that establish the energy-dependent maximum asymmetry using a silver reference and quantify spurious contributions from reflected muons using a nickel reference. In addition, we address systematic reductions of the measured asymmetry arising from incomplete beam--sample overlap by introducing a sample-size-dependent correction factor. This factor is obtained from musrSim/Geant4 simulations incorporating an updated electrostatic field map of the sample environment and is benchmarked using implantation-energy scans on SrTiO3 samples of different dimensions. Together, these updated calibrations and the simulation-based overlap correction provide a practical and up-to-date framework for LE-muSR data correction under current beam conditions and enable quantitative depth-resolved analysis of (dia)magnetic volume fractions.

en: Correcting the Depth-Dependent Asymmetry in Low-Energy muSR


en: The ability of photofunctional organic molecules to perform chemical/mechanical work or convert photons into electrons results from a complex interplay between charge transfer and transient atomic rearrangements involving the local environment. These ultrafast structural dynamics have remained elusive experimentally, preventing a mechanistic understanding of the electronic-nuclear couplings. We will use the high X-ray photon flux and sub ps time resolution of the SwissFEL to realize the first X-ray scattering observation of solute-solvent dynamics following photoinduced charge transfer in an organic chromophore. The sensitivity of the scattering to changes in atomic distances will be exploited not only to track in real time the intra- and intermolecular structural dynamics but also to identify the electronic changes that trigger them. The results will pave the way for a better understanding of the factors governing the outcomes of photoreactions across chemistry and biology.

en: Photoinduced intramolecular and solvation dynamics of an organic chromophore in polar solvent


en:
The extinct plant group Bennettitales (Cycadeoidales) was a prominent part of Mesozoic vegetation. The group has been studied extensively and for more than 100 years it has been central in discussions of the relationships among the major groups of seed plants in which extinct plants were also considered. Despite much effort the phylogenetic position of the Bennettitales is still unresolved with a major obstacle being uncertainty about how to interpret the organisation and structure of the seed. The most widely accepted interpretation has been that the seed, like the seeds of most conifers, has a single integument around the nucellus and lacks any additional coverings. An alternative interpretation is that the seed integument is surrounded by an envelope comparable to the situation in extant Gnetales and extinct Erdtmanithecales. The petrified seed cone of Cycadeoidea (Bennettites) morierei described in the late nineteenth century from the Early Cretaceous of Normandy is one of the most informative bennettitalean reproductive structures with its superb preservation offering great potential for resolving the unsettled discussion regarding seed structure in Bennettitales.
	C. morierei was carefully documented by Octave Lignier in his monumental study “Végétaux fossiles de Normandie. Structure et affinités du Bennettites morierei Sap. & Mar. (sp.)” from 1894, but despite his meticulous analysis of the fossil, Lignier left certain questions open in his interpretation and in his widely cited reconstruction of the structure of the seed. In the paper associated with the datasets provided here we have re-examined fragments of Cycadeoidea morierei using synchrotron X-ray tomographic microscopy (SRXTM) to attempt to answer some of these open questions.
	Cycadeoidea morierei is based on a single petrified seed cone. The specimen was fractured by Lignier, who prepared several thin sections, in longitudinal as well as transverse directions in preparing his account. The original material was stored at the University of Caen, France, but most was lost during the second World War. However, several larger and smaller fragments of the seed cone were later recovered at the university and made available for the present study. Among the fragments studied using SRXTM were small isolated, abraded seeds and interseminal scales (e.g., fragments 6 and 8) and a few small groups of fractured seeds and interseminal scales still tightly packed together (e.g., fragment 17). Typically, these small fragments are broken at the base and only rarely are remains of the seed stalk preserved. One larger fragment (fragment 24) consists of several seeds with seed stalks intact. For comparison with Cycadeoidea morierei we also examined a fragment of another classic petrified seed cone, Cycadeoidea gibsoniana from the Early Cretaceous of isle of Wight, Southern England, using SRXTM. The results, combined with a comparative survey of other fossil material, indicate that the seeds of Bennettitales had at least one, perhaps two, envelopes covering the integument, comparable to what is seen among extant Gnetales, extinct Erdtmanithecales, and diverse fossil seeds recovered from Early Cretaceous mesofossil assemblages. Most of the SRXTM analyses of the Cycadeoidea material were carried during 2009-2023 on the TOMCAT beamline, SLS, Paul Scherrer Institute, Villigen, Switzerland. Fragment 24 was analysed at the new I-TOMCAT beamline at SLS in November 2025.

en: Synchrotron radiation X-ray tomographic microscopy datasets for seed structure in Cycadeoidea morierei


apoferritin

en:
CryoEM dataset of Apoferritin collected on a FEI Titan Krios G4 microscope equipped with a Falcon 4i detector.
First public dataset of the OpenEM project. For more information on the project: https://swissopenem.github.io

en: Apoferritin

title_for_slug: apoferritin


en: We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.

materialscloud

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

psi

capacity: public

en: Capturing chemical intuition in synthesis of metal-organic frameworks

value: DCAT Harvester Materials Cloud

genetic-algorithms

display_name: genetic-algorithms

machine-learning

display_name: machine-learning

marvel

display_name: marvel

metal-organic-frameworks

display_name: metal-organic-frameworks

optimisation

display_name: optimisation

robotic-synthesi

display_name: robotic-synthesi

synthesis

display_name: synthesis


en: Here we present 1,000 structures each of a water monomer, water dimer, Zundel cation and bulk water used to train tensorial machine-learning models in Phys. Rev. Lett. 120, 036002 (2018). The archive entry contains files in extended-XYZ format including the structures and several tensorial properties: for the monomer, dimer and Zundel cation, the dipole moment, polarizability and first hyperpolarizability are included, and for bulk water the dipole moment, polarizability and dielectric tensor are given.

en: Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems

bulk

display_name: bulk

dielectric-tensor

display_name: dielectric-tensor

dipole-moment

display_name: dipole-moment

hyperpolarizability

display_name: hyperpolarizability

molecular

display_name: molecular

polarizability

display_name: polarizability

water

display_name: water


en: Biomolecular simulations have a great potential in protein engineering, drug discovery and many other fields. Unfortunately, this method is computationally expensive, so many interesting processes cannot be routinely studied. In order to address this problem we developed Flying Gaussian method [Journal of Chemical Theory and Computation 12, 4644-4650 (2016)]. This method simultaneously simulates multiple replicas of the studied system and disfavor replicas with similar structures by artificial bias potential. The question arises how to calculate an unbiased free energy surface from a biased simulation. This dataset demonstrates together with mathematical arguments supports application of Umbrella Sampling reweighing method, despite the fact that this method is designed for methods with a time-independent bias potential.

en: Free-Energy Surface Prediction by Flying Gaussian Method: Numerical Proof

flying-gaussian-method

display_name: flying-gaussian-method

metadynamics

display_name: metadynamics

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

sampling

display_name: sampling


envi

en: Environment


en:
Metal–organic frameworks (MOFs), a class of porous materials, are of particular interest in gas storage and separation applications due largely to their high internal surface areas and tunable structures. MOF-5 is perhaps the archetypal MOF; in particular, many isoreticular analogues of MOF-5 have been synthesized, comprising alternative dicarboxylic acid ligands. In this contribution we introduce a new set of hypothesized MOF-5 analogues, constructed from commercially available organic molecules. We describe our automated procedure for hypothetical MOF design, comprising selection of appropriate ligands, construction of 3D structure models, and structure relaxation methods. 116 MOF-5 analogues were designed and characterized in terms of geometric properties and simulated methane uptake at conditions relevant to vehicular storage applications. A strength of the presented approach is that all of the hypothesized MOFs are designed to be synthesizable utilizing ligands purchasable online.

Version 2 includes the structures in CIF format.

en: Mail-order metal-organic frameworks (MOFs): designing isoreticular MOF-5 analogues comprising commercially available organic molecules

database

display_name: database

dc

display_name: dc

deliverable-capacities

display_name: deliverable-capacities

gcmc

display_name: gcmc

grand-canonical-monte-carlo

display_name: grand-canonical-monte-carlo

high-throughput

display_name: high-throughput

methane-storage

display_name: methane-storage

nanoporous

display_name: nanoporous

three-dimensional

display_name: three-dimensional


en: In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. However, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. Recently, we developed a pore recognition approach to quantify similarity of pore structures using topological data analysis. Barcodes generated with using this approach allow us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures. This database has barcodes for zeolites, metal organic frameworks, and zeolitic imidazolate frameworks.

en: Barcodes for nanoporous materials

marvel

display_name: marvel

metal-organic-frameworks

display_name: metal-organic-frameworks

nanoporous-materials

display_name: nanoporous-materials

persistence-homology

display_name: persistence-homology

topological-data-analysis

display_name: topological-data-analysis

zeolites

display_name: zeolites


barcodes-for-nanoporous-materials

title_for_slug: barcodes-for-nanoporous-materials


en: We report a large-scale density-functional-theory study of the configuration space of water ice. We geometry optimise 74,963 ice structures, which are selected and constructed from over five million tetrahedral networks listed in the databases of Treacy and Deem, and the International Zeolite Association database. All prior knowledge of ice is set aside and we introduce generalised convex hulls to identify configurations stabilised by appropriate thermodynamic constraints. We thereby rediscover all known phases (I to XVII, i, 0 and the quartz phase) except the metastable ice IV. Crucially, we also find promising candidates for ices XVIII through LI. Using the sketch-map dimensionality-reduction algorithm we construct an a priori, navigable map of configuration space, which reproduces similarity relations between structures and highlights the novel candidates. By relating the known phases to the tractably small, yet structurally diverse set of synthesisable candidate structures, we provide an excellent starting point for identifying formation pathways.

en: Mapping uncharted territory in ice from zeolite networks to ice structures

dft

display_name: dft

first-principles

display_name: first-principles

ice

display_name: ice

water

display_name: water


database

display_name: database


en: The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure but also recovers the correct localization dynamics that follow the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description and allows us to achieve accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.

en: Simulating the ghost: quantum dynamics of the solvated electron

cscs

display_name: cscs

machine-learning

display_name: machine-learning

moller-plesset-perturbation-theory

display_name: moller-plesset-perturbation-theory

quantum-dynamics

display_name: quantum-dynamics

snsf

display_name: snsf

solvated-electron

display_name: solvated-electron


marvel

display_name: marvel


en: In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original experimental sources.This dataset is intended for researchers in materials science and mechanical engineering, offering a robust foundation for training and testing deep learning algorithms in fatigue analysis. By making this dataset publicly available, we aim to foster collaboration and further advancements in the field of metal fatigue prediction. Researchers are encouraged to utilize and contribute to the dataset, thereby enhancing its scope and applicability.

en: A deep learning dataset for metal multiaxial fatigue life prediction

deep-learning

display_name: deep-learning

fatigue-life-prediction

display_name: fatigue-life-prediction

metal-fatigue

display_name: metal-fatigue

multiaxial-fatigue

display_name: multiaxial-fatigue


en: We introduce and discuss the phenomenon of adatom-induced surface local melting, using extensive first-principles molecular dynamics simulations of Al(100) taken as a paradigmatic case of a non-premelting surface that nevertheless displays facile adatom diffusion with single and multiple exchange pathways. Here, a single adatom deposited on the surface is sufficient to nucleate a localized and diffusing liquid-like region that remains confined to the surface layer, but with an area that increases with temperature; in the absence of the adatom, the surface instead remains crystalline until reaching the bulk melting temperature.

materialscloud

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

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

en: Materials Cloud

en: Adatom-Induced Local Melting

aluminum

display_name: aluminum

defects

display_name: defects

first-principles-molecular-dynamics

display_name: first-principles-molecular-dynamics

surface-melting

display_name: surface-melting


adatom-induced-local-melting

title_for_slug: adatom-induced-local-melting


en: Charge equilibration (Qeq) methods can estimate the electrostatic potential of molecules and periodic frameworks by assigning point charges to each atom, using only a small fraction of the resources needed to compute density functional (DFT)-derived charges. This makes possible, for example, the computational screening of thousands of microporous structures to assess their performance for the adsorption of polar molecules. Recently, different variants of the original Qeq scheme were proposed to improve the quality of the computed point charges. One focus of this research was to improve the gas adsorption predictions in Metal Organic Frameworks (MOFs), for which many different structures are available. In this work, we review the evolution of the method from the original Qeq scheme, understanding the role of the different modifications on the final output. We evaluated the result of combining different protocols and set of parameters, by comparing the Qeq charges with high quality DFT-derived DDEC charges for 2338 MOF structures. We focused on the systematic errors that are attributable to specific atom types to quantify the final precision that one can expect from Qeq methods in the context of gas adsorption where the electrostatic potential plays a significant role, namely CO2 and H2S adsorption. In conclusion, both the type of algorithm and the input parameters have a large impact on the resulting charges and we draw some guidelines to help the user to choose the proper combination of the two for obtaining a meaningful set of charges. We show that, considering this set of MOFs, the accuracy of the original Qeq scheme is often still comparable with the most recent variants, even if it clearly fails in the presence of certain atom types, such as alkali metals.

en: Evaluating charge equilibration methods to generate electrostatic fields in nanoporous materials

charge-equilibration-methods

display_name: charge-equilibration-methods

partial-charges

display_name: partial-charges

qeq

display_name: qeq


en: We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.

en: Capturing chemical intuition in synthesis of metal-organic frameworks

envi

en: Environment

genetic-algorithms

display_name: genetic-algorithms

machine-learning

display_name: machine-learning

metal-organic-frameworks

display_name: metal-organic-frameworks

optimisation

display_name: optimisation

robotic-synthesi

display_name: robotic-synthesi

synthesis

display_name: synthesis


en:
We provide the input files needed to reproduce the results of the article

Toward GW Calculations on Thousands of Atoms
J. Wilhelm, D. Golze, L. Talirz, J. Hutter, C. A. Pignedoli
J. Phys. Chem. Lett.  9, 306–312 (2018) DOI:10.1021/acs.jpclett.7b02740

The GW approximation of many-body perturbation theory is an accurate method
for computing electron addition and removal energies of molecules and solids.
In a canonical implementation, however, its computational cost is  in the
system size N, which prohibits its application to many systems of interest.
We present a full-frequency GW algorithm in a Gaussian-type basis,
whose computational cost scales with N2 to N3.
The implementation is optimized for massively parallel execution on
state-of-the-art supercomputers and is suitable for nanostructures and molecules in the gas,
liquid or condensed phase, using either pseudopotentials or all electrons.
We validate the accuracy of the algorithm on the GW100 molecular test set,
finding mean absolute deviations of 35 meV for ionization potentials and 27 meV
for electron affinities. Furthermore, we study the length-dependence of quasiparticle
energies in armchair graphene nanoribbons of up to 1734 atoms in size, and compute the
local density of states across a nanoscale heterojunction.

abinitio

display_name: abinitio

dft

display_name: dft

graphene

display_name: graphene

gw

display_name: gw

high-performance-computing

display_name: high-performance-computing

marvel

display_name: marvel

nanoribbon

display_name: nanoribbon

scaling

display_name: scaling


en: Toward GW Calculations on Thousands of Atoms


en: Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal–organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure–property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane–methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage.

en: In Silico Design of Porous Polymer Networks: High Throughput Screening for Methane Storage Materials

database

display_name: database

dc

display_name: dc

deliverable-capacities

display_name: deliverable-capacities

gcmc

display_name: gcmc

grand-canonical-monte-carlo

display_name: grand-canonical-monte-carlo

high-throughput

display_name: high-throughput

methane-storage

display_name: methane-storage

nanoporous

display_name: nanoporous

porous-polymer-networks

display_name: porous-polymer-networks

ppn

display_name: ppn

three-dimensional

display_name: three-dimensional


deep-learning

display_name: deep-learning

fatigue-life-prediction

display_name: fatigue-life-prediction

metal-fatigue

display_name: metal-fatigue

multiaxial-fatigue

display_name: multiaxial-fatigue


en: Solid-state electrolytes for Li-ion batteries are attracting growing interest as they allow building safer batteries, also using lithium-metal anodes. Here, we studied a compound in the lithium superionic conductor (LISICON) family, i.e. Li₄₋ₓGe₁₋ₓPₓO₄ (LGPO). Thin films were deposited via pulsed laser deposition, and their electrical properties were compared to those of ceramic pellets. A detailed characterization of their microstructures shows that thin films can be deposited fully crystalline at higher temperatures but also partially amorphous at room temperature. The conductivity is not strongly influenced by the presence of grain boundaries, exposure to air, or lithium deficiencies. First-principles molecular dynamics simulations were employed to calculate the lithium-ion diffusion profile and the conductivity at various temperatures of the ideal LGPO crystal. Simulations give the upper limit of conductivity for a defect-free crystal, which is in the range of 10–2 S cm–1 at 300 °C. The ease of thin-film fabrication and room-temperature Li-ion conductivity in the range of a few μS cm–1 make LGPO a very appealing electrolyte material for thin-film all-solid-state all-oxide microbatteries.

en: Li₄₋ₓGe₁₋ₓPₓO₄, a potential solid-state electrolyte for all-oxide microbatteries

big-map

display_name: big-map

first-principles-molecular-dynamics

display_name: first-principles-molecular-dynamics

ionic-transport

display_name: ionic-transport

lgpo

display_name: lgpo

lisicon

display_name: lisicon

microbatteries

display_name: microbatteries

solid-state-electrolytes

display_name: solid-state-electrolytes

thin-films

display_name: thin-films


en: A transient state of the excess electron in liquid water preceding the development of the solvation shell, the so-called wet electron, has been invoked to explain spectroscopic observations, but its binding energy and atomic structure have remained highly elusive. Here, we carry out hybrid functional molecular dynamics to unveil the ultrafast solvation mechanism leading to the hydrated electron. In the pre-hydrated regime, the electron is found to repeatedly switch between a quasi-free electron state in the conduction band and a localized state with a binding energy of 0.26 eV, which we assign to the wet electron. This transient state self-traps in a region of the liquid which extends up to ∼4.5 Å and involves a severe disruption of the hydrogen-bond network. Our picture provides an unprecedented view on the nature of the wet electron, which is instrumental to understand the properties of this fundamental species in liquid water.

en: Picture of Wet Electron: A Localized Transient State in Liquid Water

first-principles

display_name: first-principles

hydrated-electron

display_name: hydrated-electron

liquid-water

display_name: liquid-water

molecular-dynamics

display_name: molecular-dynamics

wet-electron

display_name: wet-electron


en: Biomolecular simulations are computationally expensive. This limits their application in protein folding simulations, protein engineering, drug design and related fields. Enhanced sampling techniques such as metadynamics accelerates slow events in molecular simulation. This and other method apply artificial forces in directions of collective degrees of freedom (aka collective variables). Path collective variables and Property Map collective variables are defined using a series of reference structures of the studied molecular system. They require a huge number of mean square deviation calculations along the simulation. Close Structure algorithm reduces the number of these operations.

en: Reducing the Number of Mean-Square Deviation Calculations with Floating Close Structure in Metadynamics

collective-variable

display_name: collective-variable

mean-square-deviation

display_name: mean-square-deviation

metadynamics

display_name: metadynamics


en: Here we present 69,840 covalent organic frameworks (COFs) assembled in silico from a set of 666 distinct organic linkers into 2D-layered and 3D configurations. We investigate the feasibility of using these frameworks for methane storage by using grand-canonical Monte Carlo (GCMC) simulations to calculate their deliverable capacities (DCs). From these calculations, we predict that the best structure in the database is linker91_C_linker91_C_tbd, a structure composed of carbon-carbon bonded triazine linkers in the tbd topology. This structure has a predicted 65-bar DC of 216 v STP/v, greater than that of the best current methane storage material. We also predict other top performing materials, with 305 structures having DCs of over 190 v STP/v, and 34 of these having DCs of over 200 v STP/v. This archive entry contains the database of assembled COF structures (in CIF file format) together with all of their properties. Among the calculated properties for each structure are the framework density, the methane heats of desorption at the storage and depletion pressures, the methane uptakes at the storage and deplation pressures, the supercell volume, and the geometric surface area. Structures are also labeled according to their bond types (amide, amine, imine, carbon-carbon, or mixed) and their dimensionalities (2D or 3D).

materialscloud

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

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

en: Materials Cloud

en: In Silico Design of 2D and 3D Covalent Organic Frameworks for Methane Storage Applications

cof

display_name: cof

covalent-organic-frameworks

display_name: covalent-organic-frameworks

database

display_name: database

dc

display_name: dc

deliverable-capacities

display_name: deliverable-capacities

gcmc

display_name: gcmc

grand-canonical-monte-carlo

display_name: grand-canonical-monte-carlo

high-throughput

display_name: high-throughput

layered

display_name: layered

marvel

display_name: marvel

methane-storage

display_name: methane-storage

nanoporous

display_name: nanoporous

three-dimensional

display_name: three-dimensional

two-dimensional

display_name: two-dimensional


en:
Project abstract: Frustrated magnets are one class of fascinating materials that host many intriguing phases such as spin ice, spin liquid and complex long-range magnetic orderings at low temperatures. In this work we use first-principles calculations to find that in a wide range of magnetically frustrated oxides, at zero temperature a number of non-collinear magnetic orderings are more stable than the type-I collinear ordering that is observed at finite temperatures. The emergence of non-collinear orderings in those complex oxides is due to higher-order exchange interactions that originate from second-row and third-row transition metal elements. This implies a collinear-to-noncollinear spin transition at sufficiently low temperatures in those frustrated complex oxides. Furthermore, we find that in a particular oxide Ba2YOsO6, experimentally feasible uniaxial strain can tune the material between two different non-collinear magnetic orderings. Our work predicts new non- collinear magnetic orderings in frustrated complex oxides at very low temperatures and provides a mechanical route to tuning complex non-collinear magnetic orderings in those materials.

About this entry:  We provide the input files of our DFT calculations for the studied complex oxides.  The structures in POSCAR format and the INCAR files for all stabilized magnetic orderings in our study are all included. These files can be directly used into DFT calculations with VASP.

en: The complex non-collinear magnetic orderings in Ba2YOsO6: A new approach to tuning spin-lattice interactions and controlling magnetic orderings in frustrated complex oxides

double-perovskite

display_name: double-perovskite

first-principles-calculations

display_name: first-principles-calculations

frustrated-complex-oxides

display_name: frustrated-complex-oxides

non-collinear-magnetic-ordering

display_name: non-collinear-magnetic-ordering

spin-orbital-coupling

display_name: spin-orbital-coupling


envi

en: Environment


en: We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.

en: Synthesis of Metal-Organic Frameworks: capturing chemical intuition

machine-learning

display_name: machine-learning

metal-organic-frameworks

display_name: metal-organic-frameworks

optimisation

display_name: optimisation

synthesis

display_name: synthesis


en:
Project Abstract: For applications of metal-organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation we develop a simple analytical model that predicts a material's Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework's intrinsic flexibility whereby performance is either improved or reduced with increasing flexibility, depending on the material's pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other non-optimal materials' selectivity can actually be improved by flexibility. Equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.

About this entry: You can find the Xe/Kr Henry coefficients and the infinite dilution selectivity of more than 2000 CoRE MOF structures obtained via computational screening when materials are simulated as both flexible and rigid.  For details about the methods used to obtain these results, please see the corresponding paper (DOI: 10.1021/jacs.7b01688).  The data provided here was used to create the scatter plots presented in the original paper and can be used to find the adsorption properties for any material in the CoRE MOF screening.

en: The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials (Data Download)

flexibility

display_name: flexibility

nanoporous-materials

display_name: nanoporous-materials

xekr-separations

display_name: xekr-separations


cof

display_name: cof

covalent-organic-frameworks

display_name: covalent-organic-frameworks

database

display_name: database

dc

display_name: dc

deliverable-capacities

display_name: deliverable-capacities

gcmc

display_name: gcmc

grand-canonical-monte-carlo

display_name: grand-canonical-monte-carlo

high-throughput

display_name: high-throughput

layered

display_name: layered

marvel

display_name: marvel


en:
Here we present 69,840 covalent organic frameworks (COFs) assembled in silico from a set of 666 distinct organic linkers into 2D-layered and 3D configurations. We investigate the feasibility of using these frameworks for methane storage by using grand-canonical Monte Carlo (GCMC) simulations to calculate their deliverable capacities (DCs). From these calculations, we predict that the best structure in the database is linker91_C_linker91_C_tbd, a structure composed of carbon-carbon bonded triazine linkers in the tbd topology. This structure has a predicted 65-bar DC of 216 v STP/v, greater than that of the best current methane storage material. We also predict other top performing materials, with 305 structures having DCs of over 190 v STP/v, and 34 of these having DCs of over 200 v STP/v.
This archive entry contains the database of assembled COF structures (in CIF file format) together with all of their properties, which can be explored using interactive figures. Among the calculated properties for each structure are the framework density, the methane heats of desorption at the storage and depletion pressures, the methane uptakes at the storage and deplation pressures, the supercell volume, and the geometric surface area. Structures are also labeled according to their bond types (amide, amine, imine, carbon-carbon, or mixed) and their dimensionalities (2D or 3D).

methane-storage

display_name: methane-storage

nanoporous

display_name: nanoporous

three-dimensional

display_name: three-dimensional

two-dimensional

display_name: two-dimensional


en: Perovskite minerals form an essential component of the Earth's mantle, and synthetic crystals are ubiquitous in electronics, photonics, and energy technology. The extraordinary chemical diversity of these crystals raises the question of how many and which perovskites are yet to be discovered. Here we show that the "no-rattling" principle postulated by Goldschmidt in 1926, describing the geometric conditions under which a perovskite can form, is much more effective than previously thought and allows us to predict perovskites with a fidelity of 80%. By supplementing this principle with inferential statistics and internet data mining we establish that currently known perovskites are only the tip of the iceberg, and we enumerate 90,000 hitherto-unknown compounds awaiting to be studied. Our results suggest that geometric blueprints may enable the systematic screening of millions of compounds and offer untapped opportunities in structure prediction and materials design.

en: The geometric blueprint of perovskites

data-mining

display_name: data-mining

goldschmidt

display_name: goldschmidt

perovskites

display_name: perovskites

structure-predictions

display_name: structure-predictions


the-geometric-blueprint-of-perovskites

title_for_slug: the-geometric-blueprint-of-perovskites


en: Capturing chemical intuition in synthesis of metal-organic frameworks

genetic-algorithms

display_name: genetic-algorithms

machine-learning

display_name: machine-learning

metal-organic-frameworks

display_name: metal-organic-frameworks

optimisation

display_name: optimisation

robotic-synthesi

display_name: robotic-synthesi

synthesis

display_name: synthesis


en: Biomolecular simulations are computationally expensive. This limits their application in drug or protein design and related fields. Several methods have been developed to address this problem. These methods often use an artificial force or potential acting on selected degrees of freedom known as collective variables. This requires explicit calculation of a collective variable (and its derivatives) from molecular structure. For collective variables that cannot be calculated explicitly or such calculations is slow we developed anncolvar package (https://github.com/spiwokv/anncolvar). This package approximates collective variables using artificial neural networks. It was tested on Isomap low dimensional representation of cyclooctane derivative or solvent-accessible surface area of Trp-cage miniprotein.

en: Approximation of Collective Variables by anncolvar

artificial-neural-networks

display_name: artificial-neural-networks

collective-variable

display_name: collective-variable

metadynamics

display_name: metadynamics


en:
Project Abstract: For applications of metal-organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation we develop a simple analytical model that predicts a material's Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework's intrinsic flexibility whereby performance is either improved or reduced with increasing flexibility, depending on the material's pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other non-optimal materials' selectivity can actually be improved by flexibility. Equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.

About this entry: You can find the Xe/Kr Henry coefficients and the infinite dilution selectivity of more than 2000 CoRE MOF structures obtained via computational screening when materials are simulated as both flexible and rigid.  For details about the methods used to obtain these results, please see the corresponding paper (DOI: 10.1021/jacs.7b01688).  The data provided here was used to create the scatter plots presented in the original paper and can be used to find the adsorption properties for any material in the CoRE MOF screening.

Version 2 includes a minor formatting correction to make the CIF files compliant with the CIF standard.

flexibility

display_name: flexibility

nanoporous-materials

display_name: nanoporous-materials

xekr-separations

display_name: xekr-separations


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: Biomolecular simulations are computationally expensive. This limits their application in protein folding simulations, protein engineering, drug design and related fields. Enhanced sampling techniques such as metadynamics accelerates slow events in molecular simulation. This and other method apply artificial forces in directions of collective degrees of freedom (aka collective variables). Path collective variables and Property Map collective variables are defined using a series of reference structures of the studied molecular system. They require a huge number of mean square deviation calculations along the simulation. Close Structure algorithm reduces the number of these operations.

en: Materials Cloud

en: Reducing the Number of Mean-Square Deviation Calculations with Floating Close Structure in Metadynamics

mean-square-deviation

display_name: mean-square-deviation


en:
Project abstract: In this work, a theoretical framework is developed to explain and predict changes in the product distribution of the propene dimerization reaction, which yields a mixture of C6 olefin isomers, resulting from the use of different porous materials as catalysts. The MOF-74 class of materials has shown promise in catalyzing the dimerization of propene with high selectivity for valuable linear olefin products. We show that experimentally observed changes in the product distribution can be explained in terms of the contribution of the pores to the free energy of formation, which are directly computed using molecular simulation. Our model is used to screen a library of 118 existing and hypothetical MOF and zeolite structures to study how product distribution can be tuned by changing pore size, shape, and composition of porous materials. Using these molecular descriptors, catalyst properties are identified that increase the selective reaction of linear olefin isomers, which are valued as industrial feedstocks. A pore size commensurate with the size of the desired linear products enhances linear conversion by sterically hindering the branched isomers. Another promising feature is the presence of open metal sites, which interact with the olefin π-bond to provide favorable binding sites for the linear isomers.

About this entry: We provide the geometrical framework descriptors, linear conversions, and thermodynamic data for the 118 frameworks screened in this study, as well as thermodynamics of adsorption properties computed for 12 C6 olefin isomers in each framework. For details about the methods used to obtain these data, please see the corresponding paper (DOI: 10.1021/acscatal.7b00712). The data provided here was used to create the scatter plots in the original paper and can be used to find the product distribution and adsorption properties for all of the materials considered in the study.

en: Predicting Product Distribution of Propene Dimerization in Nanoporous Materials (Data Download)

metalorganic-frameworks

display_name: metalorganic-frameworks

molecular-simulation

display_name: molecular-simulation

monte-carlo

display_name: monte-carlo

product-distribution

display_name: product-distribution

propene-dimerization

display_name: propene-dimerization

zeolites

display_name: zeolites


envi

en: Environment


en:
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's post-combustion flue gases. We apply the workflow to a database of 324 covalent-organic frameworks (COFs) reported in the literature to characterize their CO2 adsorption properties using the following steps:
(1) optimization of the crystal structure (atomic positions and cell) using density functional theory,
(2) fitting atomic point charges based on the electron density,
(3) characterizing the (pore) geometry of the structures before and after optimization,
(4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations (empirical interaction potential) and, finally,
(5) assessing the CO2 parasitic energy via process modelling.

The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect inspect every input parameter and result along the workflow, download structures and files at intermediate stages and start their research right from where this work has left off.
In particular, our set of CURATED COFs with optimized geometry and high quality DFT-derived point charges is available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

en: Building a consistent and reproducible database for adsorption evaluation in Covalent-Organic Frameworks

aiida

display_name: aiida

covalent-organic-framework

display_name: covalent-organic-framework

marvel

display_name: marvel

reproducibility

display_name: reproducibility

workflows

display_name: workflows


en:
Charge equilibration (Qeq) methods can estimate the electrostatic potential of molecules and periodic frameworks by assigning point charges to each atom, using only a small fraction of the resources needed to compute density functional (DFT)-derived charges. This makes possible, for example, the computational screening of thousands of microporous structures to assess their performance for the adsorption of polar molecules. Recently, different variants of the original Qeq scheme were proposed to improve the quality of the computed point charges. One focus of this research was to improve the gas adsorption predictions in Metal Organic Frameworks (MOFs), for which many different structures are available. In this work, we review the evolution of the method from the original Qeq scheme, understanding the role of the different modifications on the final output. We evaluated the result of combining different protocols and set of parameters, by comparing the Qeq charges with high quality DFT-derived DDEC charges for 2338 MOF structures. We focused on the systematic errors that are attributable to specific atom types to quantify the final precision that one can expect from Qeq methods in the context of gas adsorption where the electrostatic potential plays a significant role, namely CO2 and H2S adsorption. In conclusion, both the type of algorithm and the input parameters have a large impact on the resulting charges and we draw some guidelines to help the user to choose the proper combination of the two for obtaining a meaningful set of charges. We show that, considering this set of MOFs, the accuracy of the original Qeq scheme is often still comparable with the most recent variants, even if it clearly fails in the presence of certain atom types, such as alkali metals.

Version 2 of the record adds the data underlying the figures in CSV format.

en: Evaluating charge equilibration methods to generate electrostatic fields in nanoporous materials

charge-equilibration-methods

display_name: charge-equilibration-methods

partial-charges

display_name: partial-charges

qeq

display_name: qeq


en: Project Abstract: Pore volume is one of the main properties for the characterization of microporous crystals. It is experimentally measurable and it can also be obtained from the refined unit cell by a number of computational techniques. In this work we assess the accuracy and the discrepancies between the different computational methods which are commonly used for this purpose, i.e, geometric, helium and probe center pore volume, by studying a database of more than 5000 frameworks. We developed a new technique to fully characterize the internal void of a microporous material and to compute the probe accessible and occupiable pore volume. We show that unlike the other definitions of pore volume, the occupiable pore volume can be directly related to the experimentally measured pore volumes from nitrogen isotherms.

en: Accurate Characterization of the Pore Volume in Microporous Crystalline Materials (Data Download)

nanoporous-materials

display_name: nanoporous-materials

pore-volume

display_name: pore-volume

void-fraction

display_name: void-fraction


en: Zeolite-templated carbons (ZTCs) comprise a relatively recent material class synthesized via the chemical vapor deposition of a carbon-containing precursor on a zeolite template, followed by the removal of the template. We have developed a theoretical framework to generate a ZTC model from any given zeolite structure, which we show can successfully predict the structure of known ZTCs. We use our method to generate a library of ZTCs from all known zeolites, to establish criteria for which zeolites can produce experimentally accessible ZTCs, and to identify over 10 ZTCs that have never before been synthesized. We show that ZTCs partition space into two disjoint labyrinths that can be described by a pair of interpenetrating nets. Since such a pair of nets also describes a triply periodic minimal surface (TPMS), our results establish the relationship between ZTCs and schwarzites—carbon materials with negative Gaussian curvature that resemble TPMSs—linking the research topics and demonstrating that schwarzites should no longer be thought of as purely hypothetical materials.

en: Generating carbon schwarzites via zeolite-templating

carbon-materials

display_name: carbon-materials

schwarzites

display_name: schwarzites

tpms

display_name: tpms

ztc

display_name: ztc


en: The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C-C cross-coupling reaction. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18,062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$/mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include earth abundant (Cu) transition metal with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates.

en: Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

homogeneous-catalysis

display_name: homogeneous-catalysis

machine-learning

display_name: machine-learning

transition-metal-complexes

display_name: transition-metal-complexes

volcano-plot

display_name: volcano-plot


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: Canonical molecular dynamics simulations of crystal growth from solution suffer from severe finite-size effects. As the crystal grows, the solute molecules are drawn from the solution to the crystal, leading to a continuous drop in the solution concentration. This is in contrast to experiments in which the crystal grows at an approximately constant supersaturation of a bulk solution. Recently, Perego et al. [J. Chem. Phys.2015, 142, 144113] showed that in a periodic setup in which the crystal is represented as a slab, the concentration in the vicinity of the two surfaces can be kept constant while the molecules are drawn from a part of the solution that acts as a molecular reservoir. This method is quite effective in studying crystallization under controlled supersaturation conditions. However, once the reservoir is depleted, the constant supersaturation conditions cannot be maintained. We propose a variant of this method to tackle this depletion problem by simultaneously dissolving one side of the crystal while letting the other side grow. A continuous supply of particles to the solution due to the crystal dissolution maintains a steady solution concentration and avoids reservoir depletion. In this way, a constant supersaturation condition can be maintained for as long as necessary. We have applied this method to study the growth and dissolution of urea crystal from water solution under constant supersaturation and undersaturation conditions, respectively. The computed growth and dissolution rates are in good agreement with those obtained in previous studies.

en: A Cannibalistic Approach to Grand Canonical Crystal Growth

crystal-nucleation-and-growth

display_name: crystal-nucleation-and-growth

erc

display_name: erc

solution-crystallization

display_name: solution-crystallization


materialscloud

en: Materials Cloud


en:
Classical turning surfaces of Kohn-Sham potentials separate classically-allowed regions (CARs) from classically-forbidden regions (CFRs). They are useful for understanding many chemical properties of molecules, but need not exist in solids, where the density never decays to zero. At equilibrium geometries, we find that CFRs are absent in perfect metals, rare in covalent semiconductors at equilibrium, but common in ionic and molecular crystals. In all materials, CFRs appear or grow as the internuclear distances are uniformly expanded. They can also appear at a monovacancy in a metal. Calculations with several approximate density functionals and codes confirm these behaviors. A classical picture of conduction suggests that CARs should be connected in metals, and disconnected in wide-gap insulators, and is confirmed in the limits of extreme compression and expansion. Surprisingly, many semiconductors have no CFR at equilibrium, a key finding for density functional construction. Nonetheless, a strong correlation with insulating behavior can still be inferred. Moreover, equilibrium bond lengths for all cases can be estimated from the bond type and the sum of the classical turning radii of the free atoms or ions.

This record contains machine readable data for this work

en: Calculation and interpretation of classical turning surfaces in solids

density-functional-theory

display_name: density-functional-theory

dft

display_name: dft

kohn-sham-potential

display_name: kohn-sham-potential

turning-surface

display_name: turning-surface


envi

en: Environment


en:
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent−organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps:
(1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory,
(2) fitting atomic point charges based on the electron density,
(3) characterizing the pore geometry of the structures before and after optimization,
(4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally,
(5) assessing the CO2 parasitic energy via process modeling.

The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

*** UPDATE December 2019 ***
 - Database extended to  include 417 COFs (from paper published until the 1st September 2019)
- Migration to AiiDA-v1.0.0
- Using the publicly available plugin aiida-lsmo

aiida

display_name: aiida

covalent-organic-framework

display_name: covalent-organic-framework

marvel

display_name: marvel

reproducibility

display_name: reproducibility

workflows

display_name: workflows


en:
We provide the input files to reproduce the data presented in the work:
Hidden Beneath the Surface: Origin of the Observed Enantioselective Adsorption on PdGa(111)
The files are subdivided in directories named after the figures/table of the manuscript
A. V. Yakutovich, J. Hoja, D. Passerone, Alexandre Tkatchenko, C. A. Pignedoli
J. Am. Chem. Soc., 140, 1401-1408 (2018) DOI: 10.1021/jacs.7b10980
In the work, we unravel the origin of the recently observed striking enantioselectivity of the PdGa(111) surface with respect to the adsorption of a small organic molecule, 9-ethynylphenanthrene, using first-principles calculations. It turns out that the key ingredient to understand the experimental evidence is the appropriate description of van der Waals interactions beyond the widely employed atomic pairwise approximation. A recently developed van der Waals-inclusive density functional method, which encompasses dielectric screening effects, reveals the origin of the experimentally observed enantioselectivity and provides conclusive evidence of chiral recognition on a bimetallic surface driven by dispersion interactions.

chirality

display_name: chirality

many-body-dispersion

display_name: many-body-dispersion

pdga

display_name: pdga


en: Hidden Beneath the Surface: Origin of the Observed Enantioselective Adsorption on PdGa(111)


en:
Fast-charging batteries typically employ electrodes capable of accommodating lithium continuously via solid-solution transformation because they have few kinetic barriers apart from Li+ diffusion. One exception is lithium titanate, an anode that exhibits extraordinary rate capability seemingly inconsistent with its two-phase reaction and slow diffusion within the two phases. Through real-time tracking of Li+ migration using operando electron energy-loss spectroscopy (EELS) along with simulation of the EELS spectra, we observe that the kinetic pathway that enables facile ionic transport in lithium titanate consists of distorted Li polyhedra in metastable intermediate states. Thus, fast-charging electrodes may not be controlled solely by the intrinsic ionic diffusivity of macroscopic phases, but also by the transport via kinetically accessible low-energy landscapes.

In order to understand the origin of various EELS spectra features, we simulate EELS spectra using the Vienna Ab initio Simulation (VASP) package. For a specific Li in a given configuration, this is done by calculating the DOS and integrated DOS considering a Li core-hole on the position of the specific Li and calculating the EELS based on the DOS. We also calculated the minimum energy paths (MEP) and migration energy of Li in various compositions, including Li4Ti5O12 with an additional Li carrier, Li5Ti5O12 with an additional Li carrier, and Li7Ti5O12 with a Li vacancy carrier. The calculation was performed using the nudged elastic band (NEB) method in VASP. Analysis of the VASP outputs was done using scripts from Transition State Tools for VASP (VTST, https://theory.cm.utexas.edu/vtsttools/index.html). We present here the relevant input and output files from the VASP calculations, the python code used to generate EELS spectra from VASP outputs, and the outputs of the VTST scripts that were used to generate MEP plots.

en: Kinetic Pathways of Ionic Transport in Fast Charging Lithium Titanate

ionic-transport

display_name: ionic-transport

nudged-elastic-band

display_name: nudged-elastic-band

vasp

display_name: vasp


en:
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent−organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps:
(1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory,
(2) fitting atomic point charges based on the electron density,
(3) characterizing the pore geometry of the structures before and after optimization,
(4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally,
(5) assessing the CO2 parasitic energy via process modeling.

The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

*** UPDATE December 2019 ***
 - Database extended to  include 417 COFs (from papers published until September 1st 2019)
- Migration to AiiDA-v1.0.0
- Using the publicly available plugin aiida-lsmo

*** UPDATE February 2020 ***
 - Database extended to  include 505 COFs (from papers published until February 1st 2020)

en: Building a consistent and reproducible database for adsorption evaluation in Covalent-Organic Frameworks


en: The study of organic molecular crystals under high pressure provides fundamental insight into crystal packing distortions and reveals mechanisms of phase transitions and the crystallization of polymorphs. These solid state transformations can be monitored directly by analyzing electron charge densities that are experimentally obtained at high pressure. However, restricting the analysis to the featureless electron density does not reveal the chemical bonding nature and the existence of intermolecular interactions. This shortcoming can be resolved by the use of the DORI (Density Overlap Region Indicator) descriptor, which is capable of detecting simultaneously both covalent patterns and non-covalent interactions from electron density and its derivatives. Using the biscarbonyl[14]annulene crystal under pressure as an example, we demonstrate how DORI can be exploited on experimental electron densities to reveal and monitor changes in electronic structure patterns resulting from molecular compression. A novel approach based on a flood fill type algorithm is proposed for analyzing the topology of the DORI isosurface. This approach avoids the arbitrarily selection of DORI isovalues and provides an intuitive way to assess how compression packing affects covalent bonding in organic solids.

en: DORI reveals the influence of non-covalent interactions on covalent bonding patterns in molecular crystals under pressure

high-pressure

display_name: high-pressure

molecular-scalar-fields

display_name: molecular-scalar-fields

organic-crystals

display_name: organic-crystals

topology

display_name: topology


en: Ice nucleation is a process of great relevance in physics, chemistry, technology, and environmental sciences; much theoretical effort has been devoted to its understanding, but it still remains a topic of intense research. We shed light on this phenomenon by performing atomistic based simulations. Using metadynamics and a carefully designed set of collective variables, reversible transitions between water and ice are able to be simulated. We find that water freezes into a stacking disordered structure with the all-atom transferable intermolecular potential with 4 points/ice (TIP4P/ice) model, and the features of the critical nucleus of nucleation at the microscopic level are revealed. We have also estimated the ice nucleation rates along with other nucleation parameters at different undercoolings. Our results are in agreement with recent experimental and other theoretical works, and they confirm that nucleation is preceded by a large increase in tetrahedrally coordinated water molecules.

en: Temperature Dependence of Homogeneous Nucleation in Ice

enhanced-sampling

display_name: enhanced-sampling

erc

display_name: erc

homegenous-nucleation

display_name: homegenous-nucleation

ice

display_name: ice

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics

plumed

display_name: plumed


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:
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent−organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps:
(1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory,
(2) fitting atomic point charges based on the electron density,
(3) characterizing the pore geometry of the structures before and after optimization,
(4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally,
(5) assessing the CO2 parasitic energy via process modeling.

The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

*** UPDATE December 2019 ***
 - Database extended to  include 417 COFs (from papers published until September 1st 2019)
- Migration to AiiDA-v1.0.0
- Using the publicly available plugin aiida-lsmo

*** UPDATE February 2020 ***
 - Database extended to  include 505 COFs (from papers published until February 1st 2020)
 - Including AiiDA Groups for quick interactive visualization

aiida

display_name: aiida

covalent-organic-framework

display_name: covalent-organic-framework

reproducibility

display_name: reproducibility

workflows

display_name: workflows


en: We describe a double helical conformation in the densely charged aromatic polyamide poly(2,2'- disulfonyl-4,4'-benzidine terephthalamide) or PBDT. This double helix macromolecule represents one of the most rigid simple molecular structures known, exhibiting an extremely high axial persistence length (~ 1 micrometer).

materialscloud

en: Materials Cloud

en: Double helix PBDT polymer - Submitted manuscript, simulations and other source data

ionic

display_name: ionic

nematic

display_name: nematic

nmr

display_name: nmr

polymer

display_name: polymer

simulations

display_name: simulations

x-ray

display_name: x-ray


envi

en: Environment


en: We present a derivation of the exact expression for Pulay forces in density-functional theory calculations augmented with extended Hubbard functionals, and arising from the use of orthogonalized atomic orbitals as projectors for the Hubbard manifold. The derivative of the inverse square root of the orbital overlap matrix is obtained as a closed-form solution of the associated Lyapunov (Sylvester) equation. The expression for the resulting contribution to the forces is presented in the framework of ultrasoft pseudopotentials and the projector-augmented-wave method, and using a plane wave basis set. We have benchmarked the present implementation with respect to finite differences of total energies for the case of NiO, finding excellent agreement. Owing to the accuracy of Hubbard-corrected density-functional theory calculations - provided the Hubbard parameters are computed for the manifold under consideration - the present work paves the way for systematic studies of solid-state and molecular transition-metal and rare-earth compounds.

en: Pulay forces in density-functional theory with extended Hubbard functionals: from nonorthogonalized to orthogonalized manifolds

cscs

display_name: cscs

density-functional-theory

display_name: density-functional-theory

dftu

display_name: dftu

dftuv

display_name: dftuv

hubbard-corrections

display_name: hubbard-corrections

hubbard-forces

display_name: hubbard-forces

hubbard-manifold

display_name: hubbard-manifold

hubbard-parameters

display_name: hubbard-parameters

lowdin-orthogonalization-method

display_name: lowdin-orthogonalization-method

lyapunov-equation

display_name: lyapunov-equation

orbital-overlap-matrix

display_name: orbital-overlap-matrix

pulay-forces

display_name: pulay-forces

self-interaction-corrections

display_name: self-interaction-corrections

sylvester-equation

display_name: sylvester-equation


marvel

display_name: marvel


en:
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent−organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps:
(1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory,
(2) fitting atomic point charges based on the electron density,
(3) characterizing the pore geometry of the structures before and after optimization,
(4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally,
(5) assessing the CO2 parasitic energy via process modeling.

The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

*** UPDATE December 2019 ***
 - Database extended to  include 417 COFs (from papers published until September 1st 2019)
- Migration to AiiDA-v1.0.0
- Using the publicly available plugin aiida-lsmo

*** UPDATE February 2020 ***
 - Database extended to  include 505 COFs (from papers published until February 1st 2020)
 - Including AiiDA Groups for quick interactive visualization

*** UPDATE June 2020 ***
 - Database extended to  include 574 COFs (from papers published until June 1st 2020)

aiida

display_name: aiida

covalent-organic-framework

display_name: covalent-organic-framework

erc

display_name: erc

marvelosp

display_name: marvelosp

reproducibility

display_name: reproducibility

workflows

display_name: workflows


en:
Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations and proton disorder. This is made possible by combining advanced free energy methods and state-of-the-art machine learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments, and reliable estimates of the melting points of light and heavy water. We observe that nuclear quantum effects contribute a crucial 0.2 meV/H2O to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine learning potentials as an intermediate step.

In this set of supplemental materials, we have included the neural network potential for bulk water, including its training set in two different formats. We have also included the input files for running free energy calculations.

en: Ab initio thermodynamics of liquid and solid water: supplemental materials

free-energy-calculation-input-files

display_name: free-energy-calculation-input-files

machine-learning-potential

display_name: machine-learning-potential

machine-learning-potential-training-set

display_name: machine-learning-potential-training-set


en: We present a large dataset of adsorption of H, C, N, O and S onto more than 2,000 metallic and bimetallic alloy surfaces, consisting of approximately 90,000 DFT calculations performed in Quantum Espresso. The alloys are constructed from all possible combinations of 37 metals into AB and A3B stoichiometries, in the L1_0 and L1_2 structures respectively, where the 37 metals in the A1 structure are included as well. Slabs are cleaved along the 111 facet for A1 and L1_2 and along the 101 facet for L1_0, and all possible adsorption sites are sampled. In addition to the monoatomic adsorbates, adsorption of CH, CH2, CH3, NH, NH2, OH, H2O and SH  is sampled for a smaller subset of alloys.

en: High-throughput calculations of catalytic properties of bimetallic alloy surfaces

chemisorption

display_name: chemisorption

dft

display_name: dft

heterogeneous-catalysis

display_name: heterogeneous-catalysis

surfaces

display_name: surfaces


en:
We present a workflow that traces the path from the bulk structure of a crystalline material to assessing its performance in carbon capture from coal's postcombustion flue gases. This workflow is applied to a database of 324 covalent−organic frameworks (COFs) reported in the literature, to characterize their CO2 adsorption properties using the following steps:
(1) optimization of the crystal structure (atomic positions and unit cell) using density functional theory,
(2) fitting atomic point charges based on the electron density,
(3) characterizing the pore geometry of the structures before and after optimization,
(4) computing carbon dioxide and nitrogen isotherms using grand canonical Monte Carlo simulations with an empirical interaction potential, and finally,
(5) assessing the CO2 parasitic energy via process modeling.

The full workflow has been encoded in the Automated Interactive Infrastructure and Database for Computational Science (AiiDA). Both the workflow and the automatically generated provenance graph of our calculations are made available on the Materials Cloud, allowing peers to inspect every input parameter and result along the workflow, download structures and files at intermediate stages, and start their research right from where this work has left off. In particular, our set of CURATED (Clean, Uniform, and Refined with Automatic Tracking from Experimental Database) COFs, having optimized geometry and high-quality DFT-derived point charges, are available for further investigations of gas adsorption properties. We plan to update the database as new COFs are being reported.

*** UPDATE December 2019 ***
 - Database extended to  include 417 COFs (from papers published until September 1st 2019)
- Migration to AiiDA-v1.0.0
- Using the publicly available plugin aiida-lsmo

*** UPDATE February 2020 ***
 - Database extended to  include 505 COFs (from papers published until February 1st 2020)
 - Including AiiDA Groups for quick interactive visualization

*** UPDATE June 2020 ***
 - Database extended to  include 574 COFs (from papers published until June 1st 2020)

*** UPDATE September 2020 ***
 - Include other applications than CCS, considering the same set of 574 COFs

en: Building a consistent and reproducible database for adsorption evaluation in Covalent-Organic Frameworks


en: Computational data to support an extensive study of prospective dopants in the zinc blende (gamma) phase of cuprous iodide. This phase of CuI holds the current record hole conductivity for intrinsic transparent p-type semiconductors. In the corresponding scientific article, a high-throughput approach was employed to systematically explore strategies for enhancing CuI further by impurity incorporation. Our objectives were to identify a practical approach for increasing hole conductivity in CuI thin films and to explore the possibility for ambivalent doping. A total of 64 chemical elements was investigated. This materials cloud record contains all optimized defective structures for Cu and I substitutional sites, calculation settings and some of the output (LOCPOT files, required for electrostatic correction calculations are not included due to their large size).  Chalcogen elements were found to display acceptor character when substituting iodine. Further eight impurities suitable for n-type doping were also discovered. Unfortunately, our work further revealed that donor doping is hindered by compensating native defects.

en: Dopant discovery in cuprous iodide

cui

display_name: cui

defects

display_name: defects

dopants

display_name: dopants

supercell

display_name: supercell

transparent-conductors

display_name: transparent-conductors


dopant-discovery-in-cuprous-iodide

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).

title_for_slug: dopant-discovery-in-cuprous-iodide


en: This is a LAMMPS readable EAM type potential file for Hf-Nb-Ta-Zr based high-entropy alloys (HEAs). In this potential file the elements are sequenced as Hf, Nb, Ta and Zr, respectively. This potential was previously used to model the HEA for the local lattice distortions due to short-range order clustering found in the alloy after long-term annealing at high-temperature. The input to build this potential is based on the physical properties of pure elements such as the lattice parameter, cohesive energy, elastic constants etc and the details are discussed in the associated reference. Units used are in eV/atom for energy and Angstrom for distance.

en: EAM potential for Hf-Nb-Ta-Zr high-entropy alloy

eam-potential

display_name: eam-potential

high-entropy-alloys

display_name: high-entropy-alloys

lammps

display_name: lammps

refractory-elements

display_name: refractory-elements


materialscloud

en: Materials Cloud


en: Zethrenes are model diradicaloids with potential applications in spintronics and optoelectronics. Despite a rich chemistry in solution, on-surface synthesis of zethrenes has never been demonstrated. This record contains data to support our recent publication where we report the on-surface synthesis of super-heptazethrene on Au(111). Scanning tunneling spectroscopy investigations reveal that super-heptazethrene exhibits an exceedingly low HOMO–LUMO gap of 230 meV and, in contrast to its open-shell singlet ground state in the solution phase and in the solid-state, likely adopts a closed-shell ground state on Au(111).

en: On-surface synthesis of super-heptazethrene

ab-initio

display_name: ab-initio

cscs

display_name: cscs

diradicaloid

display_name: diradicaloid

erc

display_name: erc

on-surface-synthesis

display_name: on-surface-synthesis

snsf

display_name: snsf


envi

en: Environment


en:
Frustrated Lewis pairs (FLPs), featuring reactive combinations of Lewis acids and Lewis bases, have been utilized for myriad metal-free homogeneous catalytic processes. Immobilizing the active Lewis sites to a solid support, especially to porous scaffolds, has shown great potential to ameliorate FLP catalysis by circumventing some of its inherent drawbacks, such as product separation and catalyst recyclability. Nevertheless, designing immobilized Lewis pair active sites (LPASs) is challenging due to the requirement of placing the donor and acceptor centers in appropriate geometric arrangements while maintaining the necessary chemical environment to perform catalysis, and clear design rules have not yet been established. In this work, we formulate simple guidelines to build highly active LPASs for direct catalytic hydrogenation of CO₂ through a large-scale screening of a diverse library of 25,000 immobilized FLPs.
The library is built by introducing boron-containing acidic sites in the vicinity of the existing basic nitrogen sites of the organic linkers of metal-organic frameworks collected in a "top-down" fashion from an experimental database. The chemical and geometrical appropriateness of these LPASs for CO₂ hydrogenation is determined by evaluating a series of simple descriptors representing the intrinsic strength (acidity and basicity) of the components and their spatial arrangement in the active sites. Analysis of the leading candidates enables the formulation of pragmatic and experimentally-relevant design principles and they constitute the starting point for further exploration of FLP-based catalysts for the reduction of CO₂.

en: Engineering  frustrated lewis pair active sites in porous organic scaffolds for catalytic CO₂ hydrogenation

catalysis

display_name: catalysis

epfl

display_name: epfl

frustrated-lewis-pairs

display_name: frustrated-lewis-pairs

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-catalysts

display_name: molecular-catalysts

volcano-plot

display_name: volcano-plot


en: Two-dimensional transition metal dichalcogenides (TMDs) of Mo and W in their 1T′ crystalline phase host the quantum spin Hall (QSH) insulator phase. We address the electronic properties of the QSH edge states by means of first-principles calculations performed on realistic models of edge terminations of different stoichiometries. The QSH edge states show a tendency to have complex band dispersions and coexist with topologically trivial edge states. We nevertheless identify two stable edge terminations that allow isolation of a pair of helical edge states within the band gap of TMDs, with monolayer 1T′-WSe2 being the most promising material. We also characterize the finite-size effects in the electronic structure of 1T′-WSe2 nanoribbons. Our results provide guidance to the experimental studies and possible practical applications of QSH edge states in monolayer 1T′-TMDs.

en: Controlling the quantum spin Hall edge states in two-dimensional transition metal dichalcogenides

edge-states

display_name: edge-states

marvel

display_name: marvel

quantum-spin-hall-effect

display_name: quantum-spin-hall-effect

topological-insulators

display_name: topological-insulators

transition-metal-dichalcogenides

display_name: transition-metal-dichalcogenides


en: We propose a general method to calculate the average misfit volumes of atoms in any random alloy via DFT calculations. The method is validated with an example of a 6-component equi-composition high entropy alloy. The special quasi-random structures (SQSs) used in our work are reported here.

en: Special quasi-random structures for the 6-component high entropy alloys

high-entropy-alloys

display_name: high-entropy-alloys

misfit-volumes

display_name: misfit-volumes

random-alloys

display_name: random-alloys

special-quasi-random-structures-sqss

display_name: special-quasi-random-structures-sqss


en: Metal-Organic Frameworks (MOFs) are highly versatile materials owing to their vast structural and chemical tunability. These hybrid inorganic-organic crystalline materials offer an ideal platform to incorporate light-harvesting and catalytic centers and thus, exhibit a great potential to be exploited in solar-driven photocatalytic processes such as H2 production and CO2 reduction. To be photocatalytically active, UV-visible optical absorption and appropriate band alignment with respect to the target redox potential is required. Despite fulfilling these criteria, the photocatalytic performance of MOFs is still limited by their ability to produce long-lived electron-hole pairs and long-range charge transport. In this work, we present a computational strategy to address these two descriptors in MOF structures and translate them into charge transfer numbers and effective mass values. We apply our approach to 15 MOF structures from the literature that encompass the main strategies used in the design of efficient photocatalysts including different metals, ligands, and topologies. Our results capture the main characteristics previously reported for these MOFs and enable us to identify promising candidates. In the quest of novel photocatalytic systems, high-throughput screening based on charge separation and charge mobility features is envisioned to be applied in large databases of both experimentally and in silico generated MOFs.

en: Charge separation and charge carrier mobility in photocatalytic metal-organic frameworks

charge-transfer

display_name: charge-transfer

erc

display_name: erc

mof

display_name: mof

photocatalysis

display_name: photocatalysis


epfl

display_name: epfl

snsf

display_name: snsf