AndreaBozzo/ceres-open-data-index huggingface dataset:
Dataset of 80,000 solvated joint-DFT free energies for ORR on spinel-oxide (100) and (111) facets

Earth-abundant spinel oxides are promising alkaline oxygen-reduction catalysts, yet mechanistic models still invoke a vacuum-DFT associative *OOH/*OO route. Here we combine >80,000 fully solvated joint-DFT calculations to map oxygen-reduction energetics across 442 Al-, Co-, Cr-, Fe-, Ga-, Mn-, Ni- and Zn-containing spinels on the (100) and (111) facets. Associative intermediates are >0.5 eV less stable than *OH/H states, revealing a four-step dissociative cycle in which surface-hydrogen passivation shuttles protons. These results establish solvated high-throughput DFT as a predictive lens on the kinetic oxygen-reduction limit of spinel oxides.

Tags: candle, dftu, electrochemistry, fuel-cells, jdft, minimum-energy-pathway, orr, spinel-oxides
Bulk band structure of RuO₂ measured with soft X-ray angle-resolved photoemission spectroscopy

Altermagnetism exhibits unique physical properties such as spin-momentum locking, anomalous Hall effect, nontrivial topological phase, and giant magnetoresistance. Among all the predicted candidates, several room temperature altermagnets are suggested to host significant potential applications. RuO₂ has been proposed as the most promising candidate. However, recently, there is intense debate about whether RuO₂ exhibits magnetic order or not. Experiments by several different technologies claim the collinear magnetic order and spin splitting induced effects. However, very recent μSR results reveal no magnetic order in RuO₂ which indicates that the time reversal symmetry is not broken. Direct observation of the high-resolution bulk band structure is absent up to date, but essential to investigate the electronic structure of RuO₂. In this study, utilizing soft X-ray ARPES, we report systematic direct experimental observation of bulk band structure of RuO₂. We observed signature of bulk band splitting induced by unknown symmetry broken. The ARPES data and calculated band structureare attached in this record.

Tags: angle-resolved-photoemission-spectroscopy, band-structure, magnetism
Dynamical response of noncollinear spin systems at constrained magnetic moments

Noncollinear magnets are notoriously difficult to describe within first-principles approaches based on density-functional theory (DFT) because of the presence of low-lying spin excitations. At the level of ground-state calculations, several methods exist to constrain the magnetic moments to a predetermined configuration, and thereby accelerate convergence towards self-consistency. Their use in a perturbative context, however, remains very limited. Here we present a general methodological framework to achieve parametric control over the local spin moments at the linear-response level. Our strategy builds on the concept of Legendre transform to switch between various flavors of magnetic functionals, and to relate their second derivatives via simple linear-algebra operations. Thereby, we can address an arbitrary response function at the time-dependent DFT level of theory with optimal accuracy and minimal computational effort. In the low frequency limit, we identify the leading correction to the existing adiabatic formulation of the problem [S. Ren , Phys. Rev. X 14, 011041 (2024)], consisting in a renormalization of the phonon and magnon masses due to electron inertia. As a demonstration, we apply our methodology to the THz optical response of bulk CrI3 and Cr2O3, where we identify contributions from hybrid (electro)magnons with mixed spin-lattice character. 

This dataset contains the input and output files used to produce the results presented in sections V and VI of the manuscript.

Tags: density-functional-perturbation-theory, magnetic-insulators, magnon-phonon-coupling, nonadiabatic-regime
Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based models to methods based on the three-dimensional structure of reactants and products. In tandem, many representations have been developed to predict experimental targets, which may hold promise for barrier prediction as well. Here, we bring together all of these efforts and benchmark various methods (Morgan fingerprints, the DRFP, the CGR representation-based Chemprop, SLATMd, B²Rl², EquiReact and language model BERT + RXNFP) for the prediction of computed activation barriers on three diverse datasets.
This record includes data to support the article "Benchmarking machine-readable vectors of chemical reactions on computed activation barriers". This supports the github repository https://github.com/lcmd-epfl/benchmark-barrier-learning which contains the codes and duplicates the data.

Tags: benchmark, chemical-reactions, epfl, erc, machine-learning, marvelp2, snsf
Density functional theory study of silicon nanowires functionalized by grafting organic molecules

Functionalizing Silicon Nanowires (SiNWs) through covalent attachment of organic molecules offers diverse advantages, including surface passivation, introduction of new functionalities, and enhanced material performance in applications like electronic devices and biosensors. Given the wide range of available functional molecules, systematic large-scale screening is crucial. Therefore, we developed an automated computational workflow using Python scripts in conjunction with the AiiDa framework to explore structural configurations of functional molecules adsorbed onto silicon surfaces. This workflow generates multiple adhesion configurations corresponding to different binding orientations using surface and functional molecule structures as inputs. This dataset contains data related to the structural optimization of molecules with single, double, and triple carbon-carbon bonds attached to the nanowire surface in various adhesion configurations. We describe the chemisorption on SiNWs using the slab models for the Si facets since our reference are samples with diameters of SiNWs around 50 nm, while the quantum confinement effects are important for diameters below 10 nm. For each configuration, structural characterization was conducted by calculating quantities including the bond distance between the two carbons closest to the surface and their respective bond angle relative to the z-axis, the carbon-silicon bond distance and its respective bond angle relative to the z-axis, along with the molecule's rotation angle in the xy plane. The values obtained are summarized in the main folder. The version v1 of dataset contains data related to the Si(111) surface and alkanes, alkenes, and alkynes with lengths from C2 to C10. The version v2 and v3 include also the moieties from C12 to C18. The version v4 extends the dataset with the data related to the Si(110) surface and alkanes, alkenes, and alkynes with lengths from C2 to C18. This version (v5) also includes the optimized structures of SiNWs oriented along ⟨112⟩, exposing Si(110) and Si(111) facets, and featuring three different sizes: small (d = 0.7 nm), medium (d = 1.7 nm), and large (d = 2.6 nm). For each system the most stable configuration will be identified, and the analysis of the electronic properties will be conducted.

Tags: aliphatic-molecules, dft, nanowires, silicon, surface-adsorption
Quasiparticle effects and strong excitonic features in exfoliable 1D semiconducting materials

We report a first-principles study of the electronic and optical properties of recently identified one-dimensional semiconducting materials exfoliable from van der Waals-bonded bulk crystals. Specifically, we investigate four chalcogenide-based atomic chains, the covalently bonded S3 and Te3 chains, and polar-bonded As2S3 and Bi2Te3 chains, using a fully first-principles approach that combines density functional theory (DFT), density functional perturbation theory (DFPT), and many-body perturbation theory within the GW approximation and Bethe−Salpeter equation (BSE). Our vibrational analysis shows that the isolated, freestanding wires remain dynamically stable, with the zone-center optical phonon modes leading to infrared activity. The main finding of this study is the presence of very strong exciton binding energies (1−3 eV), which make these exfoliable 1D materials suitable platforms for room-temperature excitonic applications. Interestingly, the exciton character remains Wannier−Mott-like, as indicated by average electron−hole separations greater than the lattice constant. Notably, the optical gaps of these materials span a wide range - from infrared (0.8 eV, Bi2Te3), through the visible spectrum (yellow: 2.17 eV, Te3; blue: 2.71 eV, As2S3), up to ultraviolet (4.07 eV, S3) - highlighting their versatility for broadband optoelectronic applications. Our results offer a detailed, many-body perspective on the optoelectronic behavior of these low-dimensional materials and underscore their potential for applications in nanoscale optoelectronic devices.

Tags: dft, exciton, gw, one-dimensional-materials, phonons, van-der-waals-wires
Exploring the magnetic landscape of easily-exfoliable two-dimensional materials

Magnetic materials often exhibit complex energy landscapes with multiple local minima, each corresponding to a self-consistent electronic structure solution. Finding the global minimum is challenging, and heuristic methods are not always guaranteed to succeed. We apply an automated workflow to systematically explore the energy landscape of 194 magnetic monolayers from the Materials Cloud 2D crystals database and determine their ground-state magnetic order. Our approach enables effective control and sampling of orbital occupation matrices, allowing rapid identification of local minima. We reveal a diverse set of self-consistent collinear metastable states, further enriched by Hubbard-corrected energy functionals with U parameters computed from first principles using linear response theory. We categorize the monolayers by their magnetic ordering and highlight promising candidates for applications.

Tags: energy-landscape, first-principles, hubbard-corrections, marvel, pbe, pbeu, quantum-espresso, romeodft, two-dimensional-magnetic-materials
Electrostatic interactions in atomistic and machine-learned potentials for polar material: data

Long-range electrostatic interactions critically affect polar materials. However, state-of-the-art atomistic potentials, such as neural networks or Gaussian approximation potentials employed in large-scale simulations, often neglect the role of these long-range electrostatic interactions.This study introduces a novel model derived from first principles to evaluate the contribution of long-range electrostatic interactions to total energies, forces, and stresses. The model is designed to integrate seamlessly with existing short-range force fields without further first-principles calculations or retraining. The approach relies solely on physical observables, like the dielectric tensor and Born effective charges, that can be consistently calculated from first principles. We demonstrate that the model reproduces critical features, such as the LO-TO splitting and the long-wavelength phonon dispersions of polar materials, with benchmark results on the cubic phase of barium titanate (BaTiO3).

 

This dataset reports the raw dynamical matrices computed for BaTiO3 using DFPT, the bare short-range GAP potential, and the long-range corrected GAP potential.  A more accurate description of the methodology and all the parameters used to reproduce these data are discussed and reported in the referenced paper.

Tags: dft, electrostatic-interactions, machine-learning, phonons
Crystal nucleation in eutectic Al-Si alloys by machine-learned molecular dynamics

Solidification control is crucial in manufacturing technologies, as it determines the microstructureand,  consequently,  the performance of the final product.  Investigating the mechanisms occurring during the early stages of nucleation remains experimentally challenging as it initiates on nanometer length and sub-picoseconds time scales.  Large scale molecular dynamics simulations using machinelearning interatomic potential with quantum accuracy appears the dedicated approach to complex,atomic level, multidimensional mechanisms with local symmetry breaking.  A potential trained ona high-dimensional neural network on density functional theory-based ab initio molecular dynamics(AIMD) trajectories for liquid and undercooled states for Al-Si binary alloys enables us to study the  nucleation  mechanisms  occurring  at  the  early  stages  from  the  liquid  phase  near  the  eutectic composition.  Our results indicate that nucleation starts with Al at hypoeutectic compositions and with Si at hypereutectic compositions.  Whereas Al nuclei grow in a globular shape, Si ones grow with polygonal faceting, whose underlying mechanisms are further discussed.

Tags: aluminum-silicon, hdnnp, machine-learning-interatomic-potential
Anisotropic superconductivity in the quasi-one-dimensional superconductor

The intermetallic quasi-one-dimensional binary superconductor V2Ga5 was recently found to exhibit a topologically nontrivial normal state, making it a natural candidate for a topological superconductor.
By combining dc-magnetization, nuclear magnetic resonance, and muon-spin rotation (µSR) measurements on high-quality V2Ga5 single crystals, we investigate the electronic properties of its normal- and superconducting ground states. NMR measurements in the normal state indicate a strong anisotropy in both the line shifts and the relaxation rates. Such anisotropy persists also in the superconducting state, as shown by the magnetization and µSR-spectroscopy results. In the latter case, data collected at different temperatures, pressures, and directions of the magnetic field (with respect to the crystalline axes) evidence a fully-gapped, strongly anisotropic superconductivity. At the same time, hydrostatic pressure is shown to only lower the Tc value, but not to change the superfluid density nor its temperature dependence. Lastly, we discuss the search for topological signatures in the normal state of V2Ga5, as well as a peak splitting in the FFT of the µSR spectrum, possibly related to an unconventional vortex lattice. Our results suggest that V2Ga5 is a novel system, whose anisotropy plays a key role in determining its unusual electronic properties.

Tags: muon-spin-rotation-and-relaxation-spectroscopy, nuclear-magnetic-resonance, superconductivity
Non-Hermitian Mott skin effect

This file contains all the data, as well as the code necessary to reproduce the exact diagonalization calculations in Phys. Rev. Lett. 133, 076502 (2024) in which the phenomenon of a non-Hermitian Mott skin effect is introduced theoretically. Skin effects are a key differentiating feature of (one-dimensional) non-Hermitian systems, and are characterized by the exponential accumulation of charge towards one side of the system in all its eigenstates. It appears in noninteracting systems. This work finds a novel analogous effect which is specific to interacting quantum particles, where the particle density is constant, but a flavour/spin degree of freedom shows the exponential localization. This is demonstrated with exact diagonalization calculations of the non-Hermitian many-body Hamiltonian as well as the time-evolution of the Lindbladian.

Tags: correlated-electron-systems, exact-diagonalization, mott-insulators, non-hermitian-systems, skin-effect
Crystallization kinetics in Ge-rich GexTe alloys from large scale simulations with a machine-learned interatomic potential

A machine-learned interatomic potential for Ge-rich GexTe alloys has been developed aiming at uncovering the kinetics of phase separation and crystallization in these materials. The results are of interest for the operation of embedded phase change memories which exploits Ge-enrichment of GeSbTe alloys to raise the crystallization temperature. The potential is generated by fitting a large database of energies and forces computed within Density Functional Theory with the neural network scheme implemented in the DeePMD-kit package. The potential is highly accurate and suitable to describe the structural and dynamical properties of the liquid, amorphous and crystalline phases of the wide range of compositions from pure Ge and stoichiometric GeTe to the Ge-rich Ge₂Te alloy. Large scale molecular dynamics simulations revealed a crystallization mechanism which depends on temperature. At 600 K, segregation of most of Ge in excess was observed to occur on the ns time scale followed by crystallization of nearly stoichiometric GeTe regions. At 500 K, nucleation of crystalline GeTe was observed to occur before phase separation, followed by a slow crystal growth due to the concurrent expulsion of Ge in excess.

Tags: crystallization, electronic-memories, machine-learning, molecular-dynamics, phase-change-materials
Band alignments through quasiparticle self-consistent 𝐺𝑊 with efficient vertex corrections

Within many-body perturbation theory, we calculate band offsets for a set of epitaxial interfaces, including AlP/GaP, AlAs/GaAs, Ge/AlAs, Ge/GaAs, Ge/ZnSe, Si/GaP, ZnSe/GaAs, and CaF2/Si. We consider various quasiparticle self-consistent 𝐺⁡𝑊 schemes with or without including vertex functions. In particular, we consider two types of effective vertex functions complying with the Ward identity in the long range, one of which additionally carries a short-range part, which has been found to improve ionization potentials. The obtained band offsets correspond to model interface structures that match the experimental lattice parameters of the bulk components. Strain, zero-phonon renormalization, and spin-orbit coupling effects are properly accounted for. For the band offsets of the semiconductor-semiconductor interfaces, all the self-consistent 𝐺⁡𝑊 schemes yield similar mean absolute errors on the order of 0.2 eV. In the case of the CaF2/Si interface, the calculated band offsets show large indetermination spanning an interval up to 1 eV, the discrepancy with respect to experiment being correlated with the error by which the band gap of the insulator is described. Through 𝐺⁡𝑊 calculations for selected interface models, we further assess the effect of self-consistently updating the charge density. Our result support the practice of relying on semilocal or hybrid-functional schemes for determining the line-up potential.

Tags: band-alignments, epitaxial-interfaces, gw-calculations
Data-intensive exploration of the photoelectrochemical responses of main-group metal sulfides

Materials that efficiently promote the thermodynamically uphill water-splitting reaction under solar illumination are essential for generating carbon-free ("green") hydrogen. Mapping out the combinatorial space of potential photocatalysts for this reaction can be expedited using data-intensive materials exploration. The calculated band gaps and band alignments can serve as key indicators and metrics to computationally screen photoactive materials. Ternary main-group metal sulfides containing p- and s-block elements represent a promising, albeit underexplored, class of photocatalysts. Here, we computationally screen 86 candidate ternary main-group metal sulfides containing p- and s-block elements. By validating electronic structure predictions against experimental band gaps and band edges for synthetically accessible materials, we propose eight potential photocatalysts. Using computed Pourbaix diagrams, we further narrowed the candidate pool to four materials based on the predicted aqueous stability. We then synthesized and characterized these four materials and experimentally screened them for photoresponsiveness under photocatalytically relevant conditions. We also characterized their experimental band gaps and band edge positions and compared them with computational predictions. Based on the experimental screening protocols, we identify MgIn₂S₄ and BaSn₂S₅ as photoresponsive materials with sufficient aqueous stability to be considered in greater depth as potential photocatalysts for overall water-splitting. This record contains the computational predictions for the four candidates discussed in our manuscript.

Tags: photocatalysts, sulfides, water-splitting
Data-driven design of metal-organic frameworks for wet flue gas CO2 capture

In this entry is a database of 324,426 hypothetical Metal-Organic Frameworks (MOFs) that were used in a study to screen potential carbon dioxide scrubbers.  Using a method to assemble these materials with topological blueprints, we only selected materials that could be accurately represented with the MEPO-QEq charge generation method. By ensuring that the electrostatic potential is accurately represented in these materials, screening for CO2 adsorption properties would result very few false positives/negatives. The atom-centered charges reported in the CIF file for each MOF were derived from the MEPO-QEq method, which can be found under the '_atom_type_partial_charge' column in each CIF file.

The relevant data for each MOF is reported in accompanying .csv files. Post-combustion flue gas was simulated at a temperature of both 298K and 0.15 bar CO2, and 313K and 0.15 bar CO2. Mixture adsorption was simulated with the conditions 298K and 0.15:0.85 CO2/N2 with a total pressure of 1 bar. The data file reports working capacities, which is the difference of adsorption of CO2 between two thermodynamic state points. The adsorption state point(s) are mentioned above, and two desorption values were simulated; 0.1 bar CO2 at 363K (vacuum swing adsorption) and 0.7 bar CO2 at 413K (temperature swing adsorption). The data presented in the main manuscript correspond to vacuum swing conditions.

Over 8,000 materials were selected for more refined simulations, including re-defining partial atomic charges with the REPEAT method, and more detailed simulations to obtain common chemical patterns surrounding CO2 binding sites (adsorbaphores). There is an additional .csv file with these refined calculations that accompany this entry titled 'top_MOFs_screening_data.csv'.

Tags: adsorbaphore, co2-capture, gas-adsorption, high-throughput-screening, nanoporous-materials