AndreaBozzo/ceres-open-data-index huggingface dataset:
Can metal–organic frameworks be used for cannabis breathalyzers?

∆9-tetrahydrocannabinol (THC) is the principal psychoactive component of cannabis, and there is an urgent need to build low-cost and portable devices that can detect its presence from breath. Similarly to alcohol detectors, these tools can be used by law enforcement to determine driver intoxication and enforce safer and more regulated use of cannabis. In this work, we propose to use a class of microporous crystals, metal–organic frameworks (MOFs), to selectively adsorb THC that can be later detected using optical, electrochemical, or fluorescence-based sensing methods. We screened computationally more than 5000 MOFs, highlighting the materials that have the largest affinity with THC, as well as the highest selectivity against water, showing that it is thermodynamically feasible for MOFs to adsorb THC from humid breath. We propose and compare different models for THC and different computational protocols to rank the promising materials, also presenting a novel approach to assess the permeability of nonspherical molecules in a porous framework. We identified three adsorption motifs in MOFs with high affinity to THC, which we refer to as "narrow channels", "thick walls" and "parking spots". Therefore, we expect our protocols and our findings to be generalizable for different classes of microporous materials, and also for investigating the adsorption properties of other large molecules that, like THC, have a nonspherical shape.

Tags: cannabis-breathalyzer, gas-adsorption, metal-organic-frameworks, tetrahydrocannabinol, thc
Barcodes for nanoporous materials

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.

Tags: marvel, metal-organic-frameworks, nanoporous-materials, persistence-homology, topological-data-analysis, zeolites
Mapping uncharted territory in ice from zeolite networks to ice structures

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.

Tags: 3d, database, dft, first-principles, high-throughput, ice, three-dimensional, water
Simulating the ghost: quantum dynamics of the solvated electron

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.

Tags: cscs, machine-learning, marvel, moller-plesset-perturbation-theory, quantum-dynamics, snsf, solvated-electron
A deep learning dataset for metal multiaxial fatigue life prediction

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.

Tags: deep-learning, fatigue-life-prediction, metal-fatigue, multiaxial-fatigue
Bundesgesetz über die Steuerreform und die AHV-Finanzierung (STAF)

Schlussresultate der eidgenössischen Abstimmung "Bundesgesetz über die Steuerreform und die AHV-Finanzierung (STAF)", 19. Mai 2019, Graubünden - Grischun - Grigioni, aufgeschlüsselt nach Gemeinden.

Tags: abstimmung, federal, national, nazionale, plaun-naziunal, votation, votazione, votaziun
Adatom-Induced Local Melting

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

Tags: aluminum, defects, first-principles-molecular-dynamics, marvel, surface-melting
Evaluating charge equilibration methods to generate electrostatic fields in nanoporous materials

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

Tags: charge-equilibration-methods, marvel, partial-charges, qeq
Capturing chemical intuition in synthesis of metal-organic frameworks

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.

Tags: genetic-algorithms, machine-learning, marvel, metal-organic-frameworks, optimisation, robotic-synthesi, synthesis
Toward GW Calculations on Thousands of Atoms

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.

Tags: abinitio, cp2k, dft, graphene, gw, high-performance-computing, marvel, nanoribbon, scaling
Nationalratswahlen 2019

Schlussresultate der eidgenössischen Wahl "Nationalratswahlen 2019", 20. Oktober 2019, Graubünden - Grischun - Grigioni, aufgeschlüsselt nach Kandidaten und Gemeinden.

Tags: election, elecziun, elezione, federal, national, nazionale, plaun-naziunal, wahl
Ständeratswahlen 2019

Schlussresultate der kantonalen Wahl "Ständeratswahlen 2019", 20. Oktober 2019, Graubünden - Grischun - Grigioni, aufgeschlüsselt nach Kandidaten und Gemeinden.

Tags: cantonal, cantonale, election, elecziun, elezione, kantonal, plaun-chantunal, wahl
In Silico Design of Porous Polymer Networks: High Throughput Screening for Methane Storage Materials

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

Tags: 3d, database, dc, deliverable-capacities, gcmc, grand-canonical-monte-carlo, high-throughput, methane-storage, nanoporous, porous-polymer-networks, ppn, three-dimensional
Li₄₋ₓGe₁₋ₓPₓO₄, a potential solid-state electrolyte for all-oxide microbatteries

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.

Tags: big-map, first-principles-molecular-dynamics, ionic-transport, lgpo, li4xge1xpxo4, lisicon, marvelinc1, microbatteries, solid-state-electrolytes, thin-films
Picture of Wet Electron: A Localized Transient State in Liquid Water

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

Tags: first-principles, hydrated-electron, liquid-water, molecular-dynamics, wet-electron
Reducing the Number of Mean-Square Deviation Calculations with Floating Close Structure in Metadynamics

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.

Tags: collective-variable, mean-square-deviation, metadynamics
In Silico Design of 2D and 3D Covalent Organic Frameworks for Methane Storage Applications

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

Tags: 2d, 3d, cof, covalent-organic-frameworks, database, dc, deliverable-capacities, gcmc, grand-canonical-monte-carlo, high-throughput, layered, marvel, methane-storage, nanoporous, three-dimensional, two-dimensional
The complex non-collinear magnetic orderings in Ba2YOsO6: A new approach to tuning spin-lattice interactions and controlling magnetic orderings in frustrated complex oxides

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.

Tags: double-perovskite, first-principles-calculations, frustrated-complex-oxides, non-collinear-magnetic-ordering, spin-orbital-coupling
Synthesis of Metal-Organic Frameworks: capturing chemical intuition

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.

Tags: machine-learning, marvel, metal-organic-frameworks, optimisation, synthesis