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

envi

en: Environment


$oid: OBSOLETE

en: The small size, the location (embedded in the temporal bone), and the tiny movements of the three ossicles of the human middle ear make the study of sound transmission through the human auditory system extremely challenging. From the encouraging results of our previous investigations with dynamic synchrotron-based phase-contrast microtomography (proposal 20200954), we aim to further characterize the biomechanics of the human middle ear. The goal is to stimulate the middle ear with a broad range of sound frequencies, covering a wider spectrum of the human voice, together with a range of sound intensities meeting physiological criteria.

en: DYNAMITE: DYNamic phAse-contrast MIcro-Tomography of the human middle Ear

approval_status: approved

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

de: PSI Data Catalog

fr: PSI Data Catalog

it: PSI Data Catalog

political_level: other

state: active

type: organization

psi

capacity: public

de: PSI Open Data Provider

type: dataset

key: guid

key: uri

value: DCAT Harvester SciCat

de: Umwelt

fr: Environnement

it: Ambiente


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: This dataset has been used in the work titled ‘Metamorphic fabrics can be formed by stress without significant strain’ by Gilgannon et al. (2023). It was acquired at the TOMCAT beamline as part of a long term science proposal called ‘Implementing novel X-ray transparent triaxial deformation rigs to study fluid-rock interaction at mid-crustal conditions’. This research was funded through NERC standard grant NE/T001615/1.

en: Metamorphic fabrics can be formed by stress without significant strain - sample VA22


en: Metamorphic fabrics can be formed by stress without significant strain - sample VA_B


en: High-temperature and high-magnetic-field-induced re-entrant superconductivity has been discovered in the infinite-layer nickelate Sm1−x−yEuxCayNiO2 (SECNO). Infinite-layer nickelates are the closest known analogues of high-Tc cuprate superconductors, yet they host distinct magnetic ground states. Using low-energy muon spin relaxation and polarized neutron reflectometry, we reveal the magnetic order in SECNO. We find that magnetic freezing occurs at a higher-temperature than in other nickelate compounds, and that a substantial net magnetization of 55 kA m−1 ±10 kA m−1 emerges and remains largely unchanged across the superconducting transition. The magnetism in SECNO is disordered and nonuniform.

en: Net Magnetization and Inhomogeneous Magnetic Order in a High-Tc Nickelate Superconductor


en: The experiment aims at visualizing with high resolution stacking faults in InSb micropillars that were deformed in a microcompression experiment. The chosen method is ptychographic topography, a new method recently developed at the cSAXS beam line.

en: Visualization of stacking faults in InSb micropillars by ptychographic topography


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

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

value: DCAT Harvester Materials Cloud

bulk

display_name: bulk

dielectric-tensor

display_name: dielectric-tensor

dipole-moment

display_name: dipole-moment

hyperpolarizability

display_name: hyperpolarizability

machine-learning

display_name: machine-learning

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

envi

en: Environment

flying-gaussian-method

display_name: flying-gaussian-method

metadynamics

display_name: metadynamics

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

sampling

display_name: sampling


barcodes-for-nanoporous-materials

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

title_for_slug: 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


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

database

display_name: database

dft

display_name: dft

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

ice

display_name: ice

three-dimensional

display_name: three-dimensional


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


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

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: 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: 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:
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


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).

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

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

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


the-geometric-blueprint-of-perovskites

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

title_for_slug: the-geometric-blueprint-of-perovskites

envi

en: Environment

data-mining

display_name: data-mining

goldschmidt

display_name: goldschmidt

perovskites

display_name: perovskites

structure-predictions

display_name: structure-predictions


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

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.

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


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

mean-square-deviation

display_name: mean-square-deviation


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

marvel

display_name: marvel

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


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

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).

crystal-nucleation-and-growth

display_name: crystal-nucleation-and-growth

erc

display_name: erc

solution-crystallization

display_name: solution-crystallization


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

materialscloud

en: Materials Cloud

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


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.

envi

en: Environment

chirality

display_name: chirality

many-body-dispersion

display_name: many-body-dispersion

pdga

display_name: pdga


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: 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

homegenous-nucleation

display_name: homegenous-nucleation

ice

display_name: ice

marvel

display_name: marvel

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics

plumed

display_name: plumed


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).

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


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


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


dopant-discovery-in-cuprous-iodide

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

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

cui

display_name: cui

defects

display_name: defects

dopants

display_name: dopants

supercell

display_name: supercell

transparent-conductors

display_name: transparent-conductors


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

materialscloud

en: Materials Cloud

eam-potential

display_name: eam-potential

high-entropy-alloys

display_name: high-entropy-alloys

lammps

display_name: lammps

refractory-elements

display_name: refractory-elements


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

envi

en: Environment

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


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

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

epfl

display_name: epfl

mof

display_name: mof

photocatalysis

display_name: photocatalysis


en: The leucine-lysine amphiphilic peptide LKα14 has been used to study fundamental driving forces in processes such as peptide-surface binding and biomineralization. Here, we employ molecular dynamics (MD) simulations in tandem with replica exchange metadynamics to probe the binding mechanism and thermodynamics of LKα14 on silica. We also investigate the effect that the nature of the silica surface – crystalline vs. amorphous, has on the binding properties and peptide-surface conformations. We find that water adsorbs differently on both surfaces; it forms a denser interfacial layer on the crystalline surface, compared to the amorphous surface. This causes the peptide to bind more strongly on the amorphous surface than the crystalline surface. Cluster analysis shows that the peptide adopts a helical conformation at both surfaces, with a greater distribution of states on the crystalline surface. Peptide binding is primarily through lysine interactions, in line with prior experimental results.

en: Amphiphilic Peptide Binding on Crystalline vs. Amorphous Silica from Molecular Dynamics Simulations

enhanced-sampling

display_name: enhanced-sampling

molecular-dynamics-simulations

display_name: molecular-dynamics-simulations

peptide-surface-interactions

display_name: peptide-surface-interactions


en: The most successful and popular machine learning models of atomic-scale properties derive their transferability from a locality ansatz. The properties of a large molecule or a bulk material are written as a sum over contributions that depend on the configurations within finite atom-centered environments. The obvious downside of this approach is that it cannot capture nonlocal, nonadditive effects such as those arising due to long-range electrostatics or quantum interference. We propose a solution to this problem by introducing nonlocal representations of the system, which are remapped as feature vectors that are defined locally and are equivariant in O(3). We consider, in particular, one form that has the same asymptotic behavior as the electrostatic potential. We demonstrate that this framework can capture nonlocal, long-range physics by building a model for the electrostatic energy of randomly distributed point-charges, for the unrelaxed binding curves of charged organic molecular dimers, and for the electronic dielectric response of liquid water. By combining a representation of the system that is sensitive to long-range correlations with the transferability of an atom-centered additive model, this method outperforms current state-of-the-art machine-learning schemes and provides a conceptual framework to incorporate nonlocal physics into atomistic machine learning.

en: Incorporating long-range physics in atomic-scale machine learning

long-range-interactions

display_name: long-range-interactions

machine-learning

display_name: machine-learning


en: We investigate formation energies of C, Si, and Ge defects in β-Ga2O3 through hybrid functional calculations. We find that the interstitial defects of these elements generally occur at higher energies than their substitutional counterparts, while they are more stable at low Fermi energies in Ga-rich conditions. In n-type and Ga-rich conditions, interstitials of Si and Ge show significantly higher formation energies than their substitutional form, but this difference is less pronounced for C. Charge transition levels of interstitial defects lie in the upper part of the band-gap, and account for several measured levels in unintentionally doped and Ge-doped samples of β-Ga2O3.

en: Defect Formation Energies of Interstitial C, Si, and Ge Impurities in β-Ga2O3

defects

display_name: defects

hybrid-functional

display_name: hybrid-functional


en: In this work, we investigate the transverse magnetoresistance of materials by combining the Fermi surfaces calculated from first principles with the Boltzmann transport theory approach relying on the semiclassical model and the relaxation time approximation. We first consider a series of simple model Fermi surfaces to provide a didactic introduction into the charge-carrier compensation and open-orbit mechanisms leading to nonsaturating magnetoresistance. We then address in detail magnetotransport in three representative materials: (i) copper,  (ii) bismuth,  and (iii) tungsten diphosphide. Furthermore, the calculations allow for a full interpretation of the observed features in terms of the Fermi surface topology. Our study thus establishes guidelines to clarifying the physical mechanisms underlying the magnetotransport properties in a broad range of materials.

en: Magnetoresistance from Fermi surface topology

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).

boltzmann-transport-theory

display_name: boltzmann-transport-theory

fermi-surface

display_name: fermi-surface

first-principles

display_name: first-principles

magnetoresistance

display_name: magnetoresistance

marvel

display_name: marvel

wanniertools

display_name: wanniertools


en: Maximally-localised Wannier functions (MLWFs) are routinely used to compute from first-principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations. At the same time, high-throughput (HT) computational materials design is an emergent field that promises to accelerate the reliable and cost-effective design and optimisation of new materials with target properties. The use of MLWFs in HT workflows has been hampered by the fact that generating MLWFs automatically and robustly without any user intervention and for arbitrary materials is, in general, very challenging. We address this problem directly by proposing a procedure for automatically generating MLWFs for HT frameworks. Our approach is based on the selected columns of the density matrix method (SCDM) and we present the details of its implementation in an AiiDA workflow. We apply our approach to a dataset of 200 bulk crystalline materials that span a wide structural and chemical space. We assess the quality of our MLWFs in terms of the accuracy of the band-structure interpolation that they provide as compared to the band-structure obtained via full first-principles calculations. We provide here an AiiDA export file with the full provenance of all simulations run in the project. Moreover, we provide a downloadable virtual machine that allows to reproduce the results of this paper and also to run new calculations for different materials, including all first-principles and atomistic simulations and the computational workflows.

materialscloud

en: Materials Cloud

aiida-workflow

display_name: aiida-workflow

automated-wannierisation

display_name: automated-wannierisation

band-structure-interpolation

display_name: band-structure-interpolation

high-throughput-simulations

display_name: high-throughput-simulations

marvelosp

display_name: marvelosp

maximally-localised-wannier-functions

display_name: maximally-localised-wannier-functions

scdm

display_name: scdm


en: Nonbenzenoid carbocyclic rings are postulated to serve as important structural elements toward tuning the chemical and electronic properties of extended polycyclic aromatic hydrocarbons (PAHs, or namely nanographenes), necessitating a rational and atomically precise synthetic approach toward their fabrication. This record contains data supporting a recent work where, using a combined bottom-up in-solution and on-surface synthetic approach, we report the synthesis of nonbenzenoid open-shell nanographenes containing two pairs of embedded pentagonal and heptagonal rings.

en: Open-shell Non-benzenoid Nanographenes Containing Two Pairs of Pentagonal and Heptagonal Rings

ab-initio

display_name: ab-initio

nanographene

display_name: nanographene

on-surface-synthesis

display_name: on-surface-synthesis

polycyclic-aromatic-hydrocarbons

display_name: polycyclic-aromatic-hydrocarbons

scanning-tunneling-microscopy

display_name: scanning-tunneling-microscopy


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

*** UPDATE October 2020 ***
 - Database extended to include 626 COFs (from papers published until October 1st 2020)

*** UPDATE February 2021 ***
 - Database extended to include 632 COFs (from papers published until February 1st 2021). We added a total of 61 new structures but we discarded 55: see CURATED-COFs GitHub for more details.

*** UPDATE June 2021 ***
 - Database extended to include 648 valid COFs (from papers published until June 1st 2021) plus 80 discarded: see CURATED-COFs GitHub for more details.

*** UPDATE July 2022 ***
 - Database extended to include 871 valid COFs (from papers published until July 1st 2022) plus 110 discarded: see CURATED-COFs GitHub for more details.

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

envi

en: Environment

aiida

display_name: aiida

covalent-organic-framework

display_name: covalent-organic-framework

erc

display_name: erc

reproducibility

display_name: reproducibility

workflows

display_name: workflows


en: Oxygen vacancies are a common source of excess electrons in complex oxides. In Mott insulators, these additional electrons can induce a metal-insulator transition (MIT), fundamentally altering the electronic properties of the  system. Here we study the effect of oxygen vacancies in LaTiO3, a prototypical Mott insulator close to the MIT. We show that the introduction of oxygen vacancies creates a vacancy-related band immediately below the partially filled Ti-t 2g bands. We study the effect of this additional band on the Mott MIT  using a combination of density functional theory and dynamical mean-field theory (DFT+DMFT), employing a minimal correlated subspace consisting of effective Ti-t 2g orbitals plus an additional Wannier function centered on the vacancy site. We find that the Mott insulating state in LaTiO3 is robust to the presence of the vacancy band, which remains fully occupied even in the presence of a local Coulomb repulsion, and therefore does not cause a doping of the Mott insulator

en: DFT+DMFT study of oxygen vacancies in a Mott insulator

defects

display_name: defects

dftdmft

display_name: dftdmft

electronic-structure

display_name: electronic-structure

mott-insulators

display_name: mott-insulators

oxygen-vacancies

display_name: oxygen-vacancies


en: We investigate the synergy occurring in photocatalytic water splitting between the metal-organic framework MIL-125-NH2 and two co-catalysts, namely NiO and Ni2P, by calculating their band edge alignment with respect to the redox levels of liquid water. For the NiO/H2O and Ni2P/H2O interfaces, we employ an explicit atomistic description of water and perform molecular dynamics simulations considering both molecular and dissociated water adsorbed at the co-catalyst surface. For the MIL-125-NH2/NiO and MIL-125-NH2/Ni2P interfaces, we rely on the concept of charge neutrality and use a scheme combining the electron affinities and the charge neutrality levels of the interface components. We provide a description of the underlying fundamental processes that is consistent with photoluminescence and intrinsic activity experiments and that supports NiO and Ni2P as suitable co-catalysts for MIL-125-NH2 as far as the hydrogen evolution reaction is concerned.

en: Unraveling the synergy between metal-organic frameworks and co-catalysts in photocatalytic water splitting

co-catalyst

display_name: co-catalyst

density-functional-calculations

display_name: density-functional-calculations

first-principles-calculations

display_name: first-principles-calculations

hydrogen-evolution

display_name: hydrogen-evolution

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-dynamics

display_name: molecular-dynamics

photocatalysis

display_name: photocatalysis

water-splitting

display_name: water-splitting


en: We investigate the solvation effect of water on the overpotentials of the oxygen evolution reaction on rutile TiO2 by applying the thermodynamic integration method on atomistic model interfaces with and without the water molecules. We compare the results at the vacuum interface with the commonly used computational hydrogen electrode method, finding overall good agreement. The effect of the solvent is found to be twofold. First, the explicit treatment of the solvent can lead to equilibrium configurations differing from the relaxed structures without solvent. Second, the overpotentials can be affected by up to 0.5 eV. The energetics are subject to electrostatic effects at the interface rather than to modifications in the hydrogen bond network. These results provide a promising perspective for treating the solvent with implicit models.

en: Effect of the Solvent on the Oxygen Evolution Reaction at the TiO2-Water Interface

explicit-solvent

display_name: explicit-solvent

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

rutile

display_name: rutile

solvation-effect

display_name: solvation-effect

water

display_name: water


en: We describe a nonempirical procedure for achieving accurate band gaps of extended systems through the insertion of suitably defined potential probes. By enforcing Koopmans' condition on the resulting localized electronic states, we determine the optimal fraction of Fock exchange to be used in the adopted hybrid functional. As potential probes, we consider point defects, the hydrogen interstitial, and various adjustable potentials that allow us to vary the energy level of the localized state in the band gap. By monitoring the delocalized screening charge, we achieve a measure of the degree of hybridization with the band states, which can be used to improve the band-gap estimate. Application of this methodology to AlP, C, and MgO yields band gaps differing by less than 0.2 eV from experiment.

en: Adjustable potential probes for band-gap predictions of extended systems through nonempirical hybrid functionals

band-gap

display_name: band-gap

first-principles

display_name: first-principles

hybrid-functional

display_name: hybrid-functional

point-defect

display_name: point-defect

potential-probe

display_name: potential-probe


en: In this work, we investigate the angle-dependent magnetoresistance (AMR) of the layered nodal-line Dirac semimetal ZrSiS for the in-plane and out-of-plane current directions. Combining the Fermi surfaces calculated from first principles with the Boltzmann's semiclassical transport theory, we reproduce all the prominent features of the unusual behavior of the in-plane and out-of-plane AMR.We can conclude that the dominant contribution the cusplike AMR lies in open orbits of the hole pocket and, in general, AMR is strongly influenced by charge compensation effect and the off-diagonal conductivity tensor elements, which give rise to peculiar butterfly-shaped AMR.

en: Highly anisotropic interlayer magnetoresitance in ZrSiS nodal-line Dirac semimetal

boltzmann-transport-theory

display_name: boltzmann-transport-theory

fermi-surface

display_name: fermi-surface

magnetoresistance

display_name: magnetoresistance

marvel

display_name: marvel

wanniertools

display_name: wanniertools


en: Two-dimensional materials are emerging as a promising platform for ultrathin channels in field-effect transistors. To this aim, novel high-mobility semiconductors need to be found or engineered. Although extrinsic mechanisms can in general be minimized by improving fabrication processes, the suppression of intrinsic scattering (driven, for example, by electron–phonon interactions) requires modification of the electronic or vibrational properties of the material. Because intervalley scattering critically affects mobilities, a powerful approach to enhance transport performance relies on engineering the valley structure. We show here the power of this strategy using uniaxial strain to lift degeneracies and suppress scattering into entire valleys, dramatically improving performance. This is shown in detail for arsenene, where a 2% strain stops scattering into four of the six valleys and leads to a 600% increase in mobility. The mechanism is general and can be applied to many other materials, including in particular the isostructural antimonene and blue phosphorene. In this entry we provide the AiiDA database with the calculation of the electron-phonon matrix elements for arsenene, both in the equilibrium and in the strained case, together with scripts to retrieve and plot them.

en: Valley-Engineering Mobilities in Two-Dimensional 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).

electronphonon-scattering

display_name: electronphonon-scattering

intervalley

display_name: intervalley

transport

display_name: transport


en: Using a density-functional framework, we investigate the vibrational spectra of vitreous SiO2 to determine to what extent these spectra provide information about the medium-range structure of the oxide network. We carry out a comparative study involving three model structures, which all feature a nondefective network of corner-sharing tetrahedra but differ through their Si-O-Si bond-angle distributions and ring statistics. We first address the results of typical diffraction probes. Fair agreement with experiment is achieved for the total neutron and total x-ray structure factors of all models, indicating limited sensitivity of these structure factors to the medium-range structure. The same consideration also applies to the Si-O and O-O partial structure factors. At variance, the Si-Si partial structure factor is found to be highly sensitive to the Si-O-Si bond-angle distribution. We then address typical vibrational spectra, such as the inelastic neutron spectrum, the infrared spectra, and the Raman spectra. Our study indicates that the considered experimental data are globally consistent with a medium-range structure characterized by an average Si-O-Si bond angle of 148° and with small-ring concentrations as derived from the intensities of the experimental Raman defect lines. To describe the infrared and Raman couplings, our work also introduces parametric models which reproduce well the spectra calculated from first principles.

en: Medium-range structure of vitreous SiO2

materialscloud

en: Materials Cloud

cscs

display_name: cscs

dft

display_name: dft

epfl

display_name: epfl

infrared

display_name: infrared

raman

display_name: raman

snsf

display_name: snsf


en: The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost. Applications are shown for various hydrocarbon molecules of increasing complexity and flexibility, and demonstrate the accuracy of the model when predicting the density on octane and octatetraene after training exclusively on butane and butadiene. This transferable, data-driven model can be used to interpret experiments, accelerate electronic structure calculations, and compute electrostatic interactions in molecules and condensed-phase systems.

en: Transferable Machine-Learning Model of the Electron Density

envi

en: Environment

electron-density

display_name: electron-density

machine-learning

display_name: machine-learning


en: Distinct to type-I Weyl semimetals (WSMs) that host quasiparticles described by the Weyl equation, the energy dispersion of quasiparticles in type-II WSMs violates Lorentz invariance and the Weyl cones in the momentum space are tilted. Since it was proposed that type-II Weyl fermions could emerge from WTe2, MoTe2, WP2 and MoP2 families of materials, a large number of experiments have been dedicated to unveiling the possible manifestation of type-II WSMs, e.g., surface-state Fermi arcs. However, the interpretations of the experimental results are very controversial. Here, using angle-resolved photoemission spectroscopy supported by the first-principles calculations, we probe the tilted Weyl cone bands in the bulk electronic structure of WP2 directly, which are at the origin of Fermi arcs at the surfaces and transport properties related to the chiral anomaly in type-II WSMs. Our results ascertain that, due to the spin-orbit coupling, the Weyl nodes originate from the splitting of fourfold degenerate band-crossing points with Chern numbers C=2 or  C=-2, which is unique among all the discovered WSMs. Our finding also provides a guiding line to observe the chiral anomaly that could manifest in novel transport properties. Input files for theoretical calculations and raw data from ARPES measurements are included in this record.

en: Observation of Weyl Nodes in Robust Type-II Weyl Semimetal WP2

arpes

display_name: arpes

type-ii-weyl-semimetal

display_name: type-ii-weyl-semimetal

vasp

display_name: vasp


en:
From the Ising model and the Lennard-Jones fluid to water and the iron-carbon system, phase diagrams are an indispensable tool to understand phase equilibria. Despite the effort of the simulation community, the calculation of a large portion of a phase diagram using computer simulation is still today a significant challenge. Here, we propose a method to calculate phase diagrams involving liquid and solid phases by the reversible transformation of the liquid and the solid. To this end, we introduce an order parameter that breaks the rotational symmetry and we leverage our recently introduced method to sample the multithermal-multibaric ensemble. In this way, in a single molecular dynamics simulation, we are able to compute the liquid-solid coexistence line for entire regions of the temperature and pressure phase diagram. We apply our approach to the bcc-liquid phase diagram of sodium and the fcc-bcc-liquid phase diagram of aluminum.

This repository contains the input files to reproduce the results of the paper mentioned above. The results are divided into two zip files Al.zip and Na.zip for simulations of aluminum and sodium, respectively. Inside each folder there is an example for the calculation of free energies at constant temperature and pressure, and the input files and results of the multithermal-multibaric simulations.

en: Calculation of phase diagrams in the multithermal-multibaric ensemble

aluminum

display_name: aluminum

enhanced-sampling

display_name: enhanced-sampling

free-energy

display_name: free-energy

multithermal-multibaric

display_name: multithermal-multibaric

phase-diagrams

display_name: phase-diagrams

sodium

display_name: sodium

ves

display_name: ves


en:
In a recent work, we demonstrated the bottom-up on-surface synthesis of poly(para-dibenzo[bc,kl]-coronenylene) , a zigzag edge-encased analog of poly(para-phenylene), and its lateral fusion into zigzag edge-extended graphene nanoribbons.
The record contains data to reproduce the calculations that were performed at Empa to support the findings discussed in the manuscript.

en: Graphene Nanoribbons Derived From Zigzag Edge-Encased Poly(para-2,9-dibenzo[bc,kl]coronenylene) Polymer Chains

ab-initio

display_name: ab-initio

garphene-nanoribbons

display_name: garphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis


en: We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whether the structural diversity found in this database can be well-represented by classical descriptors, such as distances, angles, and ring sizes, or whether a more general representation of the atomic structure, furnished by the smooth overlap of atomic position (SOAP) method, is required to capture accurately structure–property relations. We assessed the quality of each descriptor by machine-learning the molar energy and volume for each hypothetical framework in the dataset. We have found that a SOAP representation with a cutoff length of 6 Å, which goes beyond near-neighbor tetrahedra, best describes the structural diversity in the Deem database by capturing relevant interatomic correlations. Kernel principal component analysis shows that SOAP maintains its superior performance even when reducing its dimensionality to those of the classical descriptors and that the first three kernel principal components capture the main variability in the dataset, allowing a 3D point cloud visualization of local environments in the Deem database. This "cloud atlas" of local environments was found to show good correlations with the contribution of a given motif to the density and stability of its parent framework. Local volume and energy maps constructed from the SOAP/machine learning analyses provide new images of zeolites that reveal smooth variations of local volumes and energies across a given framework and correlations between the contributions to volume and energy associated with each atom-centered environment.

en: A New Kind of Atlas of Zeolite Building Blocks

erc

display_name: erc

marvel

display_name: marvel

sccer

display_name: sccer

zeolites

display_name: zeolites


en: This record mainly upload all the raw experimental data related to the manuscript. Fig1 include the transport data, magnetotransport data, AHE data, ESR data, uSR data. These data related to the conclusion that spin fluctuation established in the paramagnetic phase. Fig2 contains in-plane and out-of-plane 3D photoemission spectra, and high resolution cuts on top and side surface. Fig3 contains band electronic structure along high symmetry lines with spin along all kinds of typical directions. Fig4 contains 3-D in-plane and out-of-plane Fermi surfaces on the 101 cleavage, high resolution cuts, STM/STS data. The proved the observation of Weyl points.

en: Spin fluctuation induced Weyl semimetal state in the paramagnetic phase of EuCd2As2

arpes

display_name: arpes

magnetic-material

display_name: magnetic-material

magnetic-weyl-semimetal

display_name: magnetic-weyl-semimetal

spin-fluctuation

display_name: spin-fluctuation

topological-material

display_name: topological-material


en: Coronoids, polycyclic aromatic hydrocarbons with geometrically defined cavities, are promising model structures of porous graphene. This record contains data to support our recent research where we report the on-surface synthesis of C168 and C140 coronoids, referred to as [6]- and [5]coronoid, respectively, using 5,9-dibromo-14-phenylbenzo[m]tetraphene as the precursor. These coronoids entail large cavities (>1 nm) with inner zigzag edges, distinct from their outer armchair edges. While [6]coronoid is planar, [5]coronoid is not. Low-temperature scanning tunneling microscopy/spectroscopy and noncontact atomic force microscopy unveil structural and electronic properties in accordance with those obtained from density functional theory calculations. Detailed analysis of ring current effects identifies the rings with the highest aromaticity of these coronoids, whose pattern matches their Clar structure. The pores of the obtained coronoids offer intriguing possibilities of further functionalization toward advanced host–guest applications.

en: Large-cavity coronoids with different inner and outer edge structures

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).

aromaticity

display_name: aromaticity

coronoid

display_name: coronoid

cscs

display_name: cscs

snsf

display_name: snsf


en: This entry provides the snapshots of liquid water generated with ab initio molecular dynamics using rVV10 density functional at room temperature. Nuclear quantum effects are taken into account through path-integral molecular dynamics simulations.

en: Ab initio electronic structure of liquid water: Molecular dynamics snapshots

materialscloud

en: Materials Cloud

liquid-water

display_name: liquid-water

nuclear-quantum-effect

display_name: nuclear-quantum-effect


en: Several enhanced sampling methods, such as umbrella sampling or metadynamics, rely on the identification of an appropriate set of collective variables. Recently two methods have been proposed to alleviate the task of determining efficient collective variables. One is based on linear discriminant analysis; the other is based on a variational approach to conformational dynamics and uses time-lagged independent component analysis. In this paper, we compare the performance of these two approaches in the study of the homogeneous crystallization of two simple metals. We focus on Na and Al and search for the most efficient collective variables that can be expressed as a linear combination of X-ray diffraction peak intensities. We find that the performances of the two methods are very similar. Wherever the different metastable states are well-separated, the method based on linear discriminant analysis, based on its harmonic version, is to be preferred because simpler to implement and less computationally demanding. The variational approach, however, has the potential to discover the existence of different metastable states.

en: Improving collective variables: The case of crystallization

envi

en: Environment

crystalization

display_name: crystalization

enhanced-sampling

display_name: enhanced-sampling

hlda

display_name: hlda

plumed

display_name: plumed


en: Rare-earth nickelates exhibit a metal–insulator transition accompanied by a structural distortion that breaks the symmetry between formerly equivalent Ni sites. The quantitative theoretical description of this coupled electronic–structural instability is extremely challenging. Here, we address this issue by simultaneously taking into account both structural and electronic degrees of freedom using a charge self-consistent combination of density functional theory and dynamical mean-field theory, together with screened interaction parameters obtained from the constrained random phase approximation. Our total energy calculations show that the coupling to an electronic instability toward a charge disproportionated insulating state is crucial to stabilize the structural distortion, leading to a clear first order character of the coupled transition. The decreasing octahedral rotations across the series suppress this electronic instability and simultaneously increase the screening of the effective Coulomb interaction, thus weakening the correlation effects responsible for the metal–insulator transition. Our approach allows to obtain accurate values for the structural distortion and thus facilitates a comprehensive understanding, both qualitatively and quantitatively, of the complex interplay between structural properties and electronic correlation effects across the nickelate series.

en: Energetics of the coupled electronic–structural transition in the rare-earth nickelates

dynamical-mean-field-theory

display_name: dynamical-mean-field-theory

electronic-properties-and-materials

display_name: electronic-properties-and-materials

marvel

display_name: marvel

quantum-materials

display_name: quantum-materials


en: We present all AMONs for GDB and Zinc data-bases using no more than 7 non-hydrogen atoms (AGZ7)---a calculated organic chemistry building-block dictionary based on the AMON approach [Huang and von Lilienfeld, Nature Chemistry (2020)]. AGZ7 records Cartesian coordinates of compositional and constitutional isomers, as well as properties for ∼140k small organic molecules obtained by systematically fragmenting all molecules of Zinc and the majority of GDB17 into smaller entities, saturating with hydrogens, and containing no more than 7 heavy atoms (excluding hydrogen atoms). AGZ7 cover the elements H, B, C, N, O, F, Si, P, S, Cl, Br, Sn and I and includes optimized geometries, total energy and its decomposition, Mulliken atomic charges, dipole moment vectors, quadrupole tensors, electronic spatial extent, eigenvalues of all occupied orbitals, LUMO, gap, isotropic polarizability, harmonic frequencies, reduced masses, force constants, IR intensity, normal coordinates, rotational constants, zero-point energy, internal energy, enthalpy, entropy, free energy, and heat capacity (all at ambient conditions) using B3LYP/cc-pVTZ (pseudopotentials were used for Sn and I) level of theory. We exemplify the usefulness of this data set with AMON based machine learning models of total potential energy predictions of seven of the most rigid GDB-17 molecules.

en: Dictionary of 140k GDB and ZINC derived AMONs

building-blocks

display_name: building-blocks

erc

display_name: erc

organic-chemical-space

display_name: organic-chemical-space

quantum-machine-learning

display_name: quantum-machine-learning


en: Interatomic potentials are often necessary to describe complex realistic systems that would be too costly to study from first-principles. Commonly, interatomic potentials are designed using functional forms driven by physical intuition and fitted to experimental or computational data. The moderate flexibility of these functional forms limits their ability to be systematically improved by increasing the fitting datasets; on the other hand, their qualitative description of the essential physical interactions ensures a modicum degree of transferability. Recently, a novel trend has emerged where potential-energy surfaces are represented by neural networks fitted on large numbers of first-principles calculations, thus maximizing flexibility but requiring extensive datasets to ensure transferability. Gaussian Approximation Potentials in particular are a novel class of potentials based on non-linear, non-parametric Gaussian-process regression. Here we generate a Gaussian Approximation model for the α-phase of iron training on energies, stresses and forces taken from first-principles molecular dynamics simulations of pristine and defected bulk systems, of surfaces and γ-surfaces with different crystallographic orientations.

en: Gaussian Approximation Potentials for iron from extended first-principles database (Data Download)

artificial-neural-networks

display_name: artificial-neural-networks

gaussian-approximation-potentials

display_name: gaussian-approximation-potentials

interatomic-potentials

display_name: interatomic-potentials

iron

display_name: iron

machine-learning

display_name: machine-learning


en:
With the growth of natural gas as an energy source, upgrading CO2-contaminated supplies has become increasingly important. Here we develop a single metric that captures how well an adsorbent performs the separation of CH4 and CO2, and we then use this metric to computationally screen tens of thousands of all-silica zeolites. We show that the most important predictors of separation performance are the CO2 heat of adsorption (Qst, CO2) and the CO2 saturation loading capacity. We find that a higher-performing material results when the absolute value of the CH4 heat of adsorption (Qst, CH4) is decreased independently of Qst, CO2, but a correlation that exists between Qst, CH4 and Qst, CO2 in all-silica zeolites leads to incongruity between the objectives of optimizing Qst, CO2 and minimizing Qst, CH4, rendering Qst, CH4 nonpredictive of separation performance. We also conduct a large-scale analysis of ideal adsorbed solution theory (IAST) by comparing results obtained using directly-generated mixture isotherms to those obtained using IAST; IAST appears adequate for the purposes of establishing performance trends and structure–property relationships in a high-throughput manner, but it must be tested for validity when analyzing individual adsorbents in detail since it can produce significant errors for materials in which there is site segregation of the adsorbate species.

Version 2 provides the structures in CIF format.

en: High-throughput computational screening of nanoporous adsorbents for CO 2 capture from natural gas

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

iza

display_name: iza

methane-storage

display_name: methane-storage

nanoporous

display_name: nanoporous

porous-polymer-networks

display_name: porous-polymer-networks

three-dimensional

display_name: three-dimensional

zeolites

display_name: zeolites


enhanced-sampling-of-transition-states

en: The free energy landscapes of several fundamental processes are characterized by high barriers separating long-lived metastable states. In order to explore these type of landscapes enhanced sampling methods are used. While many such methods are able to obtain sufficient sampling in order to draw the free energy, the transition states are often sparsely sampled. We propose an approach based on the Variationally Enhanced Sampling Method to enhance sampling in the transition region. To this effect, we introduce a dynamic target distribution which uses the derivative of the instantaneous free energy surface to locate the transition regions on the fly and modulate the probability of sampling different regions. Finally, we exemplify  the effectiveness of this approach in enriching the number of configurations in the transition state region in the cases of a chemical reaction and of a nucleation process.

en: Enhanced sampling of transition states

title_for_slug: enhanced-sampling-of-transition-states

critical-clusters

display_name: critical-clusters

enhanced-sampling

display_name: enhanced-sampling

molecular-modelling

display_name: molecular-modelling

nucleation

display_name: nucleation

transition-states

display_name: transition-states


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

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).

database

display_name: database


en: Dipole polarizabilities (and other molecular properties) computed using linear response coupled cluster theory (LR-CCSD/d-aug-cc-pVDZ) and hybrid density functional theory (B3LYP/d-aug-cc-pVDZ, SCAN0/d-aug-cc-pVDZ, and B3LYP/d-aug-cc-pVTZ) for the 7,211 molecules in the QM7b database and the 52 molecules in the AlphaML showcase database.

en: Quantum Mechanical Static Dipole Polarizabilities in the QM7b and AlphaML Showcase Databases

materialscloud

en: Materials Cloud

density-functional-theory

display_name: density-functional-theory

dipole-polarizability

display_name: dipole-polarizability

linear-response-coupled-cluster

display_name: linear-response-coupled-cluster


en: Data for the case study of TuTraSt on methane diffusion in zeolites, using a standard kinetic Monte Carlo simulation based on the output of our grid analysis. We find that it is accurate, fast, and rigorous without limitations to the geometries of the diffusion tunnels or transition states.

en: TuTraST data for methane diffusion in IZA structures

envi

en: Environment

diffusion

display_name: diffusion

kinetic-monte-carlo

display_name: kinetic-monte-carlo

marvel

display_name: marvel

methane

display_name: methane

transition-state-theory

display_name: transition-state-theory

tutrast

display_name: tutrast

zeolite

display_name: zeolite