AndreaBozzo/ceres-open-data-index huggingface dataset:
Engineering  frustrated lewis pair active sites in porous organic scaffolds for catalytic CO₂ hydrogenation

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

Tags: catalysis, epfl, frustrated-lewis-pairs, metal-organic-frameworks, molecular-catalysts, volcano-plot
Polygonal tessellations as predictive models of molecular monolayers

Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by covalent, hydrogen or van der Waals interactions - self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides an entirely new view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications.

Tags: 2d-monolayer, molecular-self-assembly, tesselation
A Standard Solid State Pseudopotentials (SSSP) library optimized for precision and efficiency

Despite the enormous success and popularity of density functional theory, systematic verification and validation studies are still very limited both in number and scope. Here, we propose a universal standard protocol to verify publicly available pseudopotential libraries, based on several independent criteria including verification against all-electron equations of state and plane-wave convergence tests for phonon frequencies, band structure, cohesive energy and pressure. Adopting these criteria we obtain two optimal pseudopotential sets, namely the Standard Solid State Pseudopotential (SSSP) efficiency and precision libraries, tailored for high-throughput materials screening and high-precision materials modelling. As of today, the SSSP precision library is the most accurate open-source pseudopotential library available. This archive entry contains the database of calculations (phonons, cohesive energy, equation of state, band structure, pressure, etc.) together with the provenance of all data and calculations as stored by AiiDA.

*** UPDATE April 2020 *** 
The zipped tarball archives and AiiDA export files had inconsistent internal formats and naming conventions, which made it difficult to work with them programmatically. In this update, the files are standardized according to the conventions that are detailed in the README.md file. Note that the actual content of pseudo potential files and the JSON metadata files has **not** changed, with the exception that the keys "cutoff" and "dual" in the JSON files have been replaced with "cutoff_wfc" and "cutoff_rho". Here the value of "cutoff_wfc" is equal to the old "cutoff", and "cutoff_rho" is equal to the product of the old "cutoff" and "dual". Besides that, the update concerns merely the renaming of certain files and the restructuring of archive formats.

Tags: calculations, chessboards, cohesive-energy, cscs, database, dft, efficiency, electronic-bands, electronic-properties, elemental-solids, high-throughput, library, marvel, max, phonons, prace, precision, pseudopotentials, sssp, standard, verification, vibrational-properties
Gaussian Approximation Potentials for iron from extended first-principles database (Data Download)

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.

Tags: -iron, artificial-neural-networks, gaussian-approximation-potentials, interatomic-potentials, machine-learning, marvel
Improving collective variables: The case of crystallization

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.

Tags: crystalization, enhanced-sampling, hlda, marvel, marveldd1, plumed
Energetics of the coupled electronic–structural transition in the rare-earth nickelates

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.

Tags: correlated-systems-structural-prediction, dynamical-mean-field-theory, electronic-properties-and-materials, marvel, quantum-materials
Dictionary of 140k GDB and ZINC derived AMONs

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.

Tags: building-blocks, erc, marvel, organic-chemical-space, quantum-machine-learning, snsf
Rare-earth magnetic nitride perovskites

We propose perovskite nitrides with magnetic rare-earth metals as novel materials with a range of technological applications. These materials appear to be thermodynamically stable and, in spite of possessing different crystal structures and different atomic environments, they retain the magnetic moment of the corresponding elemental rare-earth metal. We find both magnetic metals and semiconductors, with a wide range of magnetic moments and some systems posses record-high magnetic anisotropy energies. Further tuning of the electronic and magnetic properties can also be expected by doping with other rare-earths or by creating solid solutions. The synthesis of these exotic materials with unusual compositions would extend not only the accepted stability domain of perovskites but also open the way for a series of applications enabled by their rich physics.

Tags: magnetic-anisotropy-energy, marveldd1, nitride-perovskites, rare-earth-metals, snsf
In silico design of three-dimensional porous covalent organic frameworks via known synthesis routes and commercially available species

Covalent organic frameworks (COFs) are a class of advanced nanoporous polymeric materials which combine the crystallinity of metal–organic frameworks (MOFs) with the stability and potentially low-cost organic chemistry of porous polymer networks (PPNs). Like other advanced porous materials, COFs can potentially be designed to meet the needs of a variety of applications, from energy, to security, to human health. In this work, we construct in silico a database of hypothetical three-dimensional, crystalline COFs. In constructing this library we generate novel COFs using only established synthetic routes, previously utilized tetrahedral building units, and commercially available bridging "linker" molecules. This ensures that there are no known chemical barriers to synthesizing all materials in our database. We relaxed all materials in our database through semiempirical electronic structure calculations. In addition, for those structures that allow interpenetration, we designed interpenetrated versions of the basic structure. Then, we characterized the porosity of each of these structures. The final set of 4147 structures (based on 620 unique noninterpenetrated structures) and their computed properties are publicly available and can be screened to identify promising materials for a wide variety of applications. Here, we assess the suitability of our COFs for vehicular methane storage by performing molecular simulations to predict the equilibrium methane uptake.

Tags: 3d, cofs, covalent-organic-frameworks, database, dc, deliverable-capacities, gcmc, grand-canonical-monte-carlo, high-throughput, methane-storage, nanoporous, three-dimensional
Thusis

Schlussresultate der regionalen Wahl "Thusis", 14. Juni 2026, Graubünden - Grischun - Grigioni, aufgeschlüsselt nach Kandidaten und Gemeinden.

Tags: election, elecziun, elezione, plaun-regiunal-regionlabelgr, regional-regionlabelgr, regionale-regionlabelgr, wahl
Bundesbeschluss über die Beschaffung neuer Kampfflugzeuge

Schlussresultate der eidgenössischen Abstimmung "Bundesbeschluss über die Beschaffung neuer Kampfflugzeuge", 27. September 2020, Graubünden - Grischun - Grigioni, aufgeschlüsselt nach Gemeinden.

Tags: abstimmung, federal, national, nazionale, plaun-naziunal, votation, votazione, votaziun
Gaussian Approximation Potentials for iron from extended first-principles database (Data Download)

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.

Tags: artificial-neural-networks, gaussian-approximation-potentials, interatomic-potentials, iron, machine-learning, marvel
Improving the Mechanical Stability of Metal-Organic Frameworks Using Chemical Caryatids

Metal-organic frameworks (MOFs) have emerged as versatile materials for applications ranging from gas separation and storage, catalysis, and sensing. The attractive feature of MOFs is that by changing the ligand and/or metal, they can be chemically tuned to perform optimally for a given application. In most, if not all, of these applications one also needs a material that has a sufficient mechanical stability, but our understanding of how changes in the chemical structure influence mechanical stability is limited. In this work, we rationalize how the mechanical properties of MOFs are related to framework bonding topology and ligand structure. We illustrate that the functional groups on the organic ligands can either enhance the mechanical stability through formation of a secondary network of non-bonded interactions, or soften the material by destabilizing the bonded network of a MOF. In addition, we show that synergistic effect of the bonding network of the material and the secondary network is required to achieve optimal mechanical stability of a MOF. The developed molecular insights in this work can be used for systematic improvement of the mechanical stability of the materials by careful selection of the functional groups.

Tags: marvel, mechanical-properties, nanoporous-materials, stability
Making the best of a bad situation: a multiscale approach to free energy calculation

Many enhanced sampling techniques rely on the identification of a number of collective variables that describe all the slow modes of the system. By constructing a bias potential in this reduced space one is then able to sample efficiently and reconstruct the free energy landscape. In methods like metadynamics, the quality of these collective variables plays a key role in convergence efficiency. Unfortunately in many systems of interest it is not possible to identify an optimal collective variable, and one must deal with the non-ideal situation of a system in which some slow modes are not accelerated. 
We propose a two-step approach in which, by taking into account the residual multiscale nature of the problem, one is able to significantly speed up convergence. To do so, we combine an exploratory metadynamics run with an optimization of the free energy difference between metastable states, based on the recently proposed variationally enhanced sampling method. This new method is well parallelizable and is especially suited for complex systems, because of its simplicity and clear underlying physical picture.

Tags: enhanced-sampling, free-energy, metadynamics
Isobaric-Isothermal Monte Carlo Simulations of Bulk Liquid Water from MP2 and RPA Theory (MC Trajectories Data Download)

Methods based on the second order Møller–Plesset perturbation theory (MP2) and the Random Phase Approximation (RPA) have emerged as practicable and reliable approaches to improve the accuracy of density functional approximations for first principle atomistic simulations. Such approaches are in fact capable to account ab-initio for non-local dynamical electron correlation effects, which play a fundamental role, for example, in the description of non-bonded interactions. To assess the performance of MP2 and RPA for real applications, isobaric-isothermal Monte Carlo simulations have been performed to study the structural properties of bulk liquid water under ambient conditions. The choice of bulk liquid water as benchmark system is motivated by the complicated nature of the intermolecular interactions, where repulsion, polarization, hydrogen bonding and van der Waals forces play an important role and are particularly difficult to reproduce accurately in atomistic models. The results demonstrate the feasibility of such approaches which open the way for further applications.

Tags: bulk-liquid-water, isobaric-isothermal-npt, marvel, monte-carlo-simulations, mp2, rpa
Enhanced sampling of transition states

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.

Tags: critical-clusters, enhanced-sampling, molecular-modelling, nucleation, transition-states
A Standard Solid State Pseudopotentials (SSSP) library optimized for accuracy and efficiency (Version 1.0, data download)

Despite the enormous success and popularity of density functional theory, systematic verification and validation studies are still very limited both in number and scope. Here, we propose a universal standard protocol to verify publicly available pseudopotential libraries, based on several independent criteria including verification against all-electron equations of state and plane-wave convergence tests for phonon frequencies, band structure, cohesive energy and pressure. Adopting these criteria we obtain two optimal pseudopotential sets, namely the Standard Solid State Pseudopotential (SSSP) efficiency and accuracy libraries, tailored for high-throughput materials screening and high-precision materials modelling. As of today, the SSSP accuracy library is the most accurate open-source pseudopotential library available. This archive entry contains the database of calculations (phonons, cohesive energy, equation of state, band structure, pressure, etc.) together with the provenance of all data and calculations as stored by AiiDA.

Tags: accuracy, calculations, chessboards, cohesive-energy, database, dft, efficiency, electronic-bands, electronic-properties, elemental-solids, high-throughput, library, marvel, phonons, pseudopotentials, sssp, standard, verification, vibrational-properties