en: Singlet fission (SF) is a two-step process in which a singlet splits into two triplets throughout the so-called correlated triplet-pair (1TT) state. Intramolecular SF (iSF) materials in particular, have attracted growing interest as they can be easily implemented in single junction solar cells and boost their power conversion efficiency. Still, the potential of iSF materials such as polymers and oligomers for photovoltaic applications has been partially hindered by their ability to go beyond the 1TT intermediate and generate free triplets, which mechanism remains poorly understood. In this work, the main aspects governing the 1TT dissociation in donor-acceptor copolymers and the key features that optimize this process are exposed. First, we show that both thermodynamics and kinetics play a crucial role in the intramolecular triplet-pair separation and second, we uncover the inherent flexibility of the donor unit as the fundamental ingredient to optimize them simultaneously. Overall, these results provide a better understanding of the intramolecular 1TT dissociation process and establish a new paradigm for the development of novel iSF active materials.

en: Optimizing the thermodynamics and kinetics of the triplet-pair dissociation in donor-acceptor copolymers for intramolecular singlet fission

donor-acceptor-copolymers

display_name: donor-acceptor-copolymers

electronic-structure

display_name: electronic-structure

epfl

display_name: epfl

marie-curie-fellowship

display_name: marie-curie-fellowship


$oid: OBSOLETE

materialscloud

approval_status: approved

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

singlet-fission

display_name: singlet-fission


en: Extensive machine-learning assisted research has been dedicated to predicting band gaps for perovskites, driven by their immense potential in photovoltaics. Yet, the effectiveness is often hampered by the lack of high-quality band-gap datasets, particularly for perovskites involving d orbitals. In this work, we consistently calculate a large dataset of band gaps with a high level of accuracy, which is rigorously validated by experimental and state-of-the-art GW band gaps. Leveraging this achievement, our machine-learning derived descriptor exhibits exceptional universality and robustness, proving effectiveness not only for single and double, halide and oxide perovskites regardless of the underlying atomic structures, but also for hybrid organic-inorganic perovskites. With this approach, we comprehensively explore up to 15,659 materials, unveiling 14 unreported lead-free perovskites with suitable band gaps for photovoltaics. Notably, MASnBr₃, FA₂SnGeBr₆, MA₂AuAuBr₆, FA₂AuAuBr₆, FA₂InBiCl₆, FA₂InBiBr₆, and Ba₂InBiO₆ stand out with direct band gaps, small effective masses, low exciton binding energies, and high stabilities.

en: High-quality data enabling universality of band-gap descriptor and discovery of photovoltaic perovskites

band-gap-descriptor

display_name: band-gap-descriptor

high-quality-dataset

display_name: high-quality-dataset

hybrid-functional

display_name: hybrid-functional

machine-learning

display_name: machine-learning

photoboltaic-perovskites

display_name: photoboltaic-perovskites


en: Dimensionality provides a clear fingerprint on the dispersion of infrared-active, polar-optical phonons. For these phonons, the local dipoles parametrized by the Born effective charges drive the LO-TO splitting of bulk materials; this splitting actually breaks down in two-dimensional materials. Here, we develop the theory for one-dimensional (1D) systems -nanowires, nanotubes, and atomic and polymeric chains. Combining an analytical model with the implementation of density-functional perturbation theory in 1D boundary conditions, we show that the dielectric splitting in the dispersion relations collapses as x²log(x) at the zone center. The dielectric properties and the radius of the 1D materials are linked by the present work to these red shifts, opening infrared and Raman characterization avenues.

en: Infrared-active phonons in one-dimensional materials and their spectroscopic signatures

ab-initio

display_name: ab-initio

density-functional-theory

display_name: density-functional-theory

marvel

display_name: marvel

one-dimensional-materials

display_name: one-dimensional-materials

phonons

display_name: phonons

raman

display_name: raman


en: The reliable ab-initio description of strongly correlated materials is a long-sought capability in condensed matter physics. The GW+EDMFT method is a promising scheme, which provides a self-consistent description of correlations and screening, and does not require user-provided parameters. In order to test the reliability of this approach we apply it to the experimentally well characterized perovskite compound Ca₂RuO₄, in which a temperature-dependent structural deformation drives a paramagnetic metal-insulator transition. Our results demonstrate that the nonlocal polarization and self-energy components introduced by GW are essential for setting the correct balance between interactions and bandwidths, and that the GW+EDMFT scheme produces remarkably accurate predictions of the electronic properties of this strongly correlated material.

en: Fully ab-initio electronic structure of Ca₂RuO₄

first-principles

display_name: first-principles

gw-method

display_name: gw-method

snsf

display_name: snsf

strongly-correlated-systems

display_name: strongly-correlated-systems


electronic-structure

display_name: electronic-structure


en: We perform extensive molecular dynamics simulations of an ensemble of realistic microgel particles in swollen conditions in a wide range of packing fractions ζ. We compare neutral and charged microgels, where we consider charge distribution adherent to experimental conditions. Through a detailed analysis of singleparticle behavior, we are able to identify the different regimes occurring upon increasing concentration: from shrinking to deformation and interpenetration, always connecting our findings with available experimental observations. We then link these single-particle features with the collective behavior of the suspension, finding evidence of a structural reentrance that has no counterpart in the dynamics. Hence, while the maximum of the radial distribution function displays a nonmonotonic behavior with increasing ζ, the dynamics, quantified by the microgels' mean-squared displacement, always slows down. This behavior, at odds with the simple Hertzian model, can be described by a phenomenological multi-Hertzian model, which takes into account the enhanced internal stiffness of the core. However, also this model fails when deformation enters into play, whereby more realistic many-body models are required. Thanks to our analysis, we are able to unveil the key physical mechanisms, shrinking and deformation, giving rise to the structural reentrance that holds up to very large packing fractions. We further identify key similarities and differences between neutral and charged microgels, for which we detect at high enough ζ the fusion of charged shells, previously invoked to explain key experimental findings, and responsible for the structural reentrance. Overall, our study establishes a powerful framework to uncover the physics of microgel suspensions, paving the way to tackle different regimes, e.g., high temperature, and internal architectures, such as for hollow and ultralow-cross-linked microgels, where experimental evidence is still limited.

en: Numerical study of neutral and charged microgel suspensions: from single-particle to collective behavior

collective-behavior

display_name: collective-behavior

elasticity

display_name: elasticity

microgels

display_name: microgels


en: Melt post-condensation, thermal, and thermo-oxidative degradation of a cyclo-aliphatic polyamide were studied through time-resolved rheometry (TRR). The implemented TRR elucidates structural changes occurring during two concurrent phenomena, namely melt post-condensation and thermal/thermo-oxidative degradation, during the time sweep in a parallel plate rheometer. TRR measurements were conducted on neat polyamide under nitrogen (inert/non-oxidative) and air (oxidative) environment at 3 % strain amplitude and a range of frequencies between 0.1 and 100 rad/sec for two hours. At temperatures of 260, 270, and 275 °C, a dual-stage time-dependent growth in viscoelastic properties was observed under an oxidative environment. Thermo-oxidative degradation of polyamide melt 270 °C was shown to occur from the exposed sample edge, continuing inwards, effectively reducing the radius of the unoxidized polymer melt by 6.4 %. A modeling approach using MATLAB is presented to interpret and differentiate the rheological behavior of edge oxidized gel and bulk polymer within the volume of the sample melt. The code removes the effects of the strain limiter and the edge gelation from experimental measurements in predicting the material properties.

en: Predicting the influence of edge oxidation in parallel-plate rheometry

edge-oxidation

display_name: edge-oxidation

experimental

display_name: experimental

matlab

display_name: matlab


en:
Prediction of microstructure evolution during material processing is essential to control the material properties. Simulation tools for microstructure evolution prediction based on physical concepts are computationally expensive and time-consuming. Therefore, they are not practical when either there is an urgent need for microstructure morphology during the process, or there is a need to generate big microstructure datasets. Essentially, microstructure evolution prediction is a spatiotemporal sequence prediction problem, where the prediction of material microstructure is difficult due to different process histories and chemistry. We propose a Predictive Recurrent Neural Network (PredRNN) model for the microstructure prediction, which extends the inner-layer transition function of memory states in LSTMs to spatiotemporal memory flow. As a case study, we used a dataset from spinodal decomposition simulation of FeCrCo alloy created by the phase-field method for training and predicting future microstructures by previous observations. The results show that the trained network predicts quantitatively accurate microstructure morphologies while it is several orders of magnitude faster than the phase field method.
The trained model aims to generate future material microstructures by learning from the historical microstructures, where the visual dynamics are believed to have modular structures that can be learned with compositional subsystems. A PredRNN (https://github.com/thuml/predrnn-pytorch) model was trained to predict Fe-Cr-Co microstructures evolution during the spinodal decomposition.

en: Spatiotemporal prediction of microstructure evolution with predictive recurrent neural network

phase-field-spinodal-decomposition

display_name: phase-field-spinodal-decomposition


en: In stark contrast to inorganic quantum wells, hybrid quantum wells based on metal-organic semiconductors are characterized by relatively soft lattices. In the latter, excitonic states are deeply affected by coupling with optical phonons. A detailed understanding of the lattice role in exciton dynamics is therefore essential to improve the optoelectronic performance of these materials. Beyond 2D metal halide perovskites, layered metal-organic chalcogenides (MOCs) are an air-stable, underexplored material class hosting complex excitonic phenomena that could be exploited as photodetectors, light emitting devices and ultrafast photoswitches. Here, we elucidate the role of lattice phonons in the optical transitions at different temperatures in the prototypical MOC [AgSePh]∞. We detect coherent exciton-phonon coupling by pump-probe transient absorption spectroscopy, dominated by a Fröhlich interaction with optical phonons at 7 and 12 meV. Through a concerted use of ab initio calculations, linear and Raman spectroscopies, we reveal a distinct phonon anharmonicity clearly manifests in a non-trivial temperature-dependent Stokes shift, deeply impacting the excitonic photoluminescence. The temperature-dependent absorption and photoluminesce data further reveals a Huang-Rhys parameter of about 1.7 suggesting a strong exciton-phonon coupling in both optical transitions. Finally, angle-resolved Raman spectroscopy hints at a distinct anisotropy in the phonon coupling process. Our results indicate shared characters in the exciton-phonon interaction for MOCs and the 2D perovskites, either implying shared polar character of the inorganic sublattices and/or a more important role of the hybrid quantum well superlattice structure.

en: Anharmonic exciton-phonon coupling in metal-organic chalcogenides hybrid quantum wells

exciton-phonon-coupling

display_name: exciton-phonon-coupling

metal-organic-chalcogenides

display_name: metal-organic-chalcogenides

raman-spectroscopy

display_name: raman-spectroscopy


en: Simulations of condensed matter systems at the hybrid density functional theory (DFT) level  pose significant computational challenges. The elevated costs arise from the non-local nature of the Hartree-Fock exchange (HFX) in conjunction with the necessity to approach the thermodynamic limit (TDL). In this work, we address these issues with the development of a new efficient method for the calculation of HFX in periodic systems, employing k-point sampling. We rely on a local atom-specific resolution-of-the-identity scheme, the use of atom-centered Gaussian type orbitals (GTOs), and the truncation of the Coulomb interaction to limit computational complexity. Our real-space approach exhibits a scaling that is  at worst linear with the number of k-points.  Issues related to basis set diffuseness are effectively addressed through the auxiliary density matrix method (ADMM). We report the implementation in the CP2K software package, as well as accuracy and performance benchmarks. The method demonstrates excellent agreement with equivalent Gamma-point supercell calculations in terms of relative energies and nuclear gradients. Good strong and weak scaling performances, as well as GPU acceleration, make this implementation a promising candidate for high-performance computing.

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Efficient periodic resolution-of-the-identity Hartree-Fock exchange method with k-point sampling and Gaussian basis sets

auxiliary-density-matrix-method

display_name: auxiliary-density-matrix-method

high-performance-computing

display_name: high-performance-computing

hybrid-dft

display_name: hybrid-dft

k-point-sampling

display_name: k-point-sampling

method-development

display_name: method-development

periodic-hartree-fock

display_name: periodic-hartree-fock


materialscloud

en: Materials Cloud

envi

en: Environment


en: We present the NaviCatGA package, a versatile genetic algorithm capable of optimizing molecular catalyst structures using well-suited fitness functions to achieve a set of targeted properties. The flexibility and generality of this tool are demonstrated with two examples: i) Ligand optimization and exploration for Ni-catalyzed aryl-ether cleavage manipulating SMILES and using a fitness function derived from molecular volcano plots, ii) multiobjective (i.e., activity/selectivity) optimization of bipyridine N.N'-dioxide Lewis basic organocatalysts for the asymmetric propargylation of benzaldehyde from 3D molecular fragments. We show that evolutionary optimization, enabled by NaviCatGA, is an efficient way of accelerating catalyst discovery that bypasses combinatorial scaling issues and incorporates compelling chemical constraints.

en: Genetic optimization of homogeneous catalysts

catalysis

display_name: catalysis

homogeneous-catalysis

display_name: homogeneous-catalysis

optimization

display_name: optimization

organocatalysis

display_name: organocatalysis

volcano-plot

display_name: volcano-plot


en:
The Materials Cloud three-dimensional database is a curated set of relaxed three-dimensional crystal structures based on raw CIF data taken from the external experimental databases MPDS, COD and ICSD. The raw CIF data have been imported, cleaned and parsed into a crystal structure; their ground-state has been computed using the SIRIUS-enabled pw.x code of the Quantum ESPRESSO distribution, and tight tolerance criteria for the calculations using the SSSP protocols.

This entire procedure is encoded into an AiiDA workflow which automates the process while keeping full data provenance. Here, since the original source data of the ICSD and MPDS databases are copyrighted, only the provenance of the final SCF calculation on the relaxed structures can be made publicly available.

The MC3D ID numbers come from a list of unique "parent" stoichiometric structures that has been created and curated from a collection of these experimental databases. Once a parent structure has been optimized using density-functional theory, it is made public and added to the online Discover section of the Materials Cloud (as mentioned, copyright might prevent publishing the original parent). Note that since not all structures have been calculated, some ID numbers are missing from the public version of the database. The full ID of each structure also contains as an appended modifier the functional that was used in the calculations. Since the ID number points to the same unique parent, mc3d-1234/pbe and mc3d-1234/pbesol have the same starting point, but have been then relaxed according to their respective functionals.

en: Materials Cloud three-dimensional crystals database (MC3D)

aiida

display_name: aiida

database

display_name: database

density-functional-theory

display_name: density-functional-theory

marvelhpc

display_name: marvelhpc

max

display_name: max

pasc

display_name: pasc

prace

display_name: prace


en: Prismatic slip of the screw  dislocation in magnesium at temperatures ≳ 150 K is understood to be governed by double-cross-slip of the stable basal screw through the unstable prism screw and back to the basal screw, with the activation energy controlled by the formation energy of two basal-basal kinks. However, atomistic studies of the double-kink process predict activation energies roughly twice those derived experimentally. Here, a new mechanism of prism glide is proposed, analyzed theoretically, and demonstrated qualitatively and quantitatively via direct molecular dynamics (MD) simulations. The new mechanism is intrinsically 3d, and involves the nucleation of a single kink at the junction where a 3d prismatic dislocation loop transitions from the basal screw segment to non-screw prismatic character. The relevant kink energies are calculated using recently-developed Neural-Network Potentials (NNPs) for Mg that show good agreement versus DFT for basal and prism  dislocations, enabling a parameter-free analytic model for the activation barrier. Direct MD simulations show both operation of the precise proposed mechanism and a stress-dependent activation barrier that agrees reasonably with the analytic model. Predictions of dislocation velocity compare very well with in-situ TEM data, and macroscopic strength versus temperature can also be understood. Overall, the new intrinsically 3d mechanism for dislocation glide due to single kink nucleation rather than double-kink nucleation explains key features of the prismatic slip in Mg and may have broader applicability in other metals where kink nucleation processes control thermally-activated flow.

en: Mechanism of thermally-activated prismatic slip in Mg

magnesium

display_name: magnesium

marvel

display_name: marvel

neural-network-potential

display_name: neural-network-potential

prismatic-slip

display_name: prismatic-slip


en: Weyl nodes can be classified into zero-dimensional (0D) Weyl points, 1D Weyl nodal lines, and 2D Weyl nodal surfaces (WNS), which possess finite Chern numbers. Up to date, the largest Chern number of WPs identified in Weyl semimetals is 4, which is thought to be a maximal value for linearly crossing points in solids. On the other hand, whether the Chern numbers of nonzero-dimensional linear crossing Weyl nodal objects have one upper limit is still an open question. In this work, combining angle-resolved photoemission spectroscopy with density-functional theory calculations, we show that the chiral crystal AlPt hosts a cube-shaped charged WNS which is formed by the linear crossings of two singly degenerate bands. Different from conventional Weyl nodes, the cube-shaped nodal surface in AlPt is enforced by nonsymmorphic chiral symmetries and time-reversal symmetry rather than accidental band crossings, and it possesses a giant Chern number |C|=26. Moreover, our results and analysis prove that there is no upper limit for the Chern numbers of such kind of 2D Weyl nodal object. This record includes the data in the related paper Phys. Rev. B 105, 115118 – Published 14 March 2022.

en: Giant Chern number of a Weyl nodal surface without upper limit

giant-chern-number

display_name: giant-chern-number

unpaired-weyl-point

display_name: unpaired-weyl-point

weyl-nodal-surface

display_name: weyl-nodal-surface

weyl-nodal-wall

display_name: weyl-nodal-wall


en: Magnetic materials can display many solutions to the electronic-structure problem, corresponding to different local or global minima of the energy functional. In Hartree-Fock or density-functional theory different single-determinant solutions lead to different magnetizations, ionic oxidation states, hybridizations, and inter-site magnetic couplings. The vast majority of these states can be fingerprinted through their projection on the atomic orbitals of the magnetic ions. We have devised an approach that provides an effective control over these occupation matrices, allowing us to systematically explore the landscape of the potential energy surface. We showcase the emergence of a complex zoology of self-consistent states; even more so when semi-local density-functional theory is augmented - and typically made more accurate - by Hubbard corrections. Such extensive explorations allow to robustly identify the ground state of magnetic systems, and to assess the accuracy (or not) of current functionals and approximations

en: The energy landscape of magnetic materials

high-throughput

display_name: high-throughput

openmodel

display_name: openmodel


en:
Quantum spin Hall insulators (QSHIs) are a class of topological materials that has been extensively studied during the past decade. One of their distinctive features is the presence of a finite band gap in the bulk and gapless, topologically protected edge states that are spin-momentum locked. These materials are characterized by a Z₂ topological order where, in the 2D case, a single topological invariant can be even or odd for a trivial or a topological material, respectively. Thanks to their interesting properties, such as the realization of dissipationless spin currents, spin pumping and spin filtering, they are of great interest in the field of electronics, spintronics and quantum computing. In this work we perform an high-throughput screening of QSHI starting from a set of 783 2D exfoliable materials (603 after removing materials with lanthanides), recently identified from a systematic screening of the ICSD, COD, and MPDS databases (MC2D). In this screening we identify 4 Z₂ topological insulators, 3 of which already known in the literature, and 7 direct gap metals with the potential of becoming QSHIs under a reasonable external perturbation.

All the data are also displayed in the dedicated DISCOVER section "2D topological insulators" https://www.materialscloud.org/discover/2dtopo/dashboard/ptable

en: A complementary screening for quantum spin Hall insulators in 2D exfoliable materials

dft

display_name: dft

quantum-espresso

display_name: quantum-espresso

quantum-spin-hall

display_name: quantum-spin-hall

topological-insulator

display_name: topological-insulator


en: In contrast to inorganic quantum wells, hybrid quantum wells (HQWs) based on metal-organic semiconductors are characterized by relatively soft lattices, in which excitonic states can strongly couple to lattice phonons. Therefore, understanding the lattice's impact on exciton dynamics is essential for harnessing the optoelectronic potential of HQWs. Beyond 2D metal halide perovskites, layered metal-organic chalcogenides (MOCs) are an air-stable, underexplored material class hosting room-temperature excitons that could be exploited as photodetectors, light emitting devices, and ultrafast photoswitches. Here, we elucidate the role of phonons in the optical transitions of the prototypical MOC [AgSePh]∞. Impulsive stimulated Raman scattering (ISRS) allows us to detect coherent exciton oscillations driven by Fröhlich interaction with low-energy optical phonons. Steady state absorption and Raman spectroscopies reveal a strong exciton-phonon coupling (Huang-Rhys parameter ~1.7) and its anharmonicity, manifested as non-trivial temperature-dependent Stokes shift. Our ab initio calculations support these observations, hinting at an anharmonic behavior of the low-energy phonons < 200 cm⁻¹. Our results untangle complex exciton-phonon interactions in MOCs, establishing an ideal testbed for room-temperature many-body phenomena.

en: Anharmonic exciton-phonon coupling in metal-organic chalcogenides hybrid quantum wells

exciton-phonon-coupling

display_name: exciton-phonon-coupling

metal-organic-chalcogenides

display_name: metal-organic-chalcogenides

raman-spectroscopy

display_name: raman-spectroscopy


materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).


en: The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation of experimentally observable material properties such as optical absorption and electrical conductivity. We discuss the challenges inherent in the construction of a machine-learning (ML) framework aimed at predicting the DOS as a combination of local contributions that depend in turn on the geometric configuration of neighbors around each atom, using quasiparticle energy levels from density functional theory as training data. We present a challenging case study that includes configurations of silicon spanning a broad set of thermodynamic conditions, ranging from bulk structures to clusters and from semiconducting to metallic behavior. We compare different approaches to represent the DOS, and the accuracy of predicting quantities such as the Fermi level, the electron density at the Fermi level, or the band energy, either directly or as a side product of the evaluation of the DOS. We find that the performance of the model depends crucially on the resolution chosen to smooth the DOS and that there is a tradeoff to be made between the systematic error associated with the smoothing and the error in the ML model for a specific structure. We find however that the errors are not strongly correlated among similar structures, and so the average DOS over an ensemble of configurations is in very good agreement with the reference electronic structure calculations, despite the large nominal error on individual configurations. We demonstrate the usefulness of this approach by computing the density of states of a large amorphous silicon sample, for which it would be prohibitively expensive to compute the DOS by direct electronic structure calculations and show how the atom-centered decomposition of the DOS that is obtained through our model can be used to extract physical insights into the connections between structural and electronic features.

en: Materials Cloud

en: Learning the electronic density of states in condensed matter

cosmo-epfl

display_name: cosmo-epfl

snsf

display_name: snsf


envi

en: Environment

marvel

display_name: marvel


en: In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. We show that when coupled with quantum machine learning (QML) based surrogate methods the computational burden can be alleviated such that QMC shows clear potential to undergird the formation of high quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: The fixed node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons set based QML (AQML) models. Numerical evidence presented includes converged DMC results for over one thousand small organic molecules with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules with 9 heavy atoms to validate the AQML predictions. Numerical evidence collected for 𝛥-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies  with near chemical accuracy throughout chemical space. In this archive, we present DMC energies for over one thousand small organic molecules with up to 5 heavy atoms, and 50 medium sized organic molecules with 9 heavy atoms, as well as energies computed at cheaper levels of theory such as HF, DFT, MP2 and CCSD(T).

en: Fixed node diffusion Monte Carlo energies for over one thousand small organic molecules

chemical-space

display_name: chemical-space

quantum-machine-learning

display_name: quantum-machine-learning

quantum-monte-carlo

display_name: quantum-monte-carlo


en:
Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional formulation. These functionals address two fundamental issues with density functional theory (DFT). First, while Kohn-Sham eigenvalues can loosely mirror experimental quasiparticle energies, they are not meant to reproduce excitation energies and there is formally no connection between the two (except for the HOMO for the exact functional). Second, (semi-)local DFT deviates from the expected piecewise linear behavior of the energy as a function of the total number of electrons. This can make eigenvalues an even poorer proxy for quasiparticle energies and, together with the absence of the exchange-correlation derivative discontinuity, contributes to DFT's underestimation of band gaps. By enforcing a generalized piecewise linearity condition to the entire electronic manifold, Koopmans functionals yield molecular orbital energies and solids-state band structures with comparable accuracy to many-body perturbation theory but at greatly reduced computational cost and preserving a functional formulation. This paper introduces "koopmans", an open-source package that contains all of the code and workflows needed to perform Koopmans functional calculations without requiring expert knowledge. The theory and algorithms behind Koopmans functionals are summarized, and it is shown how one can easily use the koopmans package to obtain reliable spectral properties of molecules and materials.

This archive contains files that accompany the article of the same name.

en: koopmans: an open-source package for accurately and efficiently predicting spectral properties with Koopmans functionals

charged-excitations

display_name: charged-excitations

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure

koopmans-spectral-functionals

display_name: koopmans-spectral-functionals

orbital-density-dependent-functionals

display_name: orbital-density-dependent-functionals


en: Thin film organic photovoltaics (OPVs) aspire to extract solar energy in a green, high efficiency, and cost-effective pathway, offering a sustainable solution to energy and ecosystem. We take the effort of developing low ecological harmful solvent processing of OPV devices and mini-modules, to strengthen the environmental benefits of OPV. New formula utilizing safer solvents enhanced with additives affords an optimal laboratory device efficiency of 20% as well as superior operational and thermal stability. Mini-module shows an efficiency up to 17.6% in upscaled processing, representing the highest performance for green solvent OPV development. Suitable nanoscaled phase separation is obtained in together with a micron-scale surface wrinkle pattern from the new developed processing strategy. Thus efficient photon-to-electron conversion at nano-interfaces and light extraction in broader receiving angles is achieved, which allows for more flexible installation geometries in building-integrated applications.

en: Organic solar cells with eco-friendly preparation to achieve an efficiency of 20%

long-term-lifetime

display_name: long-term-lifetime

organic-solar-cells

display_name: organic-solar-cells

sustainability

display_name: sustainability


en: Prismatic slip in magnesium at temperatures T ≲ 150 K occurs at ∼ 100 MPa independent of temperature, and jerky flow due to large prismatic dislocation glide distances is observed; this athermal regime is not understood. In contrast, the behavior at T ≳ 150 K is understood to be governed by a thermally-activated double-cross-slip of the stable basal screw dislocation through an unstable or weakly metastable prism screw configuration and back to the basal screw. Here, a range of neural network potentials (NNPs) that are very similar for many properties of Mg including the basal-prism-basal cross-slip path and pro- cess, are shown to have an instability in prism slip at a potential-dependent critical stress. One NNP, NNP-77, has a critical instability stress in good agreement with experiments and also has basal-prism-basal transition path energies in very good agreement with DFT results, making it an excellent potential for understanding Mg prism slip. Full 3d simulations of the expansion of a prismatic loop using NNP-77 then also show a transition from cross-slip onto the basal plane at low stresses to prismatic loop expansion with no cross- slip at higher stresses, consistent with in-situ TEM observations. These results reveal (i) the origin and prediction of the observed unstable low-T prismatic slip in Mg and (ii) the critical use of machine-learning potentials to guide discovery and understanding of new important metallurgical behavior.

en: A low-temperature prismatic slip instability in Mg understood using machine learning potentials

magnesium

display_name: magnesium

minimum-energy-path

display_name: minimum-energy-path

neural-network-potential

display_name: neural-network-potential

prismatic-slip

display_name: prismatic-slip


en: A key strategy to design environmental barrier coatings has been focused on doping multiple rare-earth principal components into β-type rare-earth disilicates (RE2Si2O7) to achieve versatile property optimization. However, controlling the phase formation capability of (nRExi)2Si2O7 remains a crucial challenge, due to the complex polymorphic phase competitions and evolutions led by different RE3+ combination. Herein by fabricating twenty-one model (REI0.25REII0.25REIII0.25REIV0.25)2Si2O7 compounds, we find that their formation capability can be evaluated by the ability to accommodate configurational randomness of multiple RE3+ cations in β-type lattice while preventing the β-to-γ polymorphic transformation. The phase formation and stabilization are controlled by the average RE3+ radius and the deviations of different RE3+ combinations. Subsequently, based on high-throughput density-functional-theory calculations, we propose that the configurational entropy of mixing is a reliable descriptor to predict the phase formation of β-type (nRExi)2Si2O7. The results may accelerate the design of novel (nRExi)2Si2O7 materials with tailored compositions and controlled polymorphic phases.

en: Phase formation capability of β-(REI0.25REII0.25REIII0.25REIV0.25)2Si2O7: towards compositional design of multiple rare earth principal components

density-functional-theory

display_name: density-functional-theory

environmental-barrier-coating

display_name: environmental-barrier-coating

experimental

display_name: experimental

high-entropy-ceramics

display_name: high-entropy-ceramics

phase-formation

display_name: phase-formation

rare-earth-disilicate

display_name: rare-earth-disilicate


en: A key strategy to design environmental barrier coatings focuses on doping multiple rare-earth principal components into β-type rare-earth disilicates (RE2Si2O7) to achieve versatile property optimization. However, controlling the phase formation capability of (nRExi)2Si2O7 remains a crucial challenge, due to the complex polymorphic phase competitions and evolutions led by different RE3+ combination. Herein, by fabricating twenty-one model (REI0.25REII0.25REIII0.25REIV0.25)2Si2O7 compounds, we find that their formation capability can be evaluated by the ability to accommodate configurational randomness of multiple RE3+ cations in β-type lattice while preventing the β-to-γ polymorphic transformation. The phase formation and stabilization are controlled by the average RE3+ radius and the deviations of different RE3+ combinations. Subsequently, based on high-throughput density-functional-theory calculations, we propose that the configurational entropy of mixing is a reliable descriptor to predict the phase formation of β-type (nRExi)2Si2O7. The results may accelerate the design of (nRExi)2Si2O7 materials with tailored compositions and controlled polymorphic phases.

en: Phase formation capability and compositional design of β-phase multiple rare-earth principal component disilicates


materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).


en:
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency whilst guaranteeing interpretability and transferability. In this work, we demonstrate the wide utility of prediction rigidities, a family of metrics derived from the loss function, in understanding the robustness of ML model predictions. We show that the prediction rigidities allow the assessment of the model not only at the global level, but also on the local or the component-wise level at which the intermediate (e.g. atomic, body-ordered, or range-separated) predictions are made. We leverage these metrics to understand the learning behavior of different ML models, and to guide efficient dataset construction for model training. We finally implement the formalism for a ML model targeting a coarse-grained system to demonstrate the applicability of the prediction rigidities to an even broader class of atomistic modeling problems.
This record contains all the data used for the analyses conducted in the associated work published in Faraday Discussions.

en: Prediction rigidities for data-driven chemistry

machine-learning

display_name: machine-learning

ml-model-robustness

display_name: ml-model-robustness

prediction-rigidity

display_name: prediction-rigidity

uncertainty-quantification

display_name: uncertainty-quantification


materialscloud

en: Materials Cloud


en: Augmented plane wave methods enable an efficient description of atom-centered or localized features of the electronic density, circumventing high energy cutoffs and thus prohibitive computational costs of pure plane wave formulations. To complement existing implementations for ground-state properties and excitation energies, we present the extension of the Gaussian and augmented plane wave method to excited-state nuclear gradients within the Tamm–Dancoff approximation of time-dependent density functional theory and its implementation in the CP2K program package. Benchmarks for a test set of 35 small molecules demonstrate that maximum errors in the nuclear forces for excited states of singlet and triplet spin multiplicity are smaller than 0.1 eV/Å. The method is furthermore applied to the calculation of the zero-phonon line of defective hexagonal boron nitride. This spectral feature is reproduced with an error of 0.6 eV in comparison to GW–Bethe–Salpeter reference computations and 0.4 eV in comparison to experimental measurements. Accuracy assessments and applications thus demonstrate the potential use of the outlined developments for large-scale applications on excited-state properties of extended systems. The data in this record are supplementary data to the published article with DOI 10.1021/acs.jctc.4c00614.

en: Excited-state forces with the Gaussian and augmented plane wave method for the Tamm–Dancoff approximation of time-dependent density functional theory

envi

en: Environment

augmented-plane-wave-methods

display_name: augmented-plane-wave-methods

gaussian-basis-sets

display_name: gaussian-basis-sets


en: Complex oxide functionality, such as ferroelectricity, magnetism or superconductivity is often achieved in epitaxial thin-film geometries. Oxygen vacancies tend to be the dominant type of defect in these materials but a fundamental understanding of their stability and electronic structure has so far mostly been established in the bulk or strained bulk, neglecting interfaces and surfaces present in a thin-film geometry. We investigate here, via density functional theory calculations, oxygen vacancies in the model system of a SrMnO3 (SMO) thin film grown on a SrTiO3 (STO) (001) substrate. Structural and electronic differences compared to bulk SMO result mainly from undercoordination at the film surface. The changed crystal field leads to a depletion of subsurface valence-band states and transfer of this charge to surface Mn atoms, both of which strongly affect the defect chemistry in the film. The result is a strong preference of oxygen vacancies in the surface region compared to deeper layers. Finally, for metastable oxygen vacancies in the substrate, we predict a spatial separation of the defect from its excess charge, the latter being accommodated in the film but close to the substrate boundary. These results show that surface and interface effects lead to significant differences in stability and electronic structure of oxygen vacancies in thin-film geometries compared to the (strained) bulk.

en: Surface and interface effects in oxygen deficient SrMnO3 thin films grown on SrTiO3

crystal-field

display_name: crystal-field

cscs

display_name: cscs

interface

display_name: interface

oxygen-vacancy

display_name: oxygen-vacancy

snsf

display_name: snsf

surface

display_name: surface

thin-film

display_name: thin-film


en: In the upcoming process to overcome the limitations of the standard von Neumann architecture, synaptic electronics is gaining a primary role for the development of in-memory computing. In this field, Ge-based compounds have been proposed as switching materials for nonvolatile memory devices and for selectors. By employing the classical molecular dynamics, we study the structural features of both the liquid states at 1500 K and the amorphous phase at 300 K of Ge-rich and Se-rich chalcogenides binary Ge_x Se_1–x systems in the range 0.4 ≤ x ≤ 0.6. The simulations rely on a model of interatomic potentials where ions interact through steric repulsion, as well as Coulomb and charge–dipole interactions given by the large electronic polarizability of Se ions. Our results indicate the formation of temperature-dependent hierarchical structures with short-range local orders and medium-range structures, which vary with the Ge content. Our work demonstrates that nanosecond-long simulations, not accessible via ab initio techniques, are required to obtain a realistic amorphous phase from the melt. Our classical molecular dynamics simulations are able to describe the profound structural differences between the melt and the glassy structures of GeSe chalcogenides. These results open to the understanding of the interplay between chemical composition, atomic structure, and electrical properties in switching materials.

en: Hierarchical short- and medium-range order structures in amorphous Ge_x Se_1–x for selectors applications

amorphous

display_name: amorphous

chalcogenides

display_name: chalcogenides

intersect

display_name: intersect

molecular-dynamics

display_name: molecular-dynamics


en:
Data for journal article.
We provide the input and outputs of the AGWX suite (see article) used in the work. Also, we add the plots of the obtained Green's function for all densities, k-points, and frequencies.

en: A unified Green's function approach for spectral and thermodynamic properties from algorithmic inversion of dynamical potentials

computational-materials-science

display_name: computational-materials-science

dyson-equation

display_name: dyson-equation

embedding-theory

display_name: embedding-theory

homogeneous-electron-gas

display_name: homogeneous-electron-gas

many-body-perturbation-theory-gw

display_name: many-body-perturbation-theory-gw


en: The α/β interface is central to the microstructure and mechanical properties of titanium alloys. We investigate the structure, thermodynamics and migration of the coherent and semicoherent Ti α/β interfaces as a function of temperature and misfit strain via molecular dynamics (MD) simulations, thermodynamic integration and an accurate, DFT-trained Deep Potential. The structure of an equilibrium semicoherent interface consists of an array of steps, an array of misfit dislocations, and coherent terraces. Analysis determines the dislocation and step (disconnection) array structure and habit plane. The MD simulations show the detailed interface morphology dictated by intersecting disconnection arrays. The steps are shown to facilitate α/β interface migration, while the misfit dislocations lead to interface drag; the drag mechanism is different depending on the direction of interface migration. These results are used to predict the nature of α phase nucleation on cooling through the α-β phase transition.

en: Coherent and semicoherent interfaces in titanium: structure, thermodynamics, migration

free-energy

display_name: free-energy

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

titanium-interfaces

display_name: titanium-interfaces


en: Organic semiconductors with high-spin ground states are fascinating because they could enable fundamental understanding on the spin-related phenomenon in light element and provide opportunities for organic magnetic and quantum materials. Although high-spin ground states have been observed in some quinoidal type small molecules or doped organic semiconductors, semiconducting polymers with high-spin at their neutral ground state are rarely reported. Here we report three high-mobility semiconducting polymers with different spin ground states. We show that polymer building blocks with small singlet-triplet energy gap (ΔES-T) could enable small ΔES-T gap and increase the diradical character in copolymers. We demonstrate that the electronic structure, spin density, and solid-state interchain interactions in the high-spin polymers are crucial for their ground states. Polymers with a triplet ground state (S = 1) could exhibit doublet (S = 1/2) behavior due to different spin distributions and solid-state interchain spin-spin interactions. Besides, these polymers showed outstanding charge transport properties with high hole/electron mobilities and can be both n- and p-doped with superior conductivities. Our results demonstrate a rational approach to high-mobility semiconducting polymers with different spin ground states.

en: High-mobility semiconducting polymers with different spin ground states

high-mobility-polymers

display_name: high-mobility-polymers

high-spin-materials

display_name: high-spin-materials

organic-semiconductor

display_name: organic-semiconductor


materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).


en:
Despite the high promise of Li-metal-based batteries, its commercialization has been hampered due to the formation of dendrites that lead to mechanical instability,
energy loss and eventual internal short circuits. The provided dataset consists of the phase-field simulation results for investigating the effect of applied overpotential and operating temperature on dendrite growth. These data are used for elucidating the correlation of overpotential and temperature with the surface modulation during electrodeposition. The simulation cell consists of a Li metal anode and 1M LiPF6 in EC:DMC (1:1), and the electrodeposition process was simulated under the applied overpotential ranging from 0.30 V to 0.44 V with a 0.02 V increment at temperatures from 268 K and 333 K with a 5 K increment. The data contains order parameter, chemical potential and overpotential. The supplied Python script can compute the surface tortuosity, average and maximum Li heights and dendrite height at a given temperature, overpotential and time step.

en: Phase-field investigation of lithium electrodeposition under different applied overpotentials and operating temperatures

anode

display_name: anode

big-map

display_name: big-map

dendrites

display_name: dendrites

interfaces

display_name: interfaces

lithium

display_name: lithium

phase-field

display_name: phase-field


materialscloud

en: Materials Cloud


en:
Dynamical potentials appear in many advanced electronic-structure methods, including self-energies from many-body perturbation theory, dynamical mean-field theory, electronic-transport formulations, and many embedding approaches. Here, we propose a novel treatment for the frequency dependence, introducing an algorithmic inversion method that can be applied to dynamical potentials expanded as sum-over-poles. This approach allows for an exact solution of Dyson-like equations at all frequencies via a mapping to a matrix diagonalization, and provides simultaneously frequency-dependent (spectral) and frequency-integrated (thermodynamic) properties of the Dyson-inverted propagators.
The transformation to a sum-over-poles is performed introducing n-th order generalized Lorentzians as an improved basis set to represent the spectral function of a propagator.
Numerical results for the homogeneous electron gas at the G0W0 level are provided to argue for the accuracy and efficiency of such unified approach.
In this record, we provide the input and outputs of the AGWX suite (see article) used in the work. Also, we add the plots of the obtained Green's function for all densities, k-points, and frequencies.

computational-materials-science

display_name: computational-materials-science

marvel

display_name: marvel

max

display_name: max

snsf

display_name: snsf


homogeneous-electron-gas

display_name: homogeneous-electron-gas

many-body-perturbation-theory-gw

display_name: many-body-perturbation-theory-gw


en:
If one carries out a molecular simulation of N particles using periodic boundary conditions, linear momentum is conserved and hence the number of degrees of freedom is set to 3N-3. In most programs, this number of degrees of freedom is the default setting. However, if one carries out a molecular simulation in an external field, one needs to ensure that degrees of freedom are changed from this default setting to 3N, as in an external field the velocity of the center of mass can change. Using the correct degrees of freedom is important in calculating the temperature and in some algorithms to simulate at constant temperature.
For sufficiently large systems, the difference between 3N and 3N-3 is negligible in the way. However, there are systems in which the comparison with experimental data requires molecular dynamics simulations of a small number of particles. In this work, we illustrate the effect of an incorrect setting of degrees of freedom in molecular dynamic simulations studying the diffusion properties of guest molecules in nanoporous materials. We show that previously published results have reported a surprising diffusion dependence on the loading, which could be traced back to an incorrect setting of the degrees of freedom. As the correct settings are convoluted and counter-intuitive in some of the most commonly used molecular dynamics programs, we carried out a systematic study on the consequences of the various commonly used (incorrect) settings. Our conclusion, is that for systems smaller than 50 particles the results are most likely unreliable as these are either performed at an incorrect temperature or the temperature is incorrectly used in some of the results.
Furthermore, a novel and efficient method to calculate diffusion coefficients of guest molecules into nanoporous materials at zero loading conditions is introduced.

en: On the effects of the degrees of freedom on calculating diffusion properties in nanoporous materials

diffusion

display_name: diffusion

metal-organic-frameworks

display_name: metal-organic-frameworks

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nanoporous-materials

display_name: nanoporous-materials

prace

display_name: prace

zeolites

display_name: zeolites


en: The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the energy requirement is reduced by as much as a factor of two using the correct heat capacity.

en: A data-science approach to predict the heat capacity of nanoporous materials

envi

en: Environment

heat-capacity

display_name: heat-capacity

machine-learning

display_name: machine-learning

mof

display_name: mof

thermal-properties

display_name: thermal-properties

zeolite

display_name: zeolite


en: Li–S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulphide (LiPS) shuttling. Computational screening of LiPS adsorption on single-atom catalyst (SAC) substrates is of great aid to the design of Li–S batteries which are robust against the LiPS shuttling from the cathode to the anode and the electrolyte. To facilitate this process, we develop a machine learning (ML) protocol to accelerate the systematic mapping of dominant local energy minima found with calculations based on the density functional theory (DFT), and, in turn, fast screening of LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for LiPS adsorbed on graphene decorated with a Fe–N4–C SAC. We identify minima whose binding energies are better or on par with the one previously reported in the literature. We then move to analyse the adsorption trends on Zn–N4–C SAC and observe similar adsorption strength and behaviour with the Fe–N4–C SAC, highlighting the good predictive power of our protocol. Our approach offers a comprehensive and computationally efficient alternative to conventional approaches studying LiPS adsorption

en: Accelerating the theoretical study of Li-polysulphide adsorption on single-atom catalysts via machine learning approaches

batteries

display_name: batteries

generalised-convex-hull

display_name: generalised-convex-hull

minima-search

display_name: minima-search


en:
The interplay between electron correlation and nuclear quantum effects makes our understanding of elemental hydrogen a formidable challenge. Here, we present the phase diagram of hydrogen and deuterium at low temperatures and high-pressure (P > 300 GPa) by accounting for highly accurate electronic and nuclear enthalpies. We evaluated internal electronic energies by diffusion quantum Monte Carlo, while nuclear quantum motion and anharmonicity have been included by the stochastic self-consistent harmonic approximation. Our results show that the long-sought atomic metallic hydrogen, predicted to host room-temperature superconductivity, forms at 577±10 GPa (640±14 GPa in deuterium). Indeed, anharmonicity pushes the stability of this phase towards pressures much larger than previous theoretical estimates or attained experimental values. Before atomization, molecular hydrogen transforms from a metallic phase III to another metallic structure that is still molecular (phase VI) at 422±40 GPa (442±30 GPa in deuterium). We predict clear-cut signatures in optical spectroscopy and DC conductivity that can be used experimentally to distinguish between the two structural transitions. According to our findings, the experimental evidence of metallic hydrogen has so far been limited to molecular phases.
The data uploaded here contains the structural information of the simulated phases of hydrogen and their quantum centroid position as a function of pressure and isotope.

en: Quantum phase diagram of high-pressure hydrogen

high-pressure-hydrogen

display_name: high-pressure-hydrogen


en: Smearing techniques are widely used in first-principles calculations of metallic and magnetic materials, where they improve the accuracy of Brillouin zone sampling and lessen the impact of level-crossing instabilities. Smearing introduces a fictitious electronic temperature that smooths the discontinuities of the integrands; consequently, a corresponding fictitious entropic term arises and needs to be considered in the total free energy functional. Advanced smearing techniques – such as Methfessel-Paxton and cold smearing – have been introduced to guarantee that the system's total free energy remains independent of the smearing temperature at least up to the second order. In doing so, they give rise to non-monotonic occupation functions (and, for Methfessel-Paxton, non-positive definite), which can result in the chemical potential not being uniquely defined. We explore this shortcoming in detail and introduce a numerical protocol utilizing Newton's minimization method that is able to identify the desired Fermi energy. We validate the method by calculating the Fermi energy of ~20,000 materials and comparing it with the results of standard bisection approaches. In passing, we also highlight how traditional approaches, based on Fermi-Dirac or Gaussian smearing, are actually equivalent for all practical purposes, provided the smearing width is appropriately renormalized by a factor ~2.565. This data set contains the AiiDA databases, scripts, and other data necessary for the reproduction of our results.

en: Fermi energy determination for advanced smearing techniques

fermi-energy

display_name: fermi-energy

insulators

display_name: insulators

intersect

display_name: intersect

marvel

display_name: marvel

semiconductors

display_name: semiconductors

smearing

display_name: smearing


en: Maximally-localized Wannier functions (MLWFs) are a powerful and broadly used tool to characterize the electronic structure of materials, from chemical bonding to dielectric response to topological properties. Most generally, one can construct MLWFs that describe isolated band manifolds, e.g. for the valence bands of insulators, or entangled band manifolds, e.g. in metals or describing both the valence and the conduction manifolds in insulators. Obtaining MLWFs that describe a target manifold accurately and with the most compact representation often requires chemical intuition and trial and error, a challenging step even for experienced researchers and a roadblock for automated high-throughput calculations. Here, we present a very natural and powerful approach that provides automatically MLWFs spanning the occupied bands and their natural complement for the empty states, resulting in WÎannier Hamiltonian models that provide a tight-binding picture of optimized atomic orbitals in crystals. Key to the success of the algorithm is the introduction of a projectability measure for each Bloch state onto atomic orbitals (here, chosen from the pseudopotential projectors) that determines if that state should be kept identically, discarded, or mixed into a disentangling algorithm. We showcase the accuracy of our method by comparing a reference test set of 200 materials against the selected-columns-of-the-density-matrix (SCDM) algorithm, and its reliability by Wannierizing 21 737 materials from the Materials Cloud.

en: Projectability disentanglement for accurate and automated electronic-structure Hamiltonians

high-throughput

display_name: high-throughput

snsf

display_name: snsf

wannier-functions

display_name: wannier-functions

wannierization

display_name: wannierization


en: The microscopic mechanism of heavy band formation, relevant for unconventional superconductivity in CeCoIn₅ and other Ce-based heavy fermion materials, depends strongly on the efficiency with which f electrons are delocalized from the rare earth sites and participate in a Kondo lattice. Replacing Ce³⁺ (4f 1, J = 5/2) with Sm³⁺ (4f 5, J = 5/2), we show that a combination of crystal field and on-site Coulomb repulsion causes SmCoIn₅ to exhibit a Γ7 ground state similar to CeCoIn5 with multiple f electrons. Remarkably, we also find that with this ground state, SmCoIn₅ exhibits a temperature-induced valence crossover consistent with a Kondo scenario, leading to increased delocalization of f holes below a temperature scale set by the crystal field, Tv ≈ 60 K. Our result provides evidence that in the case of many f electrons, the crystal field remains the most important tuning knob in controlling the efficiency of delocalization near a heavy fermion quantum critical point, and additionally clarifies that charge fluctuations are present and may play a generally important role in the ground state of "115" materials.

materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).

en: Charge fluctuations in the intermediate-valence ground state of SmCoIn₅

dftu

display_name: dftu

heavy-fermions

display_name: heavy-fermions

mixed-valence

display_name: mixed-valence


materialscloud

en: Materials Cloud


en: This data set includes the workflows (all steps including input parameters and output data) for the computation of the adsorption energies for atomic oxygen on different platinum nanoparticles using density functional theory. Different number of adsorbed oxygen atoms, starting from single adsorbed oxygen atoms, and different adsorption configurations have been considered. The lowest-energy adsorption configurations have been used to compute the phase diagrams describing the thermodynamic equilibrium in the platinum-oxygen system at different temperatures and pressures. For the construction of the phase diagrams, additional calculations for platinum oxide nanoparticles and a correction to the O2 dissociation energy have been performed.

en: Platinum nanoparticles under oxidizing conditions

oxygen-adsorption

display_name: oxygen-adsorption

platinum-nanocatalyst

display_name: platinum-nanocatalyst

platinum-nanoparticle

display_name: platinum-nanoparticle

pressure-temperature-phase-diagram

display_name: pressure-temperature-phase-diagram


en: For decades transition-metal oxides have generated a huge interest due to the multitude of physical phenomena they exhibit. In this class of materials, the rare-earth nickelates, RNiO₃, stand out for their rich phase diagram stemming from complex couplings between the lattice, electronic and magnetic degrees of freedom. Here, we present a first-principles study of the low-temperature phase for two members of the RNiO₃ series, with R = Pr, Y. We employ density-functional theory with Hubbard corrections accounting not only for the on-site localizing interactions among the Ni-3d electrons (U), but also the inter-site hybridization effects between the transition-metals and the ligands (V). All the U and V parameters are calculated from first-principles using density-functional perturbation theory, resulting in a fully ab initio methodology. Our simulations show that the inclusion of the inter-site interaction parameters V is necessary to simultaneously capture the features well-established by experimental characterizations of the low-temperature state: insulating character, antiferromagnetism and bond disproportionation. On the contrary, for some magnetic orderings the inclusion of on-site interaction parameters U alone completely suppresses the breathing distortion occurring in the low-temperature phase and produces an erroneous electronic state with a vanishing band gap. In addition - only when both the U and V are considered - we predict a polar phase with a magnetization-dependent electric polarization,  supporting very recent experimental observations that suggest a possible occurrence of type-II multiferroicity for these materials.

en: Hybridization driving distortions and multiferroicity in rare-earth nickelates

electronic-structure

display_name: electronic-structure

magnetism

display_name: magnetism

rare-earth-nickelates

display_name: rare-earth-nickelates


envi

en: Environment

marvel

display_name: marvel


en: Linear scaling relations have led to an understanding of trends in catalytic activity and selectivity of many reactions in heterogeneous and electro-catalysis. Yet, linear scaling between the chemisorption energies of any two small molecule adsorbates is not guaranteed. A prominent example is the lack of scaling between the chemisorption energies of carbon and oxygen on transition metal surfaces. In this work, we show that this lack of scaling originates from different re-normalised adsorbate valence energies of lower-lying oxygen versus higher-lying carbon. We develop a model for chemisorption of small molecule adsorbates within the d-band model by combining a modified form of the Newns-Anderson hybridisation energy with an effective orthogonalization term. We develop a general descriptor to a priori determine if two adsorbates are likely to scale with each other. This record contains the AiiDA archive required to reproduce all calculations in the manuscript.

en: Limits to scaling relations between adsorption energies?

catalysis

display_name: catalysis

d-band-model

display_name: d-band-model

scaling-relations

display_name: scaling-relations


en: Conversion of graphene into pure free-standing graphane — where each C atom is sp³ bound to a hydrogen atom — has not been achieved so far, in spite of numerous experimental attempts. Here, we obtain an unprecedented level of hydrogenation (~90% of sp³ bonds) by exposing fully free-standing nano porous samples — constituted by single to few veils of smoothly rippled graphene — to atomic hydrogen in ultra-high-vacuum. Such a controlled hydrogenation of high-quality and high-specific-area samples converts the original conductive graphene into a wide gap semiconductor, with the valence band maximum (VBM) ~3.5 eV below the Fermi level, as monitored by photoemission spectro-microscopy and confirmed by theoretical predictions. In fact, the calculated band structure unequivocally identifies the achievement of a stable, double-side fully hydrogenated configuration, with no trace of pi states and a gap opening in excellent agreement with the experimental results.

en: Gap opening in double-sided highly hydrogenated free-standing graphene

big-map

display_name: big-map

density-functional-theory

display_name: density-functional-theory

first-principles

display_name: first-principles

graphane

display_name: graphane

gw-approximation

display_name: gw-approximation

hydrogenated-graphene

display_name: hydrogenated-graphene

max

display_name: max

nanoporous-graphene

display_name: nanoporous-graphene

spectromicroscopy

display_name: spectromicroscopy

yambo-code

display_name: yambo-code


en:
The creation and maintenance of crystallographic data repositories is one of the greatest data-related achievements in chemistry. Platforms such as the Cambridge Structural Database host what is likely the most diverse collection of synthesizable molecules. If properly mined, they could be the basis for the large-scale exploration of new regions of the chemical space using quantum chemistry (QC). However, it is currently challenging to retrieve all the necessary information for QC based exclusively on the available structural data, especially for transition metal complexes. To solve this shortcoming, we present  cell2mol, a software that interprets crystallographic data and retrieves the connectivity and total charge of molecules, including the oxidation state (OS) of metal atoms. We prove that  cell2mol outperforms other popular methods at assigning the metal OS, while offering a much more comprehensive interpretation of the unit cell, and we make publicly available reliable QC-ready databases totaling 31k transition metal complexes and 13k ligands, encompassing incomparable chemical diversity.

This record contains the aforementioned database of crystallographic structures after interpretation using the cell2mol software. The database spans 8 different transition metals (Fe, Mn, Ru, Re, Cr, Co, Ni, Cu; named from 1 to 8) and contains over 31000 different transition metal complexes and 13000 unique ligands, but also contains the interpreted contents of the entire unit cells in terms of discrete chemical species with well-defined charges and connectivities. Details can be found in the README.txt file and an exemplary script is provided for usage. The cell2mol code can be obtained in https://github.com/lcmd-epfl/cell2mol.

en: cell2mol: encoding chemistry to interpret crystallographic data

crystal

display_name: crystal

experimental

display_name: experimental


en: Hydrogen boride (HB) sheets are metal-free two-dimensional materials comprising boron and hydrogen in a 1:1 stoichiometric ratio. In spite of the several advancements, the fundamental interactions between HB sheets and discrete molecules remain unclear. Here, we report the adsorption of CO2 and its conversion to CH4 and C2H6 using hydrogen-deficient HB sheets. Although fresh HB sheets did not adsorb CO2, hydrogen-deficient HB sheets reproducibly physisorbed CO2 at 297 K. The adsorption followed the Langmuir model with a saturation coverage of 2.4 × 10−4 mol g−1 and a heat of adsorption of approximately 20 kJ mol−1, which was supported by density functional theory calculations. When heated in a CO2 atmosphere, hydrogen-deficient HB began reacting with CO2 at 423 K. The detection of CH4 and C2H6 as CO2 reaction products in a moist atmosphere indicated that hydrogen-deficient HB promotes C–C coupling and CO2 conversion reactions. Our findings highlight the application potential of HB sheets as catalysts for CO2 conversion.

en: Carbon dioxide adsorption and conversion to methane and ethane on hydrogen boride sheets

carbon-dioxide

display_name: carbon-dioxide

ethane

display_name: ethane

hydrogen-boride

display_name: hydrogen-boride

methane

display_name: methane

two-dimensional

display_name: two-dimensional


en: Maximally localized Wannier functions (MLWFs) are widely used to construct first-principles tight-binding models that accurately reproduce the electronic structure of materials. Recently, robust and automated approaches to generate these MLWFs have emerged, leading to natural sets of atomic-like orbitals that describe both the occupied states and the lowest lying unoccupied ones (when the latter can be meaningfully described by bonding/anti-bonding combinations of localized orbitals). For many applications, it is important to instead have MLWFs that describe only certain target manifolds separated in energy between them — the occupied states, the empty states, or certain groups of bands. Here, we start from the full set of MLWFs describing simultaneously all the target manifolds, and then mix them using a combination of parallel transport and maximal localization to construct orthogonal sets of MLWFs that fully and only span the desired target submanifolds. The algorithm is simple and robust, and it is applied to some paradigmatic but non-trivial cases (the valence and conduction bands of silicon, the top valence band of MoS₂, the 3d and t2g/eg bands of SrVO₃ and to a mid-throughput study of 77 insulators.

en: Automated mixing of maximally localized Wannier functions into target manifolds

manifold-optimization

display_name: manifold-optimization

marvel

display_name: marvel

snsf

display_name: snsf

wannier-functions

display_name: wannier-functions

wannier-interpolation

display_name: wannier-interpolation

wannierization

display_name: wannierization


materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).


en:
We develop an accurate nanoelectronic modeling approach for realistic three-dimensional topological insulator nanostructures and investigate their low-energy surface-state spectrum. Starting from the commonly considered four-band k·p bulk model Hamiltonian for the Bi₂Se₃ family of topological insulators, we derive new parameter sets for Bi₂Se₃, Bi₂Te₃ and Sb₂Te₃. We consider a fitting strategy applied to ab initio band structures around the Γ point that ensures a quantitatively accurate description of the low-energy bulk and surface states, while avoiding the appearance of unphysical low-energy states at higher momenta, something that is not guaranteed by the commonly considered perturbative approach. We analyze  the effects that arise in the low-energy spectrum of topological surface states due to band anisotropy and electron-hole asymmetry, yielding Dirac surface states that naturally localize on different side facets. In the thin-film limit, when surface states hybridize through the bulk, we resort to a thin-film model and derive thickness-dependent model parameters from ab initio calculations that show good agreement with experimentally resolved band structures, unlike the bulk model that neglects relevant many-body effects in this regime. Our versatile modeling approach offers a reliable starting point for accurate simulations of realistic topological material-based nanoelectronic devices.
This dataset contains the data used in the corresponding publication.

en: Materials Cloud

en: Low-energy modeling of three-dimensional topological insulator nanostructures

dft

display_name: dft

effective-hamiltonian

display_name: effective-hamiltonian

kp-low-energy-model

display_name: kp-low-energy-model

tight-binding

display_name: tight-binding

topological-insulator

display_name: topological-insulator


en: Charge density wave (CDW) orders in vanadium-based kagome metals have recently received tremendous attention, yet their origin remains a topic of debate. The discovery of ScV₆Sn₆, a bilayer kagome metal featuring an intriguing √3 x √3 x 3 CDW order, offers a novel platform to explore the underlying mechanism behind the unconventional CDW. Here, we combine high-resolution angle-resolved photoemission spectroscopy, Raman scattering and density functional theory to investigate the electronic structure and phonon modes of ScV₆Sn₆. We identify topologically nontrivial surface states and multiple van Hove singularities (VHSs) in the vicinity of the Fermi level, with one VHS aligning with the in-plane component of the CDW vector near the K ̅ point. Additionally, Raman measurements indicate a strong electron-phonon coupling, as evidenced by a two-phonon mode and new emergent modes. Our findings highlight the fundamental role of lattice degrees of freedom in promoting the CDW in ScV₆Sn₆.

en: Phonon promoted charge density wave in topological kagome metal ScV₆Sn₆

arpes-raman-stm-dft

display_name: arpes-raman-stm-dft

nccr-marvel

display_name: nccr-marvel


envi

en: Environment


en:
Materials that can host Majorana zero modes gained a lot of attention in recent years due to the possibility to engineer topologically protected quantum computing platforms. Promising candidates are heterostructures of topological insulators and superconductors. Here we present density-functional-theory-based calculations for Pd-doped Bi₂Te₃ and Pd(Bi,Te)x (x=1,2) in order to shed light on the superconducting properties in the self-formed superconducting phase when Pd is deposited on top of the topological insulator Bi₂Te₃.
This dataset accompanies a joint experiment/theory publication and publishes the related density functional theory calculations for:
- relaxed geometries for Pd intercalation in the Bi₂Te₃ vdW gap
- electronic structure of PdTe and PdTe₂ compared to alloy phases of Pd(Bi,Te) and Pd(Bi,Te)₂, collectively referred to as "xPBT"
- calculations for the superconducting state of xPBT phases within the Kohn-Sham Bogoliubov-de Gennes method

en: Pd-doping of Bi₂Te₃ and superconductivity of Pd(Bi,Te)x from density functional theory

density-functional-theory

display_name: density-functional-theory

majorana

display_name: majorana

superconductivity

display_name: superconductivity

topological-materials

display_name: topological-materials


en: Fracture dictates the service limits of metallic structures. Damage tolerance of materials may be characterized by fracture toughness rigorously developed from fracture mechanics, or less rigorous yet more easily obtained impact toughness (or impact energy as a variant). Given the promise of high-entropy alloys (HEAs) in structural and damage-tolerance applications, we compiled a dataset of fracture toughness and impact toughness/energy from the literature till mid-2022. The dataset is subdivided into three categories, i.e., fracture toughness, impact toughness, and impact energy, which contain 148, 14, and 78 distinct data records, respectively. On top of the alloy chemistry and measured fracture quantities, each data record also records the factors influential to fracture. Examples are material processing history, phase structure, grain size, uniaxial tensile properties such as yield strength and elongation, and testing conditions.

en: Dataset for fracture and impact toughness of high-entropy alloys

fracture-toughness

display_name: fracture-toughness

high-entropy-alloys

display_name: high-entropy-alloys

impact-energy

display_name: impact-energy

impact-toughness

display_name: impact-toughness


en: We study the water dehydrogenation reaction at the BiVO₄(010)-water interface by combining nudged-elastic-band calculations and electronic structure calculations at the hybrid functional level. We investigate the pathway and the kinetic barrier for the adiabatic reaction going from the hole polaron localized in BiVO₄ to the dehydrogenation of the adsorbed water molecule at the interface. The reaction is found to involve the H2O•⁺ radical cation as intermediate, to have a kinetic barrier of 0.7 eV, and to be initiated by the electron transfer. The calculated kinetic barrier is in good agreement with experiment and is consistent with the slow hole transfer kinetics observed at the surface of BiVO₄. To characterize the structural changes occurring during this process, we analyze the O-H distances for three relevant water molecules. We also examine the Wannier functions around the O atom of the adsorbate involved in the reaction to reveal the changes in the electronic structure during the hole hopping. The projected density of states of the lowest unoccupied molecular orbitals allows us to identify the atomic orbitals that are primarily involved in the reaction. We expect that the proposed reaction mechanism generally holds when the surface coverage is dominated by molecularly adsorbed water.

en: Oxygen evolution at the BiVO₄-water interface: mechanism of the water dehydrogenation reaction

marvel

display_name: marvel

metal-oxide

display_name: metal-oxide

water-dehydrogenation-reaction

display_name: water-dehydrogenation-reaction


en: Neural networks (NNs) often assign high confidence to their predictions, even for points far out-of-distribution, making uncertainty quantification (UQ) a challenge. When they are employed to model interatomic potentials in materials systems, this problem leads to unphysical structures that disrupt simulations, or to biased statistics and dynamics that do not reflect the true physics. Differentiable UQ techniques can find new informative data and drive active learning loops for robust potentials. However, a variety of UQ techniques, including newly developed ones, exist for atomistic simulations and there are no clear guidelines for which are most effective or suitable for a given case. In this work, we examine multiple UQ schemes for improving the robustness of NN interatomic potentials (NNIPs) through active learning. In particular, we compare incumbent ensemble-based methods against strategies that use single, deterministic NNs: mean-variance estimation, deep evidential regression, and Gaussian mixture models. We explore three datasets ranging from in-domain interpolative learning to more extrapolative out-of-domain generalization challenges: rMD17, ammonia inversion, and bulk silica glass. Performance is measured across multiple metrics relating model error to uncertainty. Our experiments show that none of the methods consistently outperformed each other across the various metrics. Ensembling remained better at generalization and for NNIP robustness;  MVE only proved effective for in-domain interpolation, while GMM was better out-of-domain; and evidential regression, despite its promise, was not the preferable alternative in any of the cases. More broadly, cost-effective, single deterministic models cannot yet consistently match or outperform ensembling for uncertainty quantification in NNIPs.

en: Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

adversarial-sampling

display_name: adversarial-sampling

ammonia

display_name: ammonia

neural-network-interatomic-potentials

display_name: neural-network-interatomic-potentials

silica-glass

display_name: silica-glass

single-deterministic-neural-networks

display_name: single-deterministic-neural-networks

uncertainty-quantification

display_name: uncertainty-quantification


en: On-surface synthesis has emerged as a powerful strategy to fabricate unprecedented forms of atomically precise graphene nanoribbons (GNRs). However, the on-surface synthesis of zigzag GNRs (ZGNR) has met with only limited success. In the paper where the data are discussed, we report the synthesis and on-surface reactions of 2,7-dibromo-9,9'-bianthryl as the precursor towards π-extended ZGNRs. Characterization by scanning tunneling microscopy and high-resolution noncontact atomic force microscopy clearly demonstrated the formation of anthracene-fused ZGNRs. Unique skeletal rearrangements were also observed, which could be explained by intramolecular Diels-Alder cycloaddition. Theoretical calculations of the electronic properties of the anthracene-fused ZGNRs revealed spin-polarized edge-states and a narrow bandgap of 0.20 eV.

en: On-surface synthesis of anthracene-fused zigzag graphene nanoribbons from 2,7-dibromo-9,9'-bianthryl reveals unexpected ring rearrangements

graphene-nanoribbons

display_name: graphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis


materialscloud

en:
Materials Cloud is a platform built to enable the seamless sharing and dissemination of resources in computational materials science, offering educational, research, and archiving tools; simulation software and services; and curated and raw data. These underpin published results and empower data-based discovery, compliant with data management plans and the FAIR principles.

In the Materials Cloud you can browse, explore, download, or deposit raw and curated data (in the Discover, Explore, and Archive sections); you can access cloud or redeployable simulation services (from AiiDAlab turnkey simulations to the Quantum Mobile virtual machine, the AiiDA registry of plugins and workflows, and the OSSCAR notebooks, in the Work section), and you can use all our educational material, tutorials and lectures (in the Learn section).


en:
Hubbard-corrected density-functional theory has proven to be successful in addressing self-interaction errors in 3D magnetic materials. However, the effectiveness of this approach for 2D magnetic materials has not been extensively explored. Here, we use PBEsol+U and its extensions PBEsol+U+V to investigate the electronic, structural, and vibrational properties of 2D antiferromagnetic FePS₃ and ferromagnetic CrI₃, and compare the monolayers with their bulk counterparts. Hubbard parameters (on-site U and inter-site V) are computed self-consistently using density-functional perturbation theory, thus avoiding any empirical assumptions. We show that for FePS₃ the Hubbard corrections are crucial in obtaining the experimentally observed insulating state with the correct crystal symmetry, providing also vibrational frequencies in good agreement with Raman experiments. While empirical U can lead to an unstable ground-state (i.e. imaginary phonons), the system remains stable through the self-consistent process of calculating Hubbard parameters.
For ferromagnetic CrI₃, we discuss how a straightforward application of Hubbard corrections worsens the results and introduces a spurious separation between spin-majority and minority conduction bands. Promoting the Hubbard U to be a spin-resolved parameter — that is, applying different (first-principles) values to the spin-up and spin-down manifolds — recovers a more physical picture of the electronic bands and delivers the best comparison with experiments.

en: On-site and inter-site Hubbard corrections in magnetic monolayers: The case of FePS₃ and CrI₃

cscs

display_name: cscs

dft

display_name: dft

dftu

display_name: dftu

dftuv

display_name: dftuv

epfl

display_name: epfl

first-principles

display_name: first-principles

hubbard

display_name: hubbard

quantum-espresso

display_name: quantum-espresso


materialscloud

en: Materials Cloud


en: Oxides containing metals or semimetals from the p-block of the periodic table, e.g., indium oxide or antimony oxide, are of interest as transparent conductors and light absorbers for solar energy conversion due to the tunability of their electronic conductivity and optical absorption. Comparatively, these oxides have found limited applications in solar-to-hydrogen photocatalysis primarily due to their high electronegativity, which impedes electron transfer for converting protons into hydrogen. We have shown recently that inserting s-block metal cations into p-block oxides is effective at lowering electronegativities while affording further control of band gaps. Here, we explain the origins of this dual tunability by demonstrating the mediator role of s-block metal cations in modulating orbital hybridization while not contributing to frontier electronic states. From this result, we carry out a comprehensive computational study of 109 ternary oxides of s- and p-block metal elements as candidate photocatalysts for solar hydrogen generation. We downselect the most desirable materials using band gaps and band edges obtained from Hubbard-corrected density-functional theory with Hubbard parameters computed entirely from first principles, evaluate the stability of these oxides in aqueous conditions, and characterize experimentally four of the remaining materials, synthesized with high phase uniformity, to critically assess the accuracy of the computational models. We thus propose nine oxide semiconductors, including CsIn₃O₅, Sr₂In₂O₅, and KSbO₂ which, to the extent of our literature review, have not been previously considered as water-splitting photocatalysts. This record contains the data for the simulations discussed in our manuscript.

en: Ternary oxides of s- and p-block metals for photocatalytic solar-to-hydrogen conversion

marvel

display_name: marvel

photocatalysis

display_name: photocatalysis


envi

en: Environment


en: We study the electronic and optical properties of the hydrogen boride sheet by using the many-body perturbation theory with the perturbative GW (G₀W₀) approximation. It was found that the hydrogen boride sheet shows a semimetallic electronic structure, supporting the previous theoretical study based on the semilocal density functional theory calculations. It was also found that the optical spectrum calculated based on the quasiparticle energies agrees well with the experiments. This work suggests that G₀W₀ approximation may be useful for predicting precise electronic and optical properties of the hydrogen boride sheet and its derivatives.

en: Electronic and optical properties of the hydrogen boride sheet from the many-body perturbation theory

density-functional-theory

display_name: density-functional-theory

hydrogen-boride

display_name: hydrogen-boride

many-body-perturbation-theory

display_name: many-body-perturbation-theory

nanosheet

display_name: nanosheet

two-dimensional

display_name: two-dimensional


en: One key bottleneck of solid-state NMR spectroscopy is that ¹H NMR spectra of organic solids are often very broad due to the presence of a strong network of dipolar couplings. We have recently suggested a new approach to tackle this problem. More specifically, we parametrically mapped errors leading to residual dipolar broadening into a second dimension and removed them in a correlation experiment. In this way pure isotropic proton (PIP) spectra were obtained that contain only isotropic shifts and provide the highest ¹H NMR resolution available today in rigid solids. Here, using a deep-learning method, we extend the PIP approach to a second dimension, and for samples of L-tyrosine hydrochloride and ampicillin we obtain high resolution ¹H-¹H double-quantum/single-quantum dipolar correlation and spin-diffusion spectra with significantly higher resolution than the corresponding spectra at 100 kHz MAS, allowing the identification of previously overlapped isotropic correlation peaks.

en: Two-dimensional pure isotropic proton solid state NMR

machine-learning

display_name: machine-learning

nmr

display_name: nmr

resolution

display_name: resolution


en: The GW approach produces highly accurate quasiparticle energies, but its application to large systems is computationally challenging, which can be largely attributed to the difficulty in computing the inverse dielectric matrix. To address this challenge, we develop a machine learning approach to efficiently predict density-density response functions (DDRF) in materials. For this, an atomic decomposition of the DDRF is introduced as well as the neighbourhood density-matrix descriptor both of which transform in the same way under rotations. The resulting DDRFs are then used to evaluate quasiparticle energies via the GW approach. This technique is called the ML-GW approach. To assess the accuracy of this method, we apply it to hydrogenated silicon clusters and find that it reliably reproduces HOMO-LUMO gaps and quasiparticle energy levels. The accuracy of the predictions deteriorates when the approach is applied to larger clusters than those included in the training set. These advances pave the way towards GW calculations of complex systems, such as disordered materials, liquids, interfaces and nanoparticles.

en: Accelerating GW calculations through machine learned dielectric matrices

ab-initio

display_name: ab-initio

dft

display_name: dft


en: Existing, classical interatomic potentials for bcc iron predict contradicting crack-tip mechanisms (i.e. cleavage, dislocation emission, phase transition) for the same crack systems, thus leaving the crack propagation mechanism in bcc iron unclear. In this work, we develop a Gaussian approximation potential (GAP) by extending a DFT database for ferromagnetic bcc iron to include highly distorted primitive bcc cells and surface separation, along with small crack-tip configurations that are identified by means of a fully automated active learning workflow. Our GAP (referred to as Fe-GAP22) predicts crack propagation within 8 meV/atom accuracy. The fully automated, active learning workflow is made publicly available on GitHub. With the newly developed Fe-GAP22, we find that in absence of other defects around the crack tip (e.g. nanovoids, dislocations), the static (T=0K) crack-tip mechanism is cleavage, thus settling the contradictions in the literature. Our work also highlights the need for multi-scale modelling to predict fracture at finite temperatures and finite strain rates.

en: Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential

bcc-iron

display_name: bcc-iron

fracture

display_name: fracture

gaussian-approximation-potential

display_name: gaussian-approximation-potential