AndreaBozzo/ceres-open-data-index huggingface dataset:
Bright Electroluminescence from Single Graphene Nanoribbon Junctions

This record refers to a study on the electroluminescence of individual GNRs suspended between the tip of a scanning tunneling microscope (STM) and a Au(111) substrate. Emission spectra of such GNR junctions reveal a bright and narrow band emission of red light, whose energy can be tuned with the bias voltage applied to the junction, but always lying below the gap of infinite GNRs. Comparison with ab initio calculations indicates that the emission involves electronic states localized at the GNR termini. The results shed light on unpredicted optical transitions in GNRs.

The record includes the computational data that allows to reproduce the published results.

Tags: density-functional-theory, electroluminescence, graphene-nanoribbons, gw, induced-light-emission, max, prace, scanning-tunneling-microscopy, single-molecule-junction
Unified picture of lattice instabilities in metallic transition metal dichalcogenides

Transition metal dichalcogenides (TMDs) in the 1T polymorph are subject to a rich variety of periodic lattice distortions, often referred to as charge-density waves (CDWs) when not too strong. We study from first principles the fermiology and phonon dispersion of three representative single-layer transition metal disulfides with different occupation of the t2g subshell: TaS2 (t1 2g), WS2 (t2 2g), and ReS2 (t3 2g) across a broad range of doping levels. While strong electron-phonon interactions are at the heart of these instabilities, we argue that away from half-filling of the t2g subshell, the doping dependence of the calculated CDW wave vector can be explained from simple fermiology arguments, so that a weak-coupling nesting picture is a useful starting point for understanding. On the other hand, when the t2g subshell is closer to half-filling, we show that nesting is irrelevant, while a real-space strong-coupling picture of bonding Wannier functions is more appropriate and simple bond-counting arguments apply. Our study thus provides a unifying picture of lattice distortions in 1T TMDs that bridges the two regimes, while the crossover between these regimes can be attained by tuning the filling of the t2g orbitals.

Tags: charge-density-wave, chemical-bonding, epfl, fermi-surface-nesting, lattice-distortion, phonons, susceptibility, transition-metal-dichalcogenides, wannier-functions
A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning

Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective training set. In this work, we implement and train a MLP to obtain an accurate description of the potential energy surface and property predictions for organic compounds, as both single molecules and in the condensed phase. We devise a dual descriptor, based on the atomic cluster expansion (ACE), that couples an information-rich short-range description with a coarser long-range description that captures weak intermolecular interactions. We employ uncertainty-guided active learning for the training set generation, creating a dataset that is comparatively small for the breadth of application and consists of alcohols, alkanes, and an adipate. Utilizing that MLP, we calculate densities of those systems of varying chain lengths as a function of temperature, obtaining a discrepancy of less than 4% compared with experiment. Vibrational frequencies calculated with the MLP have a root mean square error of less than 1 THz compared to DFT. The heat capacities of condensed systems are within 11% of experimental findings, which is strong evidence that the dual descriptor provides an accurate framework for the prediction of both short-range intramolecular and long-range intermolecular interactions.

Tags: diffusion, heat-capacity, machine-learning, molecular-dynamics, radial-distribution-functions, training-data, vasp
Surses

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

Tags: election, elecziun, elezione, plaun-regiunal-regionlabelgr, regional-regionlabelgr, regionale-regionlabelgr, wahl
Non-Abelian reciprocal braiding of Weyl points and its manifestation in ZrTe

Weyl semimetals in three-dimensional crystals provide the paradigm example of topologically protected band nodes. It is usually taken for granted that a pair of colliding Weyl points annihilate whenever they carry opposite chiral charges. In stark contrast, here we report that Weyl points in systems symmetric under the composition of time-reversal with a π-rotation are characterized by a non-Abelian topological invariant. The topological charges of the Weyl points are transformed via braid phase factors which arise upon exchange inside symmetric planes of the reciprocal momentum space. We elucidate this process with an elementary two-dimensional tight-binding model implementable in cold-atoms setups and in photonic systems. In three dimensions, interplay of the non-Abelian topology with point-group symmetry is shown to enable topological phase transitions in which pairs of Weyl points may scatter or convert into nodal-line rings. By combining our theoretical arguments with first-principles calculations, we predict that Weyl points occurring near the Fermi level of zirconium telluride (ZrTe) carry non-trivial values of the non-Abelian charge, and that uniaxial compression strain drives a non-trivial conversion of the Weyl points into nodal lines.

Tags: epfl, marveldd6, non-abelian-braiding, snsf, taas, weyl-semimetal, zrte
Hund excitations and the efficiency of Mott solar cells

We study the dynamics of photoinduced charge carriers in semirealistic models of LaVO3 and YTiO3 polar heterostructures. It is shown that two types of impact ionization processes contribute to the carrier multiplication in these strongly correlated multiorbital systems: The first mechanism involves local spin state transitions, while the second mechanism involves the scattering of high-kinetic-energy carriers. Both processes act on the 10-fs timescale and play an important role in the harvesting of high-energy photons in solar cell applications. As a consequence, the optimal gap size for Mott solar cells is substantially smaller than for semiconductor devices.

Tags: dft, erc, marveldd5, out-of-equilibrium-physics, solar-cells
Property map collective variable as a useful tool for force field correction

Molecular mechanics potentials for small molecules suffer inaccuracies. To apply corrections we used a concept called property map to calculate corrections. It was calculated as a sum of [correction_i exp(-lambda D(x, x_i))] divided by the sum of [exp(-lambda D(x, x_i))], where correction_i is the difference between the accurate and inaccurate potential for i-th landmark structure x_i, lambda is a chosen prefactor, D is a distance (e.g. RMSD or MSD) and x are atomic coordinates. The concept was tested on alanine dipeptide (all combinations of 7 force fields, one used as a model of accurate and one as inaccurate). Next it was applied on an anticancer drug Imatinib (General AMBER Force Field corrected to DFT).
Simulations were carried out in Gromacs 2016.4. Correction was Implemented using Plumed 2.4. DFT energies were calculated by ORCA 4.0 at the BP86/def2-TZVP level of theory.

Tags: dft, free-energy, molecular-dynamics-simulation, molecular-mechanics
Applicability of tail-corrections in the molecular simulations of porous materials

Molecular simulations with periodic boundary conditions require to define a certain cutoff distance   beyond which pairwise dispersion interactions are neglected. For the simulation of homogeneous phases it is well-established to use tail-corrections, that can remedy this truncation of the potential. These corrections are built under the assumption that beyond  the cutoff the radial distribution function is equal to one. In this work we shed some light on the discussion whether or not tail corrections should be used in the  modelling of heterogeneous systems. We show that for the adsorption of gasses in a diverse set  nanoporous crystalline materials (zeolites, Covalent Organic Frameworks (COFs), and Metal Organic Frameworks (MOFs)), tail-corrections are an appropriate choice with which the results are much less sensitive to the details of the truncation.

Tags: covalent-organic-frameworks, gcmc, marvel, metal-organic-frameworks, tail-corrections, zeolites
All-polymer organic solar cells with nano-to-micron hierarchical morphology and large light receiving angle

Distributed photovoltaics in living environment harvest the sunlight in different incident angles throughout the day. The development of planer solar cells with large light-receiving angle can reduce the requirements in installation form factor and is therefore urgently required. Here, thin film organic photovoltaics with nano-sized phase separation integrated in micro-sized surface topology is demonstrated as an ideal solution to proposed applications. All-polymer solar cells, by means of a newly developed sequential processing, show large magnitude hierarchical morphology with facilitated exciton-to-carrier conversion. The nano fibrilar donor-acceptor network and micron-scale optical field trapping structure in combination contributes to an efficiency of 19.06% (certified 18.59%), which is the highest value to date for all-polymer solar cells. Furthermore, the micron-sized surface topology also contributes to a large light-receiving angle. A 30% improvement of power gain is achieved for the hierarchical morphology comparing to the flat-morphology devices. These inspiring results show that all-polymer solar cell with hierarchical features are particularly suitable for the commercial applications of distributed photovoltaics due to its low installation requirement.

Tags: all-polymer-solar-cell, organic-solar-cells, surface-topology
Extrinsic Defects in Amorphous Oxides: Hydrogen, Carbon, and Nitrogen Impurities in Alumina

This entry provides the most stable defect configurations of hydrogen, carbon, and nitrogen impurities in alumina, which are identified through ab initio molecular dynamics in various charge states and structural relaxations with the PBE functional. The structural configurations related to carbon and nitrogen impurities are found to depend on the total charge set in the simulation cell.

Tags: alumina, carbon, extrinsic-defects, hydrogen, nitrogen, total-charge
On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events

Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

Tags: bayesian-inference, machine-learning, molecular-dynamics
AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance

The ever-growing availability of computing power and sustained development of advanced computational methods have contributed much to recent scientific progress.
These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (http://www.aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA's workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with any simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible.

This archive record contains the data to reproduce the figures on engine performance in the section "Event versus polling-based engine" of the paper entitled "AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance". It also includes instructions to reproduce the actual data from scratch using AiiDA v1.1.1 and AiiDA v0.12.5.

Tags: automation, emmc, erc, high-throughput, intersect, marketplace, marvel, max, nffa, pasc, prace, reproducibility, snsf, swissuniversities, workflows
Oxygen defects in amorphous Al2O3

The electronic properties of the oxygen vacancy and interstitial in amorphous Al2O3 are studied via ab initio molecular dynamics simulations and hybrid functional calculations. Our results indicate that these defects do not occur in amorphous Al2O3, due to structural rearrangements which assimilate the defect structure and cause a delocalization of the associated defect levels. The imbalance of oxygen leads to a nonstoichiometric compound in which the oxygen occurs in the form of O2– ions. Intrinsic oxygen defects are found to be unable to trap excess electrons. For low Fermi energies, the formation of peroxy linkages is found to be favored leading to the capture of holes. The relative +2/0 defect levels occur at 2.5 eV from the valence band.

Tags: amorphous-al2o3, holes, oxygen-interstitial, oxygen-vacancy, peroxy-linkage
Moiré Flat Bands in Twisted Double Bilayer Graphene

We investigate twisted double bilayer graphene (TDBG), a four-layer system composed of two AB-stacked graphene bilayers rotated with respect to each other by a small angle. Our ab-initio band structure calculations reveal a considerable energy gap at the charge point neutrality that we assign to the intrinsic symmetric polarization (ISP). We then introduce the ISP effect into the tight-binding parameterization and perform calculations on TDBG models that include lattice relaxation effects down to very small twist angles. We identify a narrow region around the magic angle θ*= 1.3° characterized by a manifold of remarkably flat bands gapped out from other states even without external electric fields. To understand the fundamental origin of the magic angle in TDBG, we construct a continuum model that points to a hidden mathematical link to the twisted bilayer graphene (TBG) model, thus indicating that the band flattening is a fundamental feature of TDBG, and is not a result of external fields.

Tags: continuum-model, epfl, flat-bands, magic-angle, marveldd6, moire-superlattice, snsf, tight-binding-model, twisted-double-bilayer-graphene, wanniertools
Ranking the synthesizability of hypothetical zeolites with the sorting hat

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme—the "zeolite sorting hat"—that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the zeolite sorting hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the zeolite sorting hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the zeolite sorting hat. Finally, we analyze the behavior of the zeolite sorting hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si-O distances around 3.2–3.4 Å as the key discriminatory factor.

Tags: epfl, erc, h2020, machine-learning, marvel, zeolites
Few layer 2D pnictogens catalyze the alkylation of soft nucleophiles with esters.

Group 15 elements in zero oxidation state (P, As, Sb and Bi), also called pnictogens, are rarely used in catalysis due to the difficulties associated in preparing well–structured and stable materials. Here, we report on the synthesis of highly exfoliated, few layer 2D phosphorene and antimonene in zero oxidation state, suspended in an ionic liquid, with the native atoms ready to interact with external reagents while avoiding aerobic or aqueous decomposition pathways, and on their use as efficient catalysts for the alkylation of nucleophiles with esters. The few layer pnictogen material circumvents the extremely harsh reaction conditions associated to previous superacid–catalyzed alkylations, by enabling an alternative mechanism on surface, protected from the water and air by the ionic liquid. These 2D catalysts allow the alkylation of a variety of acid–sensitive organic molecules and giving synthetic relevancy to the use of simple esters as alkylating agents.

Tags: alkylation, antimonene, black-phosphorous, esters, ionic-liquids
Volksinitiative «Für eine massvolle Zuwanderung (Begrenzungsinitiative)»

Schlussresultate der eidgenössischen Abstimmung "Volksinitiative «Für eine massvolle Zuwanderung (Begrenzungsinitiative)»", 27. September 2020, Graubünden - Grischun - Grigioni, aufgeschlüsselt nach Gemeinden.

Tags: abstimmung, federal, national, nazionale, plaun-naziunal, votation, votazione, votaziun
Learning the exciton properties of azo-dyes

The ab initio determination of the character and properties of electronic excited states (ES) is the cornerstone of modern theoretical photochemistry. Yet, traditional ES methods become readily impractical when applied to fairly large molecules, or when used on thousands of different systems. In contrast, Machine Learning (ML) techniques have demonstrated their accuracy at retrieving ES properties of large molecular databases at a reduced computational cost. Especially for excited states applications, non-linear algorithms tend to be specialized and to target only individual properties. Learning fundamental quantum chemical objects potentially represents a more efficient, yet complex, alternative as a large number of molecular properties could be then extracted through post-processing. Herein, we report the general framework able to learn three fundamental objects of an ES: the hole and particle densities, as well as the transition density. We demonstrate the advantages of targeting those outputs of an ES computation, and address the additional complexity of learning 3-dimensional scalar fields. We apply our predictions to obtain a list of derived properties, including the state character and the topological descriptors of the exciton, for the two bands (nπ* and ππ*) of a collection of 3432 azoheteroarene photoswitches. In doing so, we prove that the proposed approach offers a way to access ES information of different kinds, bypassing the need of traditional electronic structure computations, while retaining ab initio accuracy.

Tags: azo-dyes, epfl, erc, hole-and-particle-densities, machine-learning, marveldd1, photoswitches, snsf
Chemical Shifts in Molecular Solids by Machine Learning Datasets

We present a database of energy and NMR chemical shifts DFT calculations of 2500 crystal organic solids. The structures contain only H/C/N/O atoms and were subject to all-atoms geometry optimisation. Calculations were carried out using Quantum Espresso and GIPAW.

Tags: chemical-shifts, energy-calculations, gipaw, marvel, nmr, organic-solids, quantum-espresso
MD trajectories of semiconductor-water interfaces and relaxed atomic structures of semiconductor surfaces

This entry includes the MD trajectories of several semiconductor-water interfaces generated with ab initio molecular dynamics using the rVV10 density functional at the temperature of 350 K. Eight semiconductor surfaces are considered, namely GaAs(110), GaP(110), GaN(10-10), CdS(10-10), ZnO(10-10), SnO2(110), rutile TiO2(110) and anatase TiO2(101). For GaAs, GaP and anatase TiO2, the trajectories for the interfaces with both the molecular and the dissociative adsorption mode of water are provided. In addition, the relaxed atomic structures of the semiconductor surfaces used to calculate the ionization potential (IP) reported in [Chem. Mater. 2018, 30, 94−111] are added.

Tags: md-trajectory, rvv10-functional, semiconductor-water-interface
Large magnetic exchange coupling in rhombus-shaped nanographenes with zigzag periphery

In this record we provide data to support our recent findings for the magnetic properties of rhombus-shaped nanographenes. Nanographenes with zigzag edges are predicted to manifest non-trivial π-magnetism resulting from the interplay of concurrent electronic effects, such as hybridization of localized frontier states and Coulomb repulsion between valence electrons. This provides a chemically tunable platform to explore quantum magnetism at the nanoscale and opens avenues towards organic spintronics. The magnetic stability in nanographenes is thus far greatly limited by the weak magnetic exchange coupling, which remains below the room-temperature thermal energy. In our work, we report the synthesis of large rhombus-shaped nanographenes with zigzag peripheries on gold and copper surfaces. Single-molecule scanning probe measurements show an emergent magnetic spin singlet ground state with increasing nanographene size. The magnetic exchange coupling in the largest nanographene (C70H22, containing five benzenoid rings along each edge), determined by inelastic electron tunnelling spectroscopy, exceeds 100 meV or 1,160 K, which outclasses most inorganic nanomaterials and survives on a metal electrode.

Tags: ab-initio, cscs, graphene-flagship, horizon-2020, magnetic-carbon, marveldd3, nanographenes, snsf