AndreaBozzo/ceres-open-data-index huggingface dataset:
Magnetic exchange interactions in monolayer CrI₃ from many-body wavefunction calculations

The marked interplay between the crystalline, electronic, and magnetic structure of atomically thin magnets has been regarded as the key feature for designing next-generation magneto-optoelectronic devices. In this respect, a detailed understanding of the microscopic interactions underlying the magnetic response of these crystals is of primary importance. Here, we combine model Hamiltonians with multireference configuration interaction wavefunctions to accurately determine the strength of the spin couplings in the prototypical single-layer magnet CrI₃. Our calculations identify the (ferromagnetic) Heisenberg exchange interaction J = −1.44 meV as the dominant term, being the inter-site magnetic anisotropies substantially weaker. We also find that single-layer CrI₃ features an out-of-plane easy axis ensuing from a single-ion anisotropy A = −0.10 meV, and predict g-tensor in-plane components gxx = gyy = 1.90 and out-of-plane component gzz  = 1.92. In addition, we assess the performance of a dozen widely used density functionals against our accurate correlated wavefunctions calculations and available experimental data, thereby establishing reference results for future first-principles investigations. Overall, our findings offer a firm theoretical ground to recent experimental observations.

Tags: 2d-materials, cri3, cscs, density-functional-calculations, marvel, quantum-chemistry-calculations, quantum-magnetism, snsf
Koopmans spectral functionals: an open-source periodic-boundary implementation

Koopmans' spectral  functionals aim to describe simultaneously ground state properties and charged excitations of atoms, molecules, nanostructures and periodic crystals. This is achieved augmenting standard density functionals with simple but physically motivated orbital-density-dependent corrections. These corrections act on a set of localized orbitals that, in periodic systems, resembles maximally localized Wannier function. At variance with a direct supercell implementation, we discuss here i) the complex but efficient formalism required for a periodic-boundary code using explicit Brillouin zone sampling, and ii) the calculation of the screened Koopmans' corrections with density-functional perturbation theory. In addition to delivering improved scaling with system size, the present development makes the calculation of band structures with Koopmans functionals straightforward. The implementation in the Quantum ESPRESSO distribution and the application to prototypical insulating and semiconducting systems are presented and discussed.

Tags: electronic-structure, koopmans-spectral-functionals, marvelosp, orbital-density-dependent-functionals
On‐surface synthesis of cumulene‐containing polymers via two‐step dehalogenative homocoupling of dibromomethylene-functionalized tribenzoazulene

The record contains data that support our recent findings in the fabrication of cumulene containing polymers. Cumulene compounds are notoriously difficult to prepare and study because their reactivity increases dramatically with the increasing number of consecutive double bonds. In this respect, the emerging field of on‐surface synthesis provides exceptional opportunities because it relies on reactions on clean metal substrates under well‐controlled ultrahigh‐vacuum conditions. In the work we report the on‐surface synthesis of a polymer linked by cumulene‐like bonds on a Au(111) surface via sequential thermally activated dehalogenative C−C coupling of a tribenzoazulene precursor equipped with two dibromomethylene groups. The structure and electronic properties of the resulting polymer with cumulene‐like pentagon–pentagon and heptagon–heptagon connections have been investigated by means of scanning probe microscopy and spectroscopy methods and X‐ray photoelectron spectroscopy, complemented by density functional theory calculations.

Tags: ab-initio, cscs, cumulene, dfg, erc, marveldd3, on-surface-synthesis, polymers, snsf
Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol-1 were achieved in predictions of the activation energy. This strategy opens the door for quickly and accurately predicting higher-selectivity catalysts for any reaction from available structural information.

Tags: catalysis, enantioselectivity, machine-learning, marvel, organocatalyst
Bayesian probabilistic assignment of chemical shifts in organic solids

A pre-requisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei.  Obtaining the assignment experimentally can be challenging and typically requires time-consuming multi-dimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge structural database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for over 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the 13C and 1H assignment of eleven molecular solids with experimental shifts, and benchmarked on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in over 80% of cases.

Tags: experimental, machine-learning, marveldd1, nmr, snsf
Double-hybrid DFT functionals for the condensed phase: Gaussian and plane waves implementation and evaluation

Intermolecular interactions play an important role for the understanding of catalysis, biochemistry and pharmacy. Double-hybrid density functionals (DHDFs) combine the proper treatment of short-range interactions of common density functionals with the correct description of long-range interactions of wave-function correlation methods. Up to now, there are only a few benchmark studies available examining the performance of DHDFs in condensed phase. We studied the performance of a small but diverse selection of DHDFs implemented within Gaussian and plane waves formalism on cohesive energies of four representative dispersion interaction dominated crystal structures. We found that the PWRB95 and ωB97X-2 functionals provide an excellent description of long-ranged interactions in solids. In addition, we identified numerical issues due to the extreme grid dependence of the underlying density functional for PWRB95. The basis set superposition error (BSSE) and convergence with respect to the super cell size are discussed for two different large basis sets.

This record contains raw data (input files, output files, restart files) and changes to the LibXC 4.3.4 source code.

Tags: benchmark, cscs, density-functional-theory, double-hybrid-density-functional-theory
Suppressed charge recombination in hematite photoanode via protonation and annealing

Hematite as promising photoanode for solar water splitting suffers from severe bulk and surface charge recombination. This work describes that a protonation−annealing treatment can effectively suppress both bulk and surface charge recombination in hematite. Protons/electrons are electrochemically incorporated into hematite under 0.2 VRHE followed by annealing at 120 °C. The photocurrent density increases from ∼0.9 to 1.8 mA cm−2 at 1.23 VRHE under 1 sun, and further to 2.7 mA cm−2 after loading cobalt phosphate, stabilizing at round 2.4 mA cm−2. A cathodic shift of the onset potential of photocurrent is also observed. H2O2 oxidation, impedance spectroscopy, and Mott−Schottky measurements show that the protonation suppresses bulk recombination and enhances donor density, but introducing more surface recombination. The annealing reduces surface recombination, while preserving relatively high bulk charge separation efficiency. Different from previous reports on the electrochemically reduced hematite, this work demonstrates that the performance improvement should be ascribed to the proton incorporation instead of the formation of Fe3O4 or metal Fe. This facile treatment by protonation and annealing could be applied in other semiconductors to promote the development of high performing photoelectrodes.

Tags: hematite-photoanode, marvel, photoelectrochemistry, solar-water-splitting
Band gap engineering in blended organic semiconductor films based on dielectric interactions

Blending organic molecules to tune their energy levels is currently investigated as an approach to engineer the bulk and interfacial optoelectronic properties of organic semiconductors. It has been proven that the ionization energy (IE) and electron affinity (EA) can be equally shifted in the same direction by electrostatic effects controlled by blending similar halogenated derivatives with different energetics. Here, we show that the energy gap of organic semiconductors can be tuned by blending as well. We use oligothiophenes with different numbers of thiophene rings as example and investigate their structure and electronic properties. Photoelectron spectroscopy and inverse photoelectron spectroscopy show tunability of the single-particle gap, with the optical gaps showing similar, but smaller effects. Theoretical analysis shows that this tuning is mainly caused by a change in the dielectric constant with blend ratio. Further studies will explore the practical impact of this energy-level engineering strategy in optoelectronic devices.

Tags: dielectric-interactions, energy-level-tuning, photoelectron-spectroscopy, theoretical-simulations
Ultrahigh drive current and large selectivity in GeS selector

Selector devices are indispensable components of large-scale nonvolatile memory and neuromorphic array systems. Besides the conventional silicon transistor, two-terminal ovonic threshold switching (OTS) devices with much higher scalability are currently the most industrially favored selector technology. Despite industrial confidence in OTS selectors, current devices rely heavily on intricate control of material stoichiometry and generally suffer from toxic and complex dopants to remedy the intrinsic demerits of the principal functioning materials. Here, we report on a selector with a large drive current density of 34 MA/cm2 and a ~106 high nonlinearity, realized in an environment-friendly and earth-abundant sulfide binary semiconductor, GeS. Both experiments and first-principles calculations have revealed Ge pyramid-dominated network and high density of near-valence band trap states in amorphous GeS. The high drive current capacity is associated with the strong Ge-S covalency and the high nonlinearity could arise from the synergy of the mid-gap traps assisted electronic transition and local Ge-Ge chain growth as well as locally enhanced bond alignment under high electric field. Besides the superior selector function, we have also demonstrated stochastic integrate-and-fire neuron behavior using GeS device, providing an intriguing opportunity for all-chalcogenide neuromorphic electronics.
The source data underlying Figs. 1c-e, Figs. 2a–e, and Figs. 3b, d, f are provided as a Source Data file.

Tags: ges, ots, phase-change-memory, selector
Heat and charge transport in H2O at ice-giant conditions from ab initio molecular dynamics simulations

The impact of the inner structure and thermal history of planets on their observable features, such as luminosity or magnetic field, crucially depends on the poorly known heat and charge transport properties of their internal layers. The thermal and electric conductivities of different phases of water (liquid, solid, and super-ionic) occurring in the interior of ice giant planets, such as Uranus or Neptune, are evaluated from equilibrium ab initio molecular dynamics, leveraging recent progresses in the theory and data analysis of transport in extended systems. In this record we collect the ab-initio time series of the energy flux and of the electronic and ionic charge fluxes for H2O at different planetary conditions, from which the related transport coefficients are extracted according to the Green-Kubo theory of linear response.

Tags: max, molecular-dynamics, planetary-evolution-models, transport-properties, water-at-extreme-conditions
Koopmans spectral functionals: an open-source periodic-boundary implementation

Koopmans' spectral  functionals aim to describe simultaneously ground state properties and charged excitations of atoms, molecules, nanostructures and periodic crystals. This is achieved augmenting standard density functionals with simple but physically motivated orbital-density-dependent corrections. These corrections act on a set of localized orbitals that, in periodic systems, resembles maximally localized Wannier function. At variance with a direct supercell implementation, we discuss here i) the complex but efficient formalism required for a periodic-boundary code using explicit Brillouin zone sampling, and ii) the calculation of the screened Koopmans' corrections with density-functional perturbation theory. The implementation in the Quantum ESPRESSO distribution and the application to prototypical insulating and semiconducting systems are presented and discussed.

Tags: electronic-structure, koopmans-spectral-functionals, marvelosp, orbital-density-dependent-functionals
How to extract adsorption energies, adsorbate-adsorbate interaction parameters, and saturation coverages from temperature programmed desorption experiments

We present a scheme to extract the adsorption energy, adsorbate interaction parameter and the saturation coverage from temperature programmed desorption (TPD) experiments. We propose that the coverage dependent adsorption energy can be fit using a functional form including the configurational entropy and linear adsorbate-adsorbate interaction terms. As one example of this scheme, we analyze TPD of CO desorption on Au(211) and Au(310) surfaces. We determine that under atmospheric CO pressure, the steps of both facets adsorb between 0.4-0.9 ML coverage of CO*. We compare this result against energies obtained from five density functionals, RPBE, PBE, PBE-D3, RPBE-D3 and BEEF-vdW. We find that the energies and equilibrium coverages from RPBE-D3 and PBE are closest to the values determined from the TPD. This dataset contains all the DFT calculations run using AiiDA.

Tags: benchmark, catalysis, h2020
Massive Dirac fermion behavior in a low bandgap graphene nanoribbon near a topological phase boundary

Graphene nanoribbons (GNRs) have attracted much interest due to their largely modifiable electronic properties. Manifestation of these properties requires atomically precise GNRs which can be achieved through a bottom–up synthesis approach. This has recently been applied to the synthesis of width‐modulated GNRs hosting topological electronic quantum phases, with valence electronic properties that are well captured by the Su–Schrieffer–Heeger (SSH) model describing a 1D chain of interacting dimers. In this record we provide data to support our recent publication where we demonstrate that ultralow bandgap GNRs with charge carriers behaving as massive Dirac fermions can be realized when their valence electrons represent an SSH chain close to the topological phase boundary, i.e., when the intra‐ and interdimer coupling become approximately equal. Such a system has been achieved via on‐surface synthesis based on readily available pyrene‐based precursors and the resulting GNRs are characterized by scanning probe methods. The pyrene‐based GNRs (pGNRs) can be processed under ambient conditions and incorporated as the active material in a field effect transistor. A quasi‐metallic transport behavior is observed at room temperature, whereas at low temperature, the pGNRs behave as quantum dots showing single‐electron tunneling and Coulomb blockade. This study may enable the realization of devices based on carbon nanomaterials with exotic quantum properties.

Tags: ab-initio, dirac-fermions, graphene-nanoribbons, marveldd3, snsf
Emergence of nontrivial low-energy Dirac fermions in antiferromagnetic EuCd2As2

When magnetism meets topology, colorful novel states can be created in materials. The realization of magnetic topological Dirac materials remains a major issue in topological physics studies. In this work, it is ascertained that the topologically nontrivial ground state of EuCd2As2 is a good candidate for different types of magnetic topological state: magnetic topological Dirac semimetal, axion insulator, antiferromagnetic TCI, and higher order topological insulator. This documents include all the raw data in the reference Advanced Materials 32, 1907565 (2020). This files contains all the raw data that published in the related paper. Fig.1.zip includes dat format transport measurement results which can be open by Origin. Fig.2.zip contains ibw format ARPES spectra which can be opened by IGOR. Fig.3.zip contains figures of the calculated FS, band structure. The dat format files can be open by IGOR. Fig.4.zip contains both ARPES spectra and STS data. sxm format file can be opened by SXM software which is commonly used for STM/STS data. The h5 format file can be opened by IGOR. H5 format is also commonly used for high dimensional data in ARPES experiments. Fig.5 is mainly the calculated band structure of the related fig.5. The itx format can be opened by IGOR. All the ibw format files are duplicated in the txt file with same name. The SXM file is also duplicated in txt file with same name.

Tags: axion-insulator, high-order-topological-insulator, magnetic-topological-dirac-semimetal, magnetic-topological-insulator, marvel, snsf
Machine learning for metallurgy: neural network potentials for Al-Cu-Mg and Al-Cu-Mg-Zn

Most metallurgical properties, e.g., dislocation propagation, precipitate formation, can only be fully understood atomistically but most phenomena and quantities of interest cannot be measured experimentally.  Accurate simulation methods are essential but first-principles density functional theory (DFT) is prohibitively expensive while empirical interatomic potentials are rarely sufficiently accurate for alloys.  Machine learning (ML) is emerging as an approach to create computationally-efficient atomistic potentials achieving near-DFT accuracy.  Building on recent work on binary Al-Cu and ternary Al-Mg-Si, here a family of neural network potentials (NNPs) for Al alloys of Al-Cu-Mg and Al-Cu-Mg-Zn is developed and assessed using the Behler-Parinello formulation.  Training of the potentials uses a robust set of metallurgically-relevant structures including intermetallic phases, stacking faults, solute/solute and solute/stacking fault interactions, solute clusters, and matrix/precipitate interfaces.  The accuracy of these NNPs is then demonstrated across a comprehensive set of properties derived from the training set structures and, moreover, many important structures not represented in the training set such as the generalized stacking fault energy (GSFE) surface of the critical S-phase precipitate in Al-Mg-Cu, and antisite and vacancy formation energies for Al-Cu-Mg intermetallics.  The broader Al-Cu-Mg-Zn NNPs also have high accuracy for subtle properties such as Cu substitutional energies in the $\eta'$ and T phases and formation energies of small Al-Zn-Mg clusters.  Together with earlier results, this work shows how increasingly complex multicomponent alloy potentials can be systematically developed by expanding a training database, leading to a comprehensive set of potentials for a broad alloy family, demonstrated here for the technological Al-2xxx, Al-5xxx, and Al-7xxx Al alloys.

Tags: aluminum, machine-learning, marveldd2, metallurgy
Machine learning for metallurgy: a neural network potential for Al-Cu-Mg

High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic-scale phenomena, particularly during early-stage nucleation and growth. Atomistic modeling using interatomic potentials is a desirable tool for understanding the detailed phenomena involved in precipitation and strengthening, which requires length and time scales far larger than those accessible by first-principles methods. Current interatomic potentials for alloys are not, however, sufficiently accurate for such studies. Here, a family of neural-network potentials (NNPs) for the Al-Cu-Mg system is presented as the first example of a machine-learning potential that can achieve near-first-principles accuracy for many different metallurgically-important aspects of this alloy. High fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, generalized stacking fault energies, and surfaces for slip in matrix and precipitates, antisite defect energies, and other quantities, are shown.  The NNP shows some significant transferability to defects and properties outside the structures used to develop the NNP but also shows some errors, highlighting that the use of any interatomic potential requires careful validation in application to specific metallurgical problems of interest.

Tags: aluminum, machine-learning, marveldd2, metallurgy
Metal-organic frameworks as kinetic modulators for branched selectivity in hydroformylation

Finding heterogeneous catalysts that are superior to homogeneous ones for selective organic transformation is a major challenge in catalysis. Here we show how micropores in metal-organic frameworks (MOFs) push homogeneous catalytic reactions into kinetic regimes inaccessible under standard conditions. Such property allows branched selectivity up to 90% in the Co-catalysed hydroformylation of olefins without directing groups, not achievable with existing catalysts. This finding has a big potential in the production of aldehydes for the fine chemical industry. Monte Carlo and density functional theory simulations combined with kinetic models show that the micropores of MOFs with UMCM-1 and MOF-74 topologies increase the olefins density beyond neat conditions while partially preventing the adsorption of syngas leading to high branched selectivity. The easy experimental protocol and the chemical and structural flexibility of MOFs will attract the interest of the fine chemical industries towards the design of heterogeneous processes with exceptional selectivity.

Tags: catalysis, marvel, metal-organic-frameworks, mof