AndreaBozzo/ceres-open-data-index huggingface dataset:
Generalized stacking fault energy surfaces for eight face-centered cubic transition metals

Density functional theory calculations were used to obtain the generalized stacking fault energy surfaces in eight FCC metals: Ag, Au, Cu, Ir, Ni, Pd, Pt, and Rh. Among them, ferromagnetism was considered only in Ni. Results based on non-magnetic Ni are denoted as Ni-NM.

Tags: density-functional-theory, face-centered-cubic-transition-metals, generalized-stacking-fault-energies
Dynamics of the Bulk Hydrated Electron from Many‐Body Wave‐Function Theory

Trajectories and spin densities for the bulk hydrated electron at the MP2 level of theory.  The data represent the first ab initio molecular dynamics study of the hydrated electron in the bulk using many-body wave function theory.

Tags: electronic-structure, hydrated-electron, many-body-methods, marvel, molecular-dynamics, solvated-electron, spin-density
Änderung des Bundesgesetzes über die direkte Bundessteuer

Schlussresultate der eidgenössischen Abstimmung "Änderung des Bundesgesetzes über die direkte Bundessteuer", 27. September 2020, Graubünden - Grischun - Grigioni, aufgeschlüsselt nach Gemeinden.

Tags: abstimmung, federal, national, nazionale, plaun-naziunal, votation, votazione, votaziun
Evaluation of photocatalysts for water splitting through combined analysis of surface coverage and energy-level alignment

To examine whether suitable conditions occur for the water splitting reaction at their interfaces with liquid water, we determine the pH-dependent surface coverage for a series of semiconductors, including GaAs, GaP, GaN, CdS, ZnO, SnO2, rutile and anatase TiO2. For this we calculate acidity constants at surface sites through ab initio molecular dynamics simulations and a grand-canonical formulation of adsorbates. The resulting pH values at the point of zero charge show excellent agreement with experiment and thereby support the validity of our approach. By combining information concerning the surface coverage with the alignment of the band edges with respect to the relevant redox levels, we scrutinize the potential of the considered semiconductors as photocatalysts and identify the corresponding optimal pH ranges for hydrogen and oxygen evolution. More specifically, our results indicate that GaN stands out among these semiconductors as the most promising candidate for the overall water splitting, with the potential of further improvement through alloying. With the surface coverage, our computational analysis brings a new descriptor that is at present beyond experimental reach.

Tags: cscs, deprotonation-free-energy, epfl, marvel, md-trajectory, rvv10-functional, semiconductor-water-interface, thermodynamic-integration
Enhancing sub-bandgap external quantum efficiency by photomultiplication for narrowband organic near-infrared photodetectors

Detection of electromagnetic signals for applications such as health, product quality monitoring or astronomy requires highly responsive and wavelength selective devices. Photomultiplication-type organic detectors (PM-OPDs) have shown to achieve high quantum efficiencies mainly in the visible range. Much less research has been focused on realizing near-infrared narrowband PM-OPDs. Here, we demonstrate fully vacuum-processed narrow- and broadband PM‑OPDs. Our devices are based on enhanced hole injection leading to a maximum external quantum efficiency (EQE) of almost 2000% at -10 V for the broadband device. The photomultiplicative effect is also observed in the charge-transfer (CT) state absorption region. By making use of an optical cavity device architecture, we enhance CT absorption and demonstrate a wavelength tunable narrowband PM-OPD with EQEs superior to those of pin‑devices. The presented concept can further improve the performance of state-of-the-art OPDs based on the absorption of CT states, which were so far limited by the low EQE provided by these devices.

Tags: experimental, narrowband, organic-photodetectors, photomultiplication
Electronic structure of water from Koopmans-compliant functionals

Obtaining a precise theoretical description of the spectral properties of liquid water poses challenges for both molecular dynamics (MD) and electronic structure methods. The lower computational cost of the Koopmans-compliant functionals with respect to Green's function methods allows the simulations of many MD trajectories, with a description close to the state-of-art quasi-particle self-consistent GW plus vertex corrections method (QSGW + fxc). Thus, we explore water spectral properties when different MD approaches are used, ranging from classical MD to first-principles MD, and including nuclear quantum effects. We have observed that different MD approaches lead to up to 1 eV change in the average band gap; thus, we focused on the band gap dependence with the geometrical properties of a system to explain such spread. We have evaluated the changes in the band gap due to variations in the intramolecular O–H bond distance and HOH angle, as well as the intermolecular hydrogen bond O···O distance and the OHO angles. We have observed that the dominant contribution comes from the O–H bond length; the O···O distance plays a secondary role, and the other geometrical properties do not significantly influence the gap. Furthermore, we analyze the electronic density of states (DOS), where the KIPZ functional shows good agreement with the DOS obtained with state-of-art approaches employing quasi-particle self-consistent GW plus vertex corrections. The O–H bond length also significantly influences the DOS. When nuclear quantum effects are considered, broadening of the peaks driven by the broader distribution of the O–H bond lengths is observed, leading to a closer agreement with the experimental photoemission spectra.

Tags: band-gap, marvel, molecular-dynamics, nuclear-quantum-effects, water
Pushing the limits of the donor-acceptor copolymer strategy for intramolecular singlet fission

Donor–acceptor (D–A) copolymers have shown great potential for intramolecular singlet fission (iSF). Nonetheless, very few design principles exist for optimizing these systems for iSF, with very little knowledge about how to engineer them for this purpose. In recent work, a fundamental trade-off between the main electronic ingredients required for iSF capable D–A coplanar copolymers was revealed. Still, further investigations are needed to understand these limitations and learn how to bypass them. In this work, we propose to induce torsion as an effective way to circumvent the limits of the coplanar approach. We disclose the potential of noncoplanar copolymers with inherently low triplet energies that encompass all the characteristics required for iSF beyond the limiting values associated with fully coplanar systems. Our findings shed some light on the electronic structure aspects of D–A copolymers for iSF and offer a new avenue for the rational design of novel promising candidates.

Tags: donor-acceptor-copolymers, electronic-structure, epfl, h2020, marie-curie-fellowship, singlet-fission
Detecting electron-phonon coupling during photoinduced phase transition

This record contains the data supporting our recent findings on electron-phonon coupling during photoinduced phase transition.
We measure mode- and band-selective electron-phonon couplings during the photoinduced insulator-to-metal phase transition in Ta2NiSe5 (TNS) by frequency-domain angle-resolved photoemission spectroscopy (FDARPES).
FDARPES gives us rich information about which band more couples which phonon mode by seeing frequency components of time-resolved angle-resolved photoemission spectra. 
The experiments indicate 2 THz and 3 THz phonon modes associated with the metallic and semiconducting phases. 
To get a more atomistic picture of the oscillation, we perform phonon-mode calculations relying on the density-functional theory (DFT).

The computational scheme itself is very standard that density-functional-perturbation theory (DFPT) with semilocal or local exchange-correlation functionals. 
However, the required computational resources were rather huge, 25,000 core-hour, for a single DFPT with atomic position optimization having 0.1 eV/nm force accuracy for more satisfactory computational parameters. 
Therefore, the data must be worth as a benchmark within DFT-level calculation for TNS.

Tags: dfpt, dft, electron-phonon-coupling, experimental, fdarpes, ta2nise5
Linear and quadratic magnetoresistance in the semimetal SiP2

Multiple mechanisms for extremely large magnetoresistance (XMR) found in many topologically nontrivial/trivial semimetals have been theoretically proposed, but experimentally it is unclear which mechanism is responsible in a particular sample. In this paper, by the combination of band structure calculations, numerical simulations of magnetoresistance (MR), Hall resistivity, and de Haas-van Alphen (dHvA) oscillation measurements, we studied the MR anisotropy of SiP2 which is verified to be a topologically trivial, incomplete compensation semimetal. It was found that as magnetic field H is applied along the a-axis, the MR exhibits an unsaturated nearly linear H dependence, which was argued to arise from incomplete carriers compensation. For the H // [101] orientation, an unsaturated nearly quadratic H dependence of MR up to 5.88 × 10^4%(at 1.8 K, 31.2 T) and field-induced up-turn behavior in resistivity were observed, which was suggested due to the existence of hole open orbits extending along the kx direction. Good agreement of the experimental results with the simulations based on the calculated Fermi surface (FS) indicates that the topology of FS plays an important role in its MR.

Tags: cscs, epfl, magnetoresistance, marvel, sip2, wanniertools
Efficient Kr/Xe separation from triangular g-C3N4 nanopores: density-functional theory calculations benchmarked with random phase approximation

Poly(triazine imide) or PTI is a promising material for molecular sieving membranes, thanks to its atom-thick ordered lattice with an extremely high density (1.6 × 10^14 pores/cm2) of triangular-shaped nanopores of ~0.34 nm diameter. Here, we investigate the application of PTI nanopores in the purification of Kr from Xe to reduce the storage volume of the mixture of 85Kr/Xe. Using van-der-Waals density-functional theory (vdW-DFT) calculations, benchmarked against the random phase approximation (RPA), we calculate the potential energy profiles for Kr and Xe across the nanopores. For each gas, starting from the RPA potential-energy profile, the force-field parameters to be used in the classical molecular dynamics framework are trained to calculate the Helmholtz free energy barrier as a function of temperature, and therefore, the corresponding entropic loss. Overall, due to the much higher activation energy from the adsorbed state in Xe (17.61 and 42.10 kJ/mole for Kr and Xe, respectively), a large Kr/Xe separation selectivity is postulated from the PTI membrane. Furthermore, the combination of atom-thick PTI lattice and high pore density leads to extremely large yet selective permeances for Kr. For example, Kr permeance of 1000 gas permeation units (GPU) accompanying a large Kr/Xe selectivity (>10000) is calculated at 25 °C, significantly better than the state-of-the-art membranes for Kr/Xe separation, making PTI-based membranes a leading candidate to process the hazardous waste of 85Kr/Xe mixture.

Tags: carbon-nitrides, cscs, density-functional-theory, epfl, marvel, nanoscale-transport, random-phase-approximation, snsf, van-der-waals-approximations
Coupled spin states in armchair graphene nanoribbons with asymmetric zigzag edge extensions

In this record we provide data supporting our recent work on coupled spin states in armchair nanoribbons. Exact positioning of sublattice imbalanced nanostructures in graphene nanomaterials offers a route to control interactions between induced local magnetic moments and to obtain graphene nanomaterials with magnetically nontrivial ground states. Our results reveal that such sublattice imbalanced nanostructures can be incorporated along a large band gap armchair graphene nanoribbon on the basis of asymmetric zigzag edge extensions, achieved by incorporating specifically designed precursor monomers. Scanning tunneling spectroscopy of an isolated and electronically decoupled zigzag edge extension reveals Hubbard-split states in accordance with theoretical predictions. Mean-field Hubbard-based modeling of pairs of such zigzag edge extensions reveals ferromagnetic, antiferromagnetic, or quenching of the magnetic interactions depending on the relative alignment of the asymmetric edge extensions. Moreover, a ferromagnetic spin chain is demonstrated for a periodic pattern of zigzag edge extensions along the nanoribbon axis.

Tags: carbon-magnetism, cscs, graphene-nanoribbon, marveldd3, on-surface-synthesis, snsf
Geometric landscapes for material discovery within energy-structure-function maps

Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal-organic frameworks (MOFs). To aid discovery of porous molecular crystals for desired applications, energy-structure-function (ESF) maps were developed that combine a priori prediction of both the crystal structure and its functional properties. However, it is a challenge to represent the high-dimensional structural and functional landscapes of an ESF map and to identify energetically favourable and functionally interesting polymorphs among the 1,000s-10,000s of structures typically on a single ESF map. Here, we introduce geometric landscapes, a representation for ESF maps based on geometric similarity, quantified by persistent homology. We show that this representation allows the exploration of complex ESF maps, automatically pinpointing interesting crystalline phases available to the molecule. Furthermore, we show that geometric landscapes can serve as an accountable descriptor for porous materials to predict their performance for gas adsorption applications. A machine learning model trained using this geometric similarity could reach a remarkable accuracy in predicting the materials' performance for methane storage applications.

Tags: crystal-structure-prediction, machine-learning, persistent-homology, porous-molecular-crystals
Using collective knowledge to assign oxidation states

Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed theories to predict the oxidation state based on counting rules, which can fail to describe the oxidation states of systems such as metal-organic frameworks.
Here we present a data-driven approach to automatically assign oxidation states, using a machine learning model trained on assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database.
Our approach only considers the immediate local environment around a metal centre, and is robust to experimental uncertainties (like incorrect protonation, unbound solvents, or changes in bondlength).
We find such excellent accuracy in our predictions that we can use our method to detect incorrect assignments. 
This work nicely illustrates how powerful the collective knowledge of chemists is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.

Tags: epfl, erc, lsmo, ml, mof, oxidation-state
A unified approach to enhanced sampling

The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested towards its solution. These methods are often grouped into two broad families. On the one hand methods such as umbrella sampling and metadynamics that build a bias potential based on few order parameters or collective variables. On the other hand tempering methods such as replica exchange that combine different thermodynamic ensembles in one single expanded ensemble. We adopt instead a unifying perspective, focusing on the target probability distribution sampled by the different methods. This allows us to introduce a new method that can sample any of the ensembles normally sampled via replica exchange, but does so in a collective-variables-based scheme. This method is an extension of the recently developed on-the-fly probability enhanced sampling method [Invernizzi and Parrinello, J. Phys. Chem. Lett. 11.7 (2020)] that has been previously used for metadynamics-like sampling. The method is thus very general and can be used to achieve different types of enhanced sampling. It is also reliable and simple to use, since it presents only few and robust external parameters and has a straightforward reweighting scheme. Furthermore, it can be used with any number of parallel replicas. We show the versatility of our approach with applications to multicanonical and multithermal-multibaric simulations, thermodynamic integration, umbrella sampling, and combinations thereof.

Tags: erc, expanded-ensembles, free-energy, importance-sampling, marveldd1, metadynamics, parallel-tempering, replica-exchange, snsf, thermodynamic-integration, umbrella-sampling
Learning the energy curvature versus particle number in approximate density functionals

The average energy curvature as a function of the particle number is a molecule-specific quantity, which measures the deviation of a given functional from the exact conditions of density functional theory (DFT). Related to the lack of derivative discontinuity in approximate exchange-correlation potentials, the information about the curvature has been successfully used to restore the physical meaning of Kohn-Sham orbital eigenvalues and to develop non-empirical tuning and correction schemes for density functional approximations. In this work, we propose the construction of a machine-learning framework targeting the average energy curvature between the neutral and the radical cation state of thousands of small organic molecules (QM7 database). The applicability of the model is demonstrated in the context of system-specific gamma-tuning of the LC-ωPBE functional and validated against the molecular first ionization potentials at equation-of-motion (EOM) coupled-cluster references. In addition, we propose a local version of the non-linear regression model and demonstrate its transferability and predictive power by determining the optimal range-separation parameter for two large molecules relevant to the field of hole-transporting materials. Finally, we explore the underlying structure of the QM7 database with the t-SNE dimensionality-reduction algorithm and identify structural and compositional patterns that promote the deviation from the piecewise linearity condition.

Tags: density-functional-theory, energy-curvature, epfl, machine-learning, marveldd1
Using collective knowledge to assign oxidation states

Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (>98%) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.

Tags: epfl, erc, lsmo, ml, mof, oxidation-state
Relative abundance of Z2 topological order in exfoliable two-dimensional insulators

Quantum spin Hall insulators (QSHIs) make up a class of two-dimensional materials with a finite electronic band gap in the bulk and gapless helical edge states. Some of the phenomena that can be hosted in these materials, from one-dimensional low-dissipation electronic transport to spin filtering, could be promising for many technological applications in the fields of electronics, spintronics, and topological quantum computing. Nevertheless, the rarity of two-dimensional materials that can exhibit nontrivial topological order at room temperature hinders development. In the publication, we report on our screening of a comprehensive database we recently identified of 1825 monolayers that can be exfoliated from experimentally known compounds to search for novel quantum spin Hall insulators. In this entry we provide the AiiDA database with the calculations of the DFT band structures (both with and without spin-orbit coupling) and the DFPT phonon dispersions for the QSHI candidates that we identify. All the data are also displayed in the dedicated DISCOVER section "2D topological insulators" https://www.materialscloud.org/discover/2dtopo/dashboard/ptable .

Tags: 2d-materials, dfpt, dft, first-principles, marveldd3, prace, quantum-spin-hall, snsf, spin-orbit, topological-insulator
Local electronic descriptors for solute-defect interactions in bcc refractory metals

The interactions between solute atoms and crystalline defects such as vacancies, dislocations, and grain boundaries are essential in determining alloy properties. Here we present a general linear correlation between two descriptors of local electronic structures and the solute-defect interaction energies in binary alloys of body-centered-cubic (bcc) refractory metals (such as W and Ta) with transition-metal substitutional solutes. One electronic descriptor is the bimodality of the d-orbital local density of states for a matrix atom at the substitutional site, and the other is related to the hybridization strength between the valance sp- and d-bands for the same matrix atom. For a particular pair of solute-matrix elements, this linear correlation is valid independent of types of defects and the locations of substitutional sites. These results provide the possibility to apply local electronic descriptors for quantitative and efficient predictions on the solute-defect interactions and defect properties in alloys.

Tags: bcc-refractory-alloys, electronic-structures, first-principles-calculations, solute-defect-interactions
Divalent Path to Enhance p-Type Conductivity in a SnO Transparent Semiconductor

The role of the divalent nature of tin is explored in tin monoxide, revealing a novel path for enhancing p-type conductivity. The consequences of oxygen off-stoichiometry indicate that a defect complex formed by a tin vacancy (VSn) and an impurity interstitial (Di) leads to an increased number of free carriers as well as improved acceptor state stability when compared with the isolated VSn. In this study, we identify several elements that are able to stabilize such a defect complex configuration. The enhanced ionization of the resulting complex arises from the divalent nature of Sn, which allows Sn(II) and Sn(IV) oxidation states to form. Such a novel doping mechanism not only offers a path for creating a high-performance p-type transparent SnO, but reveals an as-of-yet unexplored route to improve conductivity in other compounds formed by multivalent elements, for example, Sn(II)-based thermoelectrics.

Tags: binding-energy, group-17-compounds, impurities-and-defects, marveldd1, oxygen-defects, snsf