en:
A semi-automated protocol largely based on AiiDA has been developed and exploited to explore the yet-unsolved crystal structure of the recently-discovered AsTe₃ material with potential thermoelectric applications due to its ultralow thermal conductivity. The workflow involves different steps of increasing precision and computational cost, in a sequential approach to generate, refine and evaluate model AsTe₃ structures based on supercells of elemental-Te from the stand point of As/Te chemical disorder. Our studies shed light on the structural complexity that has prevented, to this day, a direct structure determination of the recently-discovered crystalline AsTe₃ material, despite the new synthesis protocol that has led to its obtention in a pure and fully crystalline phase, by congruent crystallization from the parent AsTe₃ glass. Structural models of crystalline AsTe₃ reveal a composition based on an intergrowth of subnanometric As₂Te₃ and Te domains with potentially defected interfaces that intimately interfere with the electronic properties of this material.
This repository contains data generated in the course of the structure-construction-and-evaluation protocol for subsets of AsTe₃ structural models obtained at different steps of the workflow, with the full provenance included, in the form of an AiiDA archive. Scripts and workchains developped specifically for this study are also privided, along with the atomic structures of the most relevant AsTe₃ models identified, in common file formats for researchers not familiar with AiiDA databases.

en: Insights into the structural complexity and local disorder of crystalline AsTe₃ from semi-automated first-principles modelling

automation

display_name: automation

chemical-disorder

display_name: chemical-disorder

structural-complexity

display_name: structural-complexity

structural-models

display_name: structural-models

thermoelectrics

display_name: thermoelectrics


$oid: OBSOLETE

en: Singlet fission (SF) is a promising multiexciton-generating process. Its demanding energy splitting criterion - that the S1 energy must be at least twice that of T1 - has limited the range of materials capable of SF. We propose heteroatom oxidation as a robust strategy to achieve sufficient S1/T1 splitting, and demonstrate the potential of this approach for intramolecular SF.

en: Heteroatom oxidation controls singlet-triplet energy splitting in singlet fission building blocks

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

conjugated-polymers

display_name: conjugated-polymers

donor-acceptor-systems

display_name: donor-acceptor-systems

epfl

display_name: epfl

excited-states

display_name: excited-states

singlet-fission

display_name: singlet-fission


en: The efficient activation of methane and simultaneous water dissociation are crucial in many catalytic reactions on oxide-supported transition metal catalysts. On very low-loaded Ni/CeO₂ surfaces, methane easily fully decomposes, CH₄ -> C + 4H,  and water dissociates, H₂O -> OH + H. However, in important reactions such as the direct oxidation of methane to methanol (MTM), where complex interplay exists between reactants (CH₄, O₂), it is desirable to avoid the complete dehydrogenation of methane to carbon.  Remarkably, the barrier for the activation of C-H bonds in CHx (x= 1-3) species on Ni/CeO₂ surfaces can be manipulated by adding Cu, forming bimetallic NiCu clusters, whereas the ease for cleavage of O-H bonds in water, is not affected by ensemble effects, as obtained from density functional theory-based calculations. CH4 activation occurs only on Ni sites and H₂O activation on both Ni and Cu sites. The MTM reaction pathway for the example of the Ni₃Cu₁/CeO₂ model catalyst predict higher selectivity and a lower activation barrier for methanol production, compared with that for Ni₄-CeO₂. These findings point toward a possible strategy to design active and stable catalysts which can be employed for methane activation and conversions.

en: Tuning selectivity in the direct conversion of methane to methanol: bimetallic synergistic effects on the cleavage of C-H and O-H bonds over NiCu/CeO₂ catalysts

bimetallic

display_name: bimetallic

ceria

display_name: ceria

methane-activation

display_name: methane-activation

water-activation

display_name: water-activation


en:
Why are materials with specific characteristics more abundant than others?
This is a fundamental question in materials science and one that is traditionally difficult to tackle, given the vastness of compositional and configurational space. We highlight here the anomalous abundance of  inorganic compounds whose primitive unit cell contains a number of atoms that is a multiple of four. This occurrence - named here the 'rule of four' - has to our knowledge not previously been reported or studied. Here, we first highlight the rule's existence, especially notable when restricting oneself to experimentally known compounds, and explore its possible relationship with established descriptors of  crystal structures, from symmetries to energies. We then investigate this relative abundance by looking at structural descriptors, both of global (packing configurations) and local (the smooth overlap of atomic positions) nature. Contrary to intuition, the overabundance does not correlate with low-energy or high-symmetry structures; in fact, structures which obey the 'rule of four' are characterized by low symmetries and loosely packed arrangements maximizing the free volume. We are able to correlate this abundance with local structural symmetries, and visualize the results using a hybrid supervised-unsupervised machine learning method.

en: The rule of four: anomalous stoichiometries of inorganic compounds

classification

display_name: classification

inorganic-databases

display_name: inorganic-databases

local-symmetries

display_name: local-symmetries

machine-learning

display_name: machine-learning

marvel

display_name: marvel

soap-vectors

display_name: soap-vectors


en: We present a self-consistent method based on first-principles calculations to determine the magnetic ground state of materials, regardless of their dimensionality. Our methodology is founded on satisfying the stability conditions derived from the linear spin wave theory (LSWT) by optimizing the magnetic structure iteratively. We demonstrate the effectiveness of our method by successfully predicting the experimental magnetic structures of NiO, FePS₃, FeP, MnF₂, FeCl₂, and CuO. In each case, we compared our results with available experimental data and existing theoretical calculations reported in the literature. Finally, we discuss the validity of the method and the possible extensions.

en: Systematic determination of a material's magnetic ground state from first principles

dftu

display_name: dftu

linear-spin-wave-theory

display_name: linear-spin-wave-theory

magnetic-structure

display_name: magnetic-structure


en: It is well known that interfaces in nanomaterials can act as ultra-fast short-circuit diffusion paths, as originating from local structural, chemical and/or electronic modifications at the interface. For example, the interface diffusivity of Cu in Cu/AlN nanomultilayers can be up to two orders of magnitude higher as compared to the bulk, which may promote interfacial premelting of Cu. Extensive ab initio calculations of vacancy formation and migration energies in Cu/AlN nanomultilayers were performed to arrive at the fundamental understanding of such anomalously fast interface diffusion phenomena. It was found that both the metallic Al-terminated interface and the mixed-bonded N-terminated interface promote high atomic interface mobilities by lowering the vacancy formation and vacancy migration energies in the interfacial Cu planes. Moreover, the out-of-plane vacancy migration energies highlights a strong tendency of vacancy segregation toward both interfaces.

en: Anomalously low vacancy formation energies and migration barriers at Cu/AlN interfaces from ab initio calculations

ab-initio

display_name: ab-initio

metal-ceramic-interfaces

display_name: metal-ceramic-interfaces

nanomultilayers

display_name: nanomultilayers

vacancy-driven-diffusion

display_name: vacancy-driven-diffusion


en: Using first-principles techniques, we study the structural, magnetic, and electronic properties of (111)-oriented (LaMnO₃)₂ₙ|(SrMnO₃)ₙ superlattices of varying thickness (n=2,4,6). We find that the properties of the thinnest superlattice (n=2) are similar to the celebrated half-metallic ferromagnetic alloy La2/3Sr1/3MnO₃, with quenched Jahn-Teller distortions. At intermediate thickness (n=4), the a⁻a⁻a⁻ tilting pattern transitions to the a⁻a⁻c⁺ tilting pattern, driven by the lattice degrees of freedom in the LaMnO₃ region. The emergence of the Jahn-Teller modes and the spatial extent needed for their development play a key role in this structural transition. For the largest thickness considered (n=6), we unveil an emergent separation of Jahn-Teller and volume-breathing orders in the ground-state structure with the a⁻a⁻c⁺ tilting pattern, whereas it vanishes in the antiferromagnetic configurations. The ground state of all superlattices is half-metallic ferromagnetic, not affected by the underlying series of structural transitions. Overall, these results outline a thickness-induced crossover between the physical properties of bulk La2/3Sr1/3MnO₃ and bulk LaMnO₃. The dataset includes the fully relaxed atomic positions of the most favorable tilting patterns, as well as the main output files of the electronic structure calculations. Selected files for the density of states are also included.

en: Emergent half-metal with mixed structural order in (111)-oriented (LaMnO₃)₂ₙ|(SrMnO₃)ₙ superlattices

dft

display_name: dft

electronic-properties

display_name: electronic-properties

magnetic-properties

display_name: magnetic-properties

oxide-heterostructures

display_name: oxide-heterostructures


en: This database contains phosphorus and sulfur K-edge X-ray absorption near-edge structure (XANES) of delithiated Lithium-Phosphorus-Sulfur compounds. The structures were generated by a computational delithiation procedure of β-Li3PS4 with density functional theory calculations, where we systematically enumerated distinct lithium/vacancy orderings in the super cells with the general composition Li12-xP4S16 (i.e., four Li3PS4 formula units and x from 0 to 12). The database contains a total of 2227 P K-edge and 8885 S K-edge XANES spectra of symmetrically inequivalent absorbing P and S sites. The XANES spectra were calculated using the excited electron and core hole method as implemented in The Vienna Ab initio Simulation Package (VASP) 6.2.1. Details of the structure generation procedure as well as the VASP simulations can be found in the associated manuscript (see reference below).

en: Atomic insights into the oxidative degradation mechanisms of sulfide solid electrolytes

lithium-ion-batteries

display_name: lithium-ion-batteries

lithium-phosphorus-sulfide

display_name: lithium-phosphorus-sulfide

solid-electrolyte

display_name: solid-electrolyte

vasp

display_name: vasp

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy


en:
Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by weak binding energy and, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors.
To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.

en: Machine-learning accelerated identification of exfoliable two-dimensional materials

binding-energy

display_name: binding-energy

crystal-structure

display_name: crystal-structure

exfoliation

display_name: exfoliation

online-tool

display_name: online-tool

two-dimensional

display_name: two-dimensional


en: Ab initio random structure searching (AIRSS) has been performed for carbon at 10GPa, a pressure at which diamond is expected to be the thermodynamic ground state. A small (~10K structures) and larger (~100K structures) set are provided. To demonstrate a search in a ternary composition space ~100K AIRSS structures for the C+N+H+O system are provided. All structures are generated at random using the buildcell tool of the AIRSS suite, and relaxed to a nearby local minimum in the DFT-PBE total energy using the CASTEP code. Computational details are provided in the REM block of the .res structure files.

en: AIRSS data for carbon at 10GPa and the C+N+H+O system at 1GPa

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).

ab-initio-random-structure-searching

display_name: ab-initio-random-structure-searching

carbon

display_name: carbon


en: As exemplified by the growing interest in the quantum anomalous Hall effect, the research on topology as an organizing principle of quantum matter is greatly enriched from the interplay with magnetism. In this vein, we present a combined electrical and thermoelectrical transport study on the magnetic Weyl semimetal EuCd₂As₂. Unconventional contribution to the anomalous Hall and anomalous Nernst effects were observed both above and below the magnetic transition temperature of EuCd₂As₂, indicating the existence of significant Berry curvature. EuCd₂As₂ represents a rare case in which this unconventional transverse transport emerges both above and below the magnetic transition temperature in the same material. The transport properties evolve with temperature and field in the antiferromagnetic phase in a different manner than in the paramagnetic phase, suggesting different mechanisms to their origin. Our results indicate EuCd₂As₂ is a fertile playground for investigating the interplay between magnetism and topology, and potentially a plethora of topologically nontrivial phases rooted in this interplay. This record contains the raw data for this manuscript [Physical Review Letters 126, 076602 (2021)].

en: Unconventional transverse transport above and below the magnetic transition temperature in Weyl semimetal EuCd₂As₂

materialscloud

en: Materials Cloud

envi

en: Environment

anomalous-hall-effect

display_name: anomalous-hall-effect

anomalous-nernst-effect

display_name: anomalous-nernst-effect

experimental

display_name: experimental

magnetic-weyl-semimetal

display_name: magnetic-weyl-semimetal

topological-spin-texture

display_name: topological-spin-texture


en:
In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemistry to materials science. While most current MLPs are based on environment-dependent atomic energies, the limitations of this locality approximation can be overcome, e.g., in fourth-generation MLPs, which incorporate long-range electrostatic interactions based on an equilibrated global charge distribution. Apart from the considered interactions,  the quality of MLPs crucially depends on the information available about the system, i.e., the descriptors.
In this work we show that including — in addition to structural information — the electrostatic potential arising from the charge distribution in the atomic environments significantly improves the quality and transferability of the potentials. Moreover, the extended descriptor allows to overcome current limitations of two- and three-body based feature vectors regarding artificially degenerate atomic environments.
The capabilities of such an electrostatically embedded fourth-generation high-dimensional neural network potential (ee4G-HDNNP), which is further augmented by pairwise interactions, are demonstrated for NaCl as a benchmark system. Employing a data set containing only neutral and negatively charged NaCl clusters, even small energy differences between different cluster geometries can be resolved, and the potential shows an impressive transferability to positively charged clusters as well as the melt.

en: Accurate fourth-generation machine learning potentials by electrostatic embedding

dft

display_name: dft

electrostatics

display_name: electrostatics

machine-learning-potentials

display_name: machine-learning-potentials


en: We use a data-driven approach to study the magnetic and thermodynamic properties of van der Waals (vdW) layered materials. We investigate monolayers of the form A2B2X6, based on the known material Cr2Ge2Te6, using density functional theory (DFT) calculations and determine their magnetic properties, such as magnetic order and magnetic moment. We also examine formation energies and use them as a proxy for chemical stability.

en: Data-driven studies of magnetic two-dimensional materials

machine-learning

display_name: machine-learning

magnetic-materials

display_name: magnetic-materials

two-dimensional-materials

display_name: two-dimensional-materials


en: The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system's modes that most slowly approach equilibrium. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on-the-fly probability enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a mini-protein and the study of materials crystallization.

en: Deep learning the slow modes for rare events sampling

collective-variables

display_name: collective-variables

enhanced-sampling

display_name: enhanced-sampling

molecular-dynamics

display_name: molecular-dynamics

protein-folding

display_name: protein-folding

silicon-crystallization

display_name: silicon-crystallization


en: Being Wannierizable is not the end of the story for topological insulators. We introduce a family of topological insulators that would be considered trivial in the paradigm set by the tenfold way, topological quantum chemistry, and the method of symmetry-based indicators. Despite having a symmetric, exponentially localized Wannier representation, each Wannier function cannot be completely localized to a single primitive unit cell in the bulk. Such multicellular topology is shown to be neither stable nor fragile, but delicate; i.e., the topology can be nullified by adding trivial bands to either valence or conduction band.

en: Multicellularity of delicate topological insulators

delicate-topology

display_name: delicate-topology

erc

display_name: erc

hopf-insulator

display_name: hopf-insulator

multicellular-wannier-functions

display_name: multicellular-wannier-functions

returning-thouless-pump

display_name: returning-thouless-pump

snsf

display_name: snsf

topological-insulators

display_name: topological-insulators


en:
Despite their incredible performance, it is well reported that deep neural networks tend to be overoptimistic about their prediction confidence. Finding effective and efficient calibration methods for neural networks is therefore an important endeavour towards better uncertainty quantification in deep learning. In this manuscript, we introduce a novel calibration technique named expectation consistency (EC), consisting of a post-training rescaling of the last layer weights by enforcing that the average validation confidence coincides with the average proportion of correct labels. First, we show that the EC method achieves similar calibration performance to temperature scaling (TS) across different neural network architectures and data sets, all while requiring similar validation samples and computational resources. However, we argue that EC provides a principled method grounded on a Bayesian optimality principle known as the Nishimori identity. Next, we provide an asymptotic characterization of both TS and EC in a synthetic setting and show that their performance crucially depends on the target function. In particular, we discuss examples where EC significantly outperforms TS.
This record provides the code for the paper "Expectation consistency for calibration of neural networks".

en: Expectation consistency for calibration of neural networks (code)

neural-networks

display_name: neural-networks

temperature-scaling

display_name: temperature-scaling

uncertainty-quantification

display_name: uncertainty-quantification


en: Photochemical reactions of contaminants in snow and ice can be important sources and sinks for various organic and inorganic compounds. Snow contaminants can be found in the bulk ice matrix, in internal liquid-like regions (LLRs), or in quasi-liquid layers (QLLs) at the air-ice interface, where they can readily exchange with the firn air. Some studies have reported that direct photochemical reactions occur faster in LLRs and QLLs than in aqueous solution, while others have found similar rates. Here, we measure the photodegradation rate constants of the three dimethoxybenzene isomers under varying experimental conditions, including in aqueous solution, in LLRs, and at the air-ice interface of nature-identical snow. Relative to aqueous solution, we find modest photodegradation enhancements (3- and 6-fold) in LLRs for two of the isomers, and larger enhancements (15- to 30-fold) at the air-ice interface for all three isomers. We use computational modeling to assess the impact of light absorbance changes on photodegradation rate enhancements at the interface. We find small (2–5 nm) bathochromic (red) absorbance shifts at the interface relative to in solution, which increases light absorption, but this factor only accounts for less than 50 % of the measured rate constant enhancements. The major factor responsible for photodegradation rate enhancements at the air-ice interface appears to be more efficient photodecay: estimated dimethoxybenzene quantum yields are 6- to 24-fold larger at the interface compared to in aqueous solution and account for the majority (51–96 %) of the observed enhancements. Using a hypothetical model compound with an assumed Gaussian-shaped absorbance peak, we find that a shift in the peak to higher or lower wavelengths can have a minor to substantial impact on photodecay rate constants, depending on the original location of the peak and the magnitude of the shift. Changes in other peak properties at the air-ice interface, such as peak width and height (i.e., molar absorptivity) can also impact rates of light absorption and direct photodecay.

en: Enhanced photodegradation of dimethoxybenzene isomers in/on ice compared to in aqueous solution

absorption-spectra

display_name: absorption-spectra

dimethoxybenzene

display_name: dimethoxybenzene

ice

display_name: ice


en: A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic and non-invasive way is to incorporate a photoresponsive unit into a catalytically competent molecule. However, the description of the photoinduced conformational or structural changes that alter the catalyst's intrinsic reactivity is often reduced to a handful of intuitive static representations, which can struggle to capture the complexity of flexible organocatalysts. Here, we show how a comprehensive exploration of the free energy landscape of N-alkylated azobenzene-tethered piperidine catalysts is essential to unravel the conformational characteristics of each configurational state and explain the experimentally observed reactivity trends. Mapping the catalysts' conformational space highlights the existence of false ON or OFF states that lower their switching ability. Our findings expose the challenges associated with the realisation of reversible steric shielding for the photocontrol of Brønsted basicity of piperidine photoswitchable organocatalysts.

en: How robust is the reversible steric shielding strategy for photoswitchable organocatalysts?

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).

epfl

display_name: epfl

photoswitchable-organocatalysis

display_name: photoswitchable-organocatalysis

reversible-steric-shielding

display_name: reversible-steric-shielding

statistical-sampling

display_name: statistical-sampling


en: Two-dimensional (2D) materials have emerged as promising candidates for next-generation electronic and optoelectronic applications. Yet, only a few dozens of 2D materials have been successfully synthesized or exfoliated. Here, we search for novel 2D materials that can be easily exfoliated from their parent compounds. Starting from 108423 unique, experimentally known three-dimensional compounds we identify a subset of 5619 that appear layered according to robust geometric and bonding criteria. High-throughput calculations using van-der-Waals density-functional theory, validated against experimental structural data and calculated random-phase-approximation binding energies, allow to identify 1825 compounds that are either easily or potentially exfoliable. In particular, the subset of 1036 easily exfoliable cases provides novel structural prototypes and simple ternary compounds as well as a large portfolio of materials to search from for optimal properties. For a subset of 258 compounds we explore vibrational, electronic, magnetic, and topological properties, identifying 56 ferromagnetic and antiferromagnetic systems, including half-metals and half-semiconductors. This archive entry contains the database of 2D materials (structural parameters, band structures, binding energies, phonons for the subset of the 258 easily exfoliable materials with less than 6 atoms, structures and binding energies for the remaining 1567 materials) together with the provenance of all data and calculations as stored by AiiDA.

en: Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

materialscloud

en: Materials Cloud

database

display_name: database

dft

display_name: dft

electronic-bands

display_name: electronic-bands

electronic-properties

display_name: electronic-properties

high-throughput

display_name: high-throughput

magnetic-properties

display_name: magnetic-properties

marvel

display_name: marvel

monolayer

display_name: monolayer

phonons

display_name: phonons

two-dimensional

display_name: two-dimensional

vibrational-properties

display_name: vibrational-properties


en: The ethanol surface reaction over CeO₂ nanooctahedra (NO) and nanocubes (NC), which mainly expose (111) and (100) surfaces, respectively, was studied by means of infrared spectroscopy (TPSR-IR), mass spectrometry (TPSR-MS) and density functional theory (DFT) calculations. TPSR-MS results show that the production of H₂ is 2.4 times higher on CeO₂ -NC than on -NO, which is rationalized starting from the different types of adsorbed ethoxy species controlled by the shape of the ceria particles. Over the CeO₂(111) surface, monodentate type I and II ethoxy species with the alkyl chain perpendicular or parallel to the surface, respectively, were identified. Whereas on the CeO₂(100) surface, bidentate and monodentate type III ethoxy species on the checkerboard O-terminated and on a pyramid of the reconstructed (100) surface, respectively, are found. The more labile surface ethoxy species on each ceria nanoshape, which are the monodentate type I or III ethoxy on CeO₂ -NO and -NC, respectively, react on the surface to give acetate species that decomposes to CO₂ and CH₄, while H₂ is formed via the recombination of hydroxyl species. In addition, the more stable monodentate type II and bidentate ethoxy species on CeO₂ -NO and -NC, respectively, give an ethylenedioxy intermediate the binding of which is facet dependent. On the (111) facet, the less strongly bound ethylenedioxy desorbs as ethylene, whereas on the (100) facet, the more strongly bound intermediate also produces CO₂ and H₂ via formate species. Thus, on the (100) facet an additional pathway towards H₂ formation is found. ESR activity measurements show an enhanced H₂ production on the nanocubes.

en: Shape-controlled pathways in the hydrogen production from ethanol steam reforming over ceria nanoparticles

envi

en: Environment

ceria-nanostructures

display_name: ceria-nanostructures

ethanol-decomposition

display_name: ethanol-decomposition

ethylenedioxy-species

display_name: ethylenedioxy-species

facet-dependent-activity

display_name: facet-dependent-activity

green-hydrogen

display_name: green-hydrogen


en: This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

en: E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

density-functional-theory

display_name: density-functional-theory

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en: Topological crystalline insulators (TCIs) are insulating electronic states with nontrivial topology protected by crystalline symmetries. Recently, theory has proposed new classes of TCIs protected by rotation symmetries Ĉ_n, which have surface rotation anomaly evading the fermion doubling theorem, i.e., n instead of 2n Dirac cones on the surface preserving the rotation symmetry. Here, we report the first realization of the Ĉ_2 rotation anomaly in a binary compound SrPb. Our first-principles calculations reveal two massless Dirac fermions protected by the combination of time-reversal symmetry T̂ and Ĉ_2y on the (010) surface. Using angle-resolved photoemission spectroscopy, we identify two Dirac surface states inside the bulk band gap of SrPb, confirming the Ĉ_2 rotation anomaly in the new classes of TCIs. The findings enrich the classification of topological phases, which pave the way for exploring exotic behavior of the new classes of TCIs.

en: Discovery of Ĉ₂ rotation anomaly in topological crystalline insulator SrPb

electronic-structure

display_name: electronic-structure

snsf

display_name: snsf

topological-materials

display_name: topological-materials


en:
The application of machine learning to the modeling of materials and molecules has proven to be extremely successful in accelerating the understanding, design, and characterization of materials. A major factor in this success has been the development of representations of atomic structures that reflect physics-based symmetries of the underlying interactions. Most of the descriptions of atomic properties or even global observables rely on decompositions into atomic contributions that are subsequently learnt in an atom-centered framework. However, many quantities associated with quantum mechanical calculations, such as the single-particle Hamiltonian matrices written in an atomic-orbital basis, are associated with multiple atom-centers.
Following the introduction of equivariant N-center structural descriptors, in the reference below, that generalize the very successful atom-centered density correlation features to the problem of learning properties indexed by N atoms, we present benchmarks showing how the construction can be applied to efficiently learn the matrix elements of the (effective) single-particle Hamiltonian in an atom-centered orbital basis.
In this record, we include the dataset comprising the Fock and overlap matrices in the def2-SVP of 1000 distorted water molecules, up to 4500 ethanol structures, and a subset of QM7-CHNO molecules. We also provide scripts to generate the two-center representations and fit linear and sparse kernel models for the Hamiltonians.

en: Equivariant representations for molecular Hamiltonians

epfl

display_name: epfl

equivariant-representations

display_name: equivariant-representations

erc

display_name: erc

hamiltonian-learning

display_name: hamiltonian-learning

machine-learning-hamiltonians

display_name: machine-learning-hamiltonians

n-center-representations

display_name: n-center-representations


en: High Entropy Alloys (HEAs) are a new broad class of near-random solid solution alloys that can possess some impressive mechanical and physical properties including high stability against grain growth (i.e. low grain boundary (GB) mobility). Here, it is shown that an initially flat GB in an HEA can become spontaneously rough, driven by natural local compositional fluctuations. Roughening lowers the total GB energy and thus can inhibit migration. A parameter-free theoretical framework is developed to demonstrate the energetics and size scales of the roughening in terms of solute/GB interaction energies and GB disconnection energies. Above a critical level of solute/GB interactions, a planar GB is predicted to roughen down to the scale of the GB periodic unit. A similar theory for 1D GBs (minimum periodic length in one direction) is also developed since such geometries are common in atomistic simulations. Specific predictions are made for the symmetric tilt boundaries Σ17 [100] (530) and Σ5 [100] (310) in a model CoCuFeNi alloy and atomistic simulations demonstrate roughening consistent with the theory. Analysis of the stresses needed to drive migration shows how migration can be inhibited or enhanced, rationalizing variations in mobility of GBs in HEAs.

en: Theory of spontaneous grain boundary roughening in high entropy alloys

grain-boundaries

display_name: grain-boundaries

high-entropy-alloys

display_name: high-entropy-alloys

pinning

display_name: pinning

spontaneous-roughening

display_name: spontaneous-roughening


en: In this record we provide the data to support our recent finding on surface catalyzed cycloaromatization. Immobilization of organic building blocks on metal surfaces and their coupling via thermally induced C-C bond formations are developing as an important addition to the toolbox of organic and polymer synthesis. Additional advantages of this technique are the in situ monitoring of the reaction by scanning probe methods and the accessibility of insoluble and reactive carbon nanostructures. The diversity of conceivable products, however, sensitively depends on the number of available on-surface reactions. In the manuscript where the results are discussed, we introduce an unprecedented example, the intermolecular oxidative coupling of isopropyl substituents of arenes. With a new phenylene ring being formed, this [3+3] dimerization can be regarded as a formal cycloaromatization. The synthetic value of this novel reaction is proven by the synthesis of polyarylenes and co-polyarylenes, which we demonstrate by synthesizing poly(2,7-pyrenylene-1,4-phenylene). Scanning tunneling microscopy and non-contact atomic force microscopy studies complemented by density functional theory calculations are employed to obtain mechanistic insights into the title reaction.

en: On-surface polyarylene synthesis by cycloaromatization of isopropyl substituents

ab-initio

display_name: ab-initio

catalysis

display_name: catalysis

on-surface-synthesis

display_name: on-surface-synthesis


en: The screening arising from many-body excitations is a crucial quantity for describing ab-sorption and inelastic X-ray scattering (IXS) of materials. Similarly, the electron screening plays a critical role in state-of-the-art approaches for determining the fundamental band gap. However, ab initio studies of the screening in liquid water have remained limited. Here, we use a combined analysis based on the Bethe-Salpeter equation and time-dependent density functional theory. We first show that absorption spectra at near-edge energies are insufficient to assess the accuracy by which the screening is described. Next, when the energy range under scrutiny is extended, we instead find that the IXS spectra are highly sensitive and allow for the selection of the optimal theoretical scheme. This leads to good agreement with experiment over a large range of transferred energies and momenta, and enables establishing the elusive fundamental band gap of liquid water at 9.3 eV.

en: Many-body screening effects in liquid water

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).

dft

display_name: dft

mbpt

display_name: mbpt

water

display_name: water


en: The facet-dependent adsorption of CO on oxidized and reduced CeO₂ single crystal surfaces is reviewed, with emphasis on the effect of CO coverage and the ability of state-of-the-art quantum-mechanical methods to provide reliable energies and an accurate description of the IR vibrational frequency of CO. Comparison with detailed, high-resolution experimental IRRAS data performed on single crystal samples allows the assignment of the different CO vibrational bands observed on all three low-index ceria surfaces. Good agreement is achieved with the hybrid DFT approach with the HSE06 functional and with saturation coverage. It is shown that CO is very sensitive to the structure of cerium oxide surfaces and to the presence of oxygen vacancies. The combined theoretical-experimental approach offers new opportunities for a better characterization of ceria nanoparticles and for unraveling changes occurring during reactions involving CO at higher pressures.

en: Vibrational frequencies of CO bound to all three low-index cerium oxide surfaces: a consistent theoretical description of vacancy-induced changes using density functional theory

materialscloud

en: Materials Cloud

co-vibrational-frequency

display_name: co-vibrational-frequency

oxidized-and-reduced-ceria

display_name: oxidized-and-reduced-ceria


en:
Despite governing heat management in any realistic device, the microscopic mechanisms of heat transport in all-solid-state electrolytes are poorly known: existing calculations, all based on simplistic semi-empirical models, are unreliable for superionic conductors and largely overestimate their thermal conductivity. In this work, we deploy a combination of state-of-the-art methods to calculate the thermal conductivity of a prototypical Li-ion conductor, the Li₃ClO antiperovskite. By leveraging ab initio, machine learning, and forcefield descriptions of interatomic forces, we are able to reveal the massive role of anharmonic interactions and diffusive defects on the thermal conductivity and its temperature dependence, and to eventually embed their effects into a simple rationale which is likely applicable to a wide class of ionic conductors.
In this record, we provide data and scripts to generate the plots supporting our findings. We also provide the machine learning model and the dataset to train it.

en: Temperature- and vacancy-concentration-dependence of heat transport in Li₃ClO from multi-method numerical simulations

envi

en: Environment

batteries

display_name: batteries

heat-transport

display_name: heat-transport

max

display_name: max

snsf

display_name: snsf

solid-state-electrolytes

display_name: solid-state-electrolytes

superionics

display_name: superionics


en: Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work, we present the simplex method that can perform the inverse operation, i.e., calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large dataset of fingerprints, we can find a particular largest simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can be used in a fully automatic way to analyze structures. In this way, we can, for instance, detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting fingerprints on the largest simplex, we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced.

en: Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression

atomic-environment-selection

display_name: atomic-environment-selection

atomic-fingerprints

display_name: atomic-fingerprints

classification-of-atomic-environments

display_name: classification-of-atomic-environments

dimensionality-reduction

display_name: dimensionality-reduction

largest-volume-simplex

display_name: largest-volume-simplex

machine-learning

display_name: machine-learning

overlap-matrix-fingerprint

display_name: overlap-matrix-fingerprint

quantum-non-local-effects

display_name: quantum-non-local-effects


en:
The uploaded archive provides a ML-ready data set extracted from the juHemd database (see references) augmented with supplemental data for atomic descriptors. Descriptors provided in this data set include structural, magnetic, atomic quantities as well as derived (summed) quantities. In total, 118 possible descriptors are included of which 12 are DFT generated. For each simulation type (LDA/GGA) there is also a data set cleaned from DFT data available.

After data cleaning and preprocessing we extracted 387 LDA calculated magnetic Heusler structures as well as 408 GGA structures which have a full structural and magnetic data set. As we only aim at magnetic compounds, we chose to filter out compounds from the original JuHemd which have at least 0.1 Bohr magneton as total absolute magnetic moment. For each data file there is an existing descriptor file naming all the descriptors included in the data set.

en: ML-ready Curie temperatures and descriptors extracted from the JuHemd database

critical-temperature

display_name: critical-temperature

curie-temperature

display_name: curie-temperature

density-functional-theory

display_name: density-functional-theory

electronic-structure

display_name: electronic-structure

heusler-alloy

display_name: heusler-alloy

judft

display_name: judft

kkr

display_name: kkr

magnetic

display_name: magnetic

magnetic-phase-transition

display_name: magnetic-phase-transition

magnetic-structure

display_name: magnetic-structure

monte-carlo

display_name: monte-carlo


en: CeO₂ greatly enhances the electrocatalytic oxygen evolution reaction (OER) activity of CoOx, though the enhancement mechanism beyond this synergy is yet to be understood. Here, operando hard X-ray adsorption spectroscopy (hXAS) is applied to monitor the Co K edge and Ce L₃ edge in CoOx/CeO₂ to shed light on the evolution of the Co and Ce oxidation states during OER. In addition, ex situ soft XAS (sXAS) characterizations provide information on the irreversible surface-specific transformations of the Co L₃ edge as well as of the O K edge. Combining the operando and ex situ spectroscopic characterizations with comprehensive electrochemical analyses, we confirm CeO₂ is not the active center for the OER. However, coupling CeO₂ with CoOx introduces significant modifications in the Co and O species at the CoOx surface and alters the flat band potential (Efb), leading to more favorable Co oxidation state transformations during OER and possibly modifying the preferential reaction pathway. This work establishes the connections between electronic structures, Co oxidation state and the OER reaction mechanism for CoOx/CeO₂ composites electrodes.

en: Operando tracking the interactions between CoOx and CeO₂ during oxygen evolution reaction

co-oxidation

display_name: co-oxidation

co-redox

display_name: co-redox

operando-xas

display_name: operando-xas

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en: We demonstrate the importance of addressing the 𝚪 vertex and thus going beyond the GW approximation for achieving the energy levels of liquid water in many- body perturbation theory. In particular, we consider an effective vertex function in both the polarizability and the self-energy, which does not produce any computational overhead compared with the GW approximation. We yield the band gap, the ionization potential, and the electron affinity in good agreement with experiment and with a hybrid functional description. The achieved electronic structure and dielectric screening further lead to a good description of the optical absorption spectrum, as obtained through the solution of the Bethe–Salpeter equation. In particular, the experimental peak position of the exciton is accurately reproduced.

en: Absolute energy levels of liquid water from many-body perturbation theory with effective vertex corrections

gw

display_name: gw

ionization-potential

display_name: ionization-potential

many-body-perturbation-theory

display_name: many-body-perturbation-theory

vertex

display_name: vertex

water

display_name: water


en: Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often non-transferable and cannot be applied across different reactions. To overcome this drawback, we introduce a general featurization strategy for bidentate ligands that is coupled with an automated feature selection pipeline and Bayesian ridge regression to perform multivariate linear regression modeling. This approach, which is applicable to any reaction, incorporates electronic, steric, and topological features (rigidity/flexibility, branching, geometry, constitution) and is well-suited for early-stage ligand optimization. Using only a limited number of points per dataset, our workflow capably predicts the enantioselectivity of four metal-catalyzed asymmetric reactions. Uncertainty estimates provided by Bayesian ridge regression permit the use of Bayesian optimization to efficiently explore pools of prospective new ligands. Using this procedure, a new library of 312 chiral bidentate ligands was screened to identify promising ligand candidates for a challenging asymmetric oxy-alkynylation reaction.

en: Reaction-agnostic featurization of bidentate ligands for Bayesian ridge regression of enantioselectivity

bidentate-ligands

display_name: bidentate-ligands

catalysis

display_name: catalysis

epfl

display_name: epfl

homogenous-catalysis

display_name: homogenous-catalysis

ligands

display_name: ligands

nccr-catalysis

display_name: nccr-catalysis


en: In this study, we benchmarked various interatomic potentials and force fields in comparison to an ab initio dataset for bulk amorphous alumina. We investigated a comprehensive set of fixed-charge and variable-charge potentials tailored for alumina. We also train a machine learning interatomic potential, using the NequIP framework. Results highlight that the fixed-charge potential by Matsui provides an ideal blend of computational speed and alignment with ab initio findings for stoichiometric alumina. For non-stoichiometric variants, the variable charge potentials, especially ReaxFF, align remarkably well with DFT outcomes. The NequIP ML potential, while superior in some instances and adaptable, might not be the best fit for specific tasks.

en:
Do we really need machine learning interatomic potentials for modeling amorphous metal oxides?
Case study on amorphous alumina by recycling an existing ab-initio database.

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).

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics


en: We present ab initio calculations of hidden magnetoelectric multipolar order in Cr₂O₃ and its iron-based analogue, α-Fe₂O₃. We show the presence of hidden antiferroically-ordered magnetoelectric multipoles in both the prototypical magnetoelectric material Cr₂O₃, and centrosymmetric α-Fe₂O₃, which has the same crystal structure as Cr₂O₃, but a different magnetic dipolar ordering. In turn, we predict anti-magnetoelectric effects, in which local magnetic dipole moments are induced in opposite directions under the application of an external electric field, to create an additional antiferromagnetic ordering. We confirm the predicted induced moments using first-principles calculations. Our results demonstrate the existence of hidden magnetoelectric multipoles leading to local linear magnetoelectric responses even in centrosymmetric materials, where a net bulk linear magnetoelectric effect is forbidden by symmetry.  This archive contains input files and some output files necessary for creating all the data discussed in our paper with the title: Hidden orders and (anti-)Magnetoelectric Effects in Cr₂O₃ and α-Fe₂O₃.

en: Hidden orders and (anti-)magnetoelectric effects in Cr₂O₃ and α-Fe₂O₃

materialscloud

en: Materials Cloud

erc

display_name: erc

magnetoelectricity

display_name: magnetoelectricity

multipoles

display_name: multipoles


en:
In this paper, we propose an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation. Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus improve the convergence of statistical averages at a fraction of the original computational cost, a machine-learned (ML) energy function is trained on FP energies and forces and used in the MD simulations. The ML workflow and MD simulation times (≈200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV. The extension is successfully benchmarked on the same set of alkaline and alkaline-earth ions.
The record includes all molecular-dynamics trajectories, energies and forces used to obtain the solvation energies of alkaline and alkaline-earth ions in water, as reported in Table 2 of referenced paper.

en: Solvation free energies from machine learning molecular dynamics

envi

en: Environment

solvation

display_name: solvation


en: Twinning is an important deformation mode in plastically deformed hexagonal close-packed materials. The extremely high twin growth rates at the nanoscale make atomistic simulations an attractive method for investigating the role of individual twin/matrix interfaces such as twin boundaries and basal-prismatic interfaces in twin growth kinetics. Unfortunately, there is no single framework that allows researchers to differentiate such interfaces automatically, neither in experimental in-situ transmission electron microscopy analysis images nor in atomistic simulations. Moreover, the presence of alloying elements introduces substantial noise to local atomic environments, making it nearly impossible to identify which atoms belong to which interface. Here, with the help of advanced machine learning methods, we provide a proof-of-concept way of using the local stress field distribution as an indicator for the presence of interfaces and for determining their types. We apply such an analysis to the growth of twin embryos in Mg-10 at.% Al alloys under constant stress and constant strain conditions, corresponding to two extremes of high and low strain rates, respectively. We discover that the kinetics of such growth is driven by high-energy basal-prismatic interfaces, in line with our experimental observations for pure Mg. The data provided here allows for reproducing findings from our work.

en: Machine learning of twin/matrix interfaces from local stress field

magnesium-twinning

display_name: magnesium-twinning


en: Antiferromagnets are normally thought of as materials with compensated magnetic sublattices. This adds to their technological advantages but complicates readout of the antiferromagnetic state. We demonstrate theoretically the existence of a Dzyaloshinskii-Moriya interaction (DMI) which is determined by the magnetic symmetry classes of Cr₂O₃ surfaces with an in-plane magnetic easy axis. The DMI explains a previously predicted out-of-plane magnetization at the nominally compensated surfaces of chromia, leading to a surface-localized canted ferrimagnetism. This is in agreement with magnetotransport measurements and with density functional theory predictions which further allow us to quantify the strength of DMI. The temperature dependence of the transversal resistance for these planes shows distinct behavior in comparison with that of the Cr₂O₃  plane, which we attribute to the influence of DMI. Our work provides a framework to analyze surface-driven phenomena in antiferromagnets, and motivates the use of nominally compensated chromia surfaces for antiferomagnetic spintronics and magnonics. Here, we provide the data for density functional theory and Monte Carlo calculations. Micromagnetic and experimental data for this joint experiment-theory collaboration are archived separately according to EU regulations.

en: Surface-symmetry-driven Dzyaloshinskii-Moriya interaction and canted ferrimagnetism in collinear magnetoelectric antiferromagnet Cr₂O₃

density-functional-theory

display_name: density-functional-theory

dzyaloshinskii-moriya-interactions

display_name: dzyaloshinskii-moriya-interactions

magnetoelectrics

display_name: magnetoelectrics

magnetotransport

display_name: magnetotransport

micromagnetics

display_name: micromagnetics

surface-magnetization

display_name: surface-magnetization


en: We present a first-principles study of the low-temperature rhombohedral phase of BaTiO₃ using Hubbard-corrected density-functional theory. By employing density-functional perturbation theory, we compute the onsite Hubbard U for Ti(3d) states and the intersite Hubbard V between Ti(3d) and O(2p) states. We show that applying the onsite Hubbard U correction alone to Ti(3d) states proves detrimental, as it suppresses the Ti(3d)-O(2p) hybridization and drives the system towards a cubic phase. Conversely, when both onsite U and intersite V are considered, the localized character of the Ti(3d) states is maintained, while also preserving the Ti(3d)-O(2p) hybridization, restoring the rhombohedral phase of BaTiO₃. The generalized PBEsol+U+V functional yields good agreement with experimental results for the band gap and dielectric constant, while the optimized geometry is slightly less accurate compared to PBEsol. Zone-center phonon frequencies and Raman spectra are found to be significantly influenced by the underlying geometry. PBEsol and PBEsol+U+V provide satisfactory agreement with the experimental Raman spectrum when the PBEsol geometry is used, while PBEsol+U Raman spectrum diverges strongly from experimental data highlighting the adverse impact of the U correction alone in BaTiO₃. Our findings underscore the promise of the extended Hubbard PBEsol+U+V functional with first-principles U and V for the investigation of other ferroelectric perovskites with mixed ionic-covalent interactions.

en: Understanding the role of Hubbard corrections in the rhombohedral phase of BaTiO₃

cscs

display_name: cscs

dftu

display_name: dftu

dftuv

display_name: dftuv

phonons

display_name: phonons

raman-spectra

display_name: raman-spectra


en: The mechanisms underlying the reaction between carbon monoxide (CO) and activated dioxygen on metal oxide substrates to produce CO₂ remain poorly understood, particularly regarding the role of oxygen vacancies and the nature of the activated O₂ adsorbate. In this study, we present experimental findings from infrared reflection-absorption spectroscopy on a model system of bulk monocrystalline CeO₂(111). Contrary to expectations, exposing the reduced surface to dioxygen (O₂) at 80 K does not yield activated oxygen species, such as superoxo or peroxo. Notably, in the presence of adsorbed CO, an unexpected low-temperature oxidation reaction occurs, consuming CO while oxidizing the CeO₂ substrate. Since a direct reaction between impinging O₂ and adsorbed CO is unlikely at these low temperatures, a novel mechanism is proposed. Extensive spin-polarized density functional theory (DFT) calculations reveal that oxygen vacancies play a critical role in this low-temperature CO oxidation. Initially located in the subsurface region (Vss), these vacancies migrate to the surface (Vs) via a concerted interaction with coadsorbed CO and O₂, leading to O₂ activation and the formation of superoxo or peroxo species. Detailed analysis identifies key reaction intermediates and quantifies their adsorption energies and activation barriers. Our findings suggest that the peroxo-mediated pathway, with its lower activation barrier, is more favorable for CO oxidation at low temperatures compared to the carbonate pathway. This study provides valuable insights into the dynamic role of subsurface oxygen vacancies in the activation of gaseous O₂ and CO oxidation mechanisms on CeO₂.

en: Synergistic effects in low-temperature CO oxidation on cerium oxide surfaces

ceria

display_name: ceria

co-oxidation

display_name: co-oxidation

dft

display_name: dft

peroxo-and-superoxo

display_name: peroxo-and-superoxo


en: An extensive dataset consisting of adsorption energies of pernicious impurities present in biomass upgrading processes on common catalysts and support materials has been generated. This work aims to inform catalyst and process development for the conversion of biomass-derived feedstocks to fuels and chemicals. A high-throughput workflow was developed to execute density functional theory calculations for a diverse set of atomic (Al, B, Ca, Cl, Fe, K, Mg, Mn, N, Na, P, S, Si, Zn) and molecular (COS, H₂S, HCl, HCN, K₂O, KCl, NH₃) species on 35 unique surfaces for transition-metal (Ag, Au, Co, Cu, Fe, Ir, Ni, Pd, Pt, Re, Rh, Ru) and metal-oxide (Al₂O₃, MgO, anatase-TiO₂, rutile-TiO₂, ZnO, ZrO₂) catalysts and supports. Approximately 3,000 unique adsorption geometries were obtained. The data record includes structure and calculation output files for each unique adsorbate geometry on each surface.

en: High-throughput dataset of impurity adsorption on common catalysts in biomass upgrading applications

high-throughput

display_name: high-throughput

vasp

display_name: vasp


en: Predicting the electrical properties of organic molecular crystals (OMCs) is challenging due to their complex crystal structures and electron-phonon (e-ph) interactions. Charge transport in OMCs is conventionally categorized into two limiting regimes – band transport, characterized by weak e-ph interactions, and charge hopping due to localized polarons formed by strong e-ph interactions. However, between these two limiting cases there is a less well understood intermediate regime where polarons are present but transport does not occur via hopping. Here we show a many-body first-principles approach that can accurately predict the carrier mobility in OMCs in the intermediate regime and shed light on its microscopic origin. Our approach combines a finite-temperature cumulant method to describe strong e-ph interactions with Green-Kubo transport calculations. We apply this parameter-free framework to naphthalene crystal, demonstrating electron mobility predictions within a factor of 1.5–2 of experiment between 100–300 K. Our analysis reveals that electrons couple strongly with both inter- and intramolecular phonons in the intermediate regime, as evidenced by the formation of a broad polaron satellite peak in the electron spectral function and the failure of the Boltzmann transport equation (BTE). Our study advances quantitative modeling of charge transport in complex organic crystals. This dataset contains input and output files required to reproduce the BTE results presented in our work.

en: Intermediate polaronic charge transport in organic crystals from a many-body first-principles approach

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).

boltzmann-equation

display_name: boltzmann-equation

carrier-mobility

display_name: carrier-mobility

charge-transport

display_name: charge-transport

electron-phonon-coupling

display_name: electron-phonon-coupling

electron-phonon-scattering-rate

display_name: electron-phonon-scattering-rate

first-principles-calculations

display_name: first-principles-calculations

molecular-crystal

display_name: molecular-crystal

naphthalene

display_name: naphthalene

organic-crystal

display_name: organic-crystal

polaron

display_name: polaron


en:
Metal-organic frameworks (MOF) and covalent organic frameworks (COFs) are promising nanocarriers for targeted drug delivery. For their uptake and release, non-covalent interactions between the framework and the drugs play a fundamental role. However, an in-depth understanding of how different functional groups affect these interactions is still lacking. Using a multilevel approach combining molecular docking and density functional theory, we show in the publication associated with this data how computational modeling can be exploited to gain information on the interplay between functionalization and drug delivery features. We find that functional groups significantly impact the strength of the host-guest interactions. Moreover, the interaction strength qualitatively correlates with drug release data from experimental literature, providing a link between framework structure and release properties. The results of our study facilitate the customization of non-covalent interactions between MOFs/COFs and drugs and give direction on how to rationally design nanocarriers for sustaining the drug release in the body over longer times.
The data deposited on the Materials Cloud Archive contains the inputs and outputs of the molecular docking and DFT simulations.

en: Engineering host-guest interactions in organic framework materials for drug delivery

materialscloud

en: Materials Cloud

covalent-organic-frameworks

display_name: covalent-organic-frameworks

drug-delivery

display_name: drug-delivery

metal-organic-frameworks

display_name: metal-organic-frameworks


en:
We developed a general framework for hybrid quantum-classical computing of molecular and periodic embedding calculations based on an orbital space separation of the fragment and environment degrees of freedom. We show its potential by presenting a specific implementation of periodic range-separated DFT coupled to a quantum circuit ansatz, whereby the variational quantum eigensolver and the quantum equation-of-motion approach are used to obtain the low-lying spectrum of the embedded fragment Hamiltonian.
Application of this scheme to study strongly correlated molecular systems and localized electronic states in materials is showcased through the accurate prediction of the optical properties for the neutral oxygen vacancy in magnesium oxide (MgO). Despite some discrepancies in absorption predictions, the method demonstrates competitive performance with state-of-the-art ab initio approaches, particularly evidenced by the accurate prediction of the photoluminescence emission peak.

en: A general framework for active space embedding methods: applications in quantum computing

envi

en: Environment

marvelqs

display_name: marvelqs

qiskit-nature

display_name: qiskit-nature

quantum-embedding

display_name: quantum-embedding

quantum-simulation

display_name: quantum-simulation


en: This dataset includes the full computational workflows of a proof-of-concept study of various possible mechanisms (standard and bifunctional ones) for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) on exfoliated NiOOH as electrocatalyst, including active edge sites (M5) and hydrogen acceptor sites in the same model system. Furthermore, explicit water is included in the model to describe the equilibration of the M-OOH species to M-OOH/eq, a crucial step that enables a bifunctional route to be operative. Additionally, different single Fe-dopant positions (M1, M2, M3, M4, M5, M6 and M7) are considered and four different reaction schemes (S1, S2, S3 and S4) are studied for the OER and the reverse ORR process. The results are relevant in alkaline conditions, where the studied model systems are stable. Certain Fe-dopant positions result in active Ni-edge sites with very low overpotentials provided that water is present within the model system.

en: Oxygen evolution and reduction on Fe‑doped NiOOH

electrochemical-overpotential

display_name: electrochemical-overpotential

mixed-metal-oxyhydroxides

display_name: mixed-metal-oxyhydroxides

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction


en: The electronic properties and optical response of ice and water are intricately shaped by their molecular structure, including the quantum mechanical nature of hydrogen atoms. Despite numerous former studies, a comprehensive understanding of nuclear quantum effects (NQE) on the electronic structure of water and ice at finite temperatures remains elusive. Here, we utilize molecular simulations that harness efficient machine-learning potentials and many-body perturbation theory to assess how NQEs impact the electronic bands of water and hexagonal ice. By comparing path-integral and classical simulations, we find that NQEs lead to a larger renormalization of the fundamental gap of ice, compared to that of water, ultimately yielding similar bandgaps in the two systems, consistent with experimental estimates. Our calculations suggest that the increased quantum mechanical delocalization of protons in ice, relative to water, is a key factor leading to the enhancement of NQEs on the electronic structure of ice.

en: Nuclear quantum effects on the electronic structure of water and ice

electronic-structure

display_name: electronic-structure

molecular-dynamics

display_name: molecular-dynamics

nuclear-quantum-effects

display_name: nuclear-quantum-effects

water

display_name: water


en: This work presents an application of the strain-fluctuation method, exploiting the fluctuations of the strain from extensive first-principles molecular dynamics simulations in the isobaric-isothermal ensemble, to the study of the elastic tensors of superionic materials. As the superionic materials for solid-state electrolyte applications usually do not have well-defined ground-state configurations, it is challenging to apply the static methods to calculate the elastic tensors of these materials. Instead, the strain-fluctuation method captures the dynamical nature of the elastic response of these materials and is a promising approach to studying their elastic properties. In this work: a protocol is presented and documented to extract the elastic the elastic moduli and their statistical errors from the molecular dynamics trajectories (open-source code available at https://github.com/materzanini); results for two benchmark superionic materials (Li₁₀GeP₂S₁₂ and Li₁₀GeP₂O₁₂) are given; for these superionic materials, a comparison to static methods is also provided, showing that static methods overestimate the moduli with respect to the correct dynamical treatment by ~25-50%.

en: Solids that are also liquids: elastic tensors of superionic materials

big-map

display_name: big-map

cscs

display_name: cscs

elastic-constants

display_name: elastic-constants

first-principles-molecular-dynamics

display_name: first-principles-molecular-dynamics

lgpo

display_name: lgpo

lgps

display_name: lgps

snsf

display_name: snsf

solid-state-electrolytes

display_name: solid-state-electrolytes

statistical-sampling

display_name: statistical-sampling

stress-strain

display_name: stress-strain

superionics

display_name: superionics


en: The study of magnetism in transition metals is a cornerstone in understanding complex electronic and magnetic interactions in condensed matter systems. Among transition metal elements, body-centered cubic (bcc) chromium stands out because of its spin-density wave (SDW) ground state, posing a long-standing challenge for density functional theory (DFT). Conventional functionals, such as the generalized-gradient approximation (GGA) and the local-density approximation (LDA), fail to predict this experimentally observed incommensurate SDW as the ground state. In this study, we present a comprehensive DFT analysis of bcc Cr employing GGA and a variety of meta-GGA functionals. We evaluated total energies, structural parameters, and magnetic properties across a wide range of SDW wave vectors. Our results show that all meta-GGA functionals overestimate the local magnetic moments and enhance the nodal magnetic frustration, destabilizing the SDW state relative to the commensurate antiferromagnetic (AF) configuration. Tao-Perdew-Staroverov-Scuseria (TPSS) yields results closest to those of the GGA, thus providing the most adequate description of bcc Cr among the meta-GGA functionals. These results emphasize the need for the further development of non-local or hybrid functionals tailored for complex magnetic systems.

en: A first-principles study of bcc chromium beyond the generalized gradient approximation (GGA)

chromium

display_name: chromium

dft

display_name: dft

magnetism

display_name: magnetism

spin-density-waves

display_name: spin-density-waves


en: This dataset includes the full computational workflows of a density functional theory based thermodynamics model for the overpotential of the oxygen evolution reaction (OER) on an oxidized palladium surface. The model assumes an oxygen bridge vacancy as an active site on the 110 surface of the tetragonal PdO2 (rutile type structure). The critical OER potential has been computed with variation of the Fe modifyer type, either dopant or adsorbate, and modifyer position. Furthermore, an alternative bifunctional pathway of OER has been considered by adding an H atom to an auxiliary O-bridge site from which the proton−electron pair for second OER reaction step is released rather than from the hydroxylated active site. The computed OER overpotential on the Fe-free surface via this bifunctional route is 0.42 V. A substitution of Pd with Fe directly at this active site further reduces the calculated OER overpotential, over the same route, to 0.35 V. This 70 mV decrease in overpotential is in good agreement with the experimentally measured decrease of 60 mV in the onset potential.

en: Oxygen evolution reaction by a palladium foil in the presence of iron

electrochemical-overpotential

display_name: electrochemical-overpotential

iron-dopant

display_name: iron-dopant

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

palladium-dioxide

display_name: palladium-dioxide


en:
Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ranging from temperature scaling to different Bayesian treatments of neural networks, have been proposed to mitigate overconfidence, most often supported by the numerical observation that they yield better calibrated uncertainty measures. In this work, we provide a sharp comparison between popular uncertainty measures for binary classification in a mathematically tractable model for overparametrized neural networks: the random features model. We discuss a trade-off between classification accuracy and calibration, unveiling a double descent like behavior in the calibration curve of optimally regularized estimators as a function of overparametrization. This is in contrast with the empirical Bayes method, which we show to be well calibrated in our setting despite the higher generalization error and overparametrization.
This record provides the code to reproduce the numerical experiments of the related paper "On double-descent in uncertainty quantification in overparametrized models".

en: On double-descent in uncertainty quantification in overparametrized models (code)

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).

neural-networks

display_name: neural-networks

numerical-simulation

display_name: numerical-simulation

uncertainty-quantification

display_name: uncertainty-quantification


en: This work explores hydrogen-doped samarium nickelate from first-principles calculations. At a concentration of 1/4 hydrogen per formula unit we find a number of polar states due to the presence of the interstitial hydrogen. Physically, the polarization of the material arises from the localization of the hydrogen's valence electron on a nearby nickel-oxygen octahedron leading to a local dipole. Due to the inherent tilt pattern present in samarium nickelate, a perovskite with an a-a-c+ tilt pattern, there is an insurmountable energy barrier to switch a given polar state the structure related by inversion symmetry. Instead, we use an in-plane epitaxial constraint to tune the total energy of two structures to be equal. These two structures, unrelated by a cell-symmetry operation, have similar a similar position of the interstitial hydrogen atom, but the valence electron localizes on a different nickel-oxygen octahedron leading to different polarizations. We find that there is a surmountable energy barrier to switch between these two structures.

en: "Fraternal-twin" ferroelectricity:  competing polar states in hydrogen-doped samarium nickelate from first principles

materialscloud

en: Materials Cloud

charge-localization

display_name: charge-localization

ferroelectricty

display_name: ferroelectricty

rare-earth-nickelates

display_name: rare-earth-nickelates


en: The determination of transport coefficients through the time-honoured Green-Kubo theory of linear response and equilibrium molecular dynamics requires significantly longer simulation times than those of equilibrium properties, while being further hindered by the lack of well-established data-analysis techniques to evaluate the statistical accuracy of the results. Leveraging recent advances in the spectral analysis of the current time series associated to molecular trajectories, we introduce a new method to estimate the full (diagonal as well as off-diagonal) Onsager matrix of transport coefficients from a single statistical model. This approach, based on the knowledge of the statistical distribution of the Onsager-matrix samples in the frequency domain, unifies the evaluation of diagonal (conductivities and viscosities) and off-diagonal (e.g., thermoelectric) transport coefficients within a comprehensive framework, significantly improving the reliability of transport coefficient estimation for materials ranging from molten salts to solid-state electrolytes. We validate the accuracy of this method against existing approaches using benchmark data on molten cesium fluoride and liquid water, and conclude our presentation with the computation of various transport coefficients of the Li₃PS₄ solid-state electrolyte.

en: Transport coefficients from equilibrium molecular dynamics

envi

en: Environment

molecular-dynamics

display_name: molecular-dynamics

solid-state-electrolytes

display_name: solid-state-electrolytes

thermoelectricity

display_name: thermoelectricity

transport

display_name: transport


en: This study combines machine learning (ML) and high-throughput calculations to uncover new ternary electrides in the A₂BC₂ family of compounds with the P4/mbm space group. Starting from a library of 214 known A₂BC₂ phases, density-functional theory calculations were used to compute the maximum value of the electron localization function, indicating that 42 are potential electrides. A model was then trained on this dataset and used to predict the electride behaviour of 14,437 hypothetical compounds generated by structural prototyping. Then, the stability and electride features of the 1254 electride candidates predicted by the model were carefully checked by high-throughput calculations.

en: Machine learning-accelerated discovery of A₂BC₂ ternary electrides with diverse anionic electron densities

density-functional-theory

display_name: density-functional-theory

electrides

display_name: electrides

machine-learning

display_name: machine-learning


en:
A comprehensive study of the electronic and structural phase transition from 1T` to Td in the bulk Weyl semimetal Mo1-xWxTe2 at different doping concentrations has been carried out using time-of-flight momentum microscopy (including circular and linear dichroism), X-ray photoelectron spectroscopy, X-ray photoelectron diffraction, X-ray diffraction (XRD), angle-resolved Raman spectroscopy, transport measurements (including longitudinal elastoresistance), density functional theory (DFT) and Kikuchi pattern calculations. High-resolution, angle-resolved photoemission spectroscopy at 20 K reveals surface electronic states, which are indicative for topological Fermi arcs. Their dispersion agrees with the position of Weyl points predicted by DFT calculations based on the precise crystal structure of our samples obtained from XRD measurements. Raman spectroscopy confirms the inversion symmetry breaking for the Td-phase, which is a necessary condition for the emergence of topological states. Transport measurements show that increasing the doping concentration from 2 to 9 % leads to an increase in the temperature of the phase transition from 1T` to Td from 230 K to 270 K. Magnetoresistance and longitudinal elastoresistance show significantly increased values in the Td-phase.
This dataset contains the raw data discussed in the manuscript with the same title.

en: Doping-Induced Electronic and Structural Phase Transition in the Bulk Weyl semimetal Mo1-xWxTe2

alloy

display_name: alloy

cpa

display_name: cpa

dft

display_name: dft

kkr

display_name: kkr

tmdc

display_name: tmdc

weyl-semimetal

display_name: weyl-semimetal


en: Two-dimensional (2D) materials are among the most promising candidates for beyond silicon electronic and optoelectronic applications. Recently, their recognized importance, sparked a race to discover and characterize new 2D materials. Within few years the number of experimentally exfoliated or synthesized 2D materials went from a couple of dozens to few hundreds while the number theoretically predicted compounds reached a few thousands. In 2018 we first contributed to this effort with the identification of 1825 compounds that are either easily (1036) or potentially (789) exfoliable from experimentally known 3D compounds. In the present work we report on the new materials recently added to the 2D-portfolio thanks to the extension of the screening to an additional experimental database (MPDS) as well as the most up-to-date versions of the two databases (ICSD and COD) used in our previous work. This expansion led to the discovery of an additional 1252 unique monolayers bringing the total to 3077 compounds and, notably, almost doubling the number of easily exfoliable materials (2004). Moreover, we optimized the structural properties of all the materials (regardless of their binding energy or number of atoms in the unit cell) as isolated mono-layer and explored their electronic band structure. This archive entry contains the database of 2D materials in particular it contains the structural parameters for all the 3077 structures of the global Material Cloud 2D database as extracted from their bulk 3D parent, 2710 optimized 2D structures and 2345 electronic band structure together with the provenance of all data and calculations as stored by AiiDA.

en: The Materials Cloud 2D database (MC2D)

electronic-structure

display_name: electronic-structure

high-throughput

display_name: high-throughput


en: Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the potential to reduce the time and resources needed for reaction optimization by more rapidly identifying an ideal catalyst. These approaches, however, are often non-transferable and cannot be applied across different reactions. To overcome this drawback, we introduce a general featurization strategy for bidentate ligands that is coupled with an automated feature selection pipeline and Bayesian ridge regression to perform multivariate linear regression modeling. This approach, which is applicable to any reaction, incorporates electronic, steric, and topological features (rigidity/flexibility, branching, geometry, constitution) and is well-suited for early-stage ligand optimization. Using only small datasets, our workflow capably predicts the enantioselectivity of four metal-catalyzed asymmetric reactions. Uncertainty estimates provided by Bayesian ridge regression permit the use of Bayesian optimization to efficiently explore pools of prospective new ligands. Finally, we constructed the BDL-Cu-2023 dataset, comprised of 312 bidentate ligands extracted from the CSD, and screened it with this procedure to identify promising ligand candidates for a challenging asymmetric oxy-alkynylation reaction.

en: Reaction-agnostic featurization of bidentate ligands for Bayesian ridge regression of enantioselectivity

bidentate-ligands

display_name: bidentate-ligands

catalysis

display_name: catalysis

epfl

display_name: epfl

homogenous-catalysis

display_name: homogenous-catalysis

ligands

display_name: ligands

nccr-catalysis

display_name: nccr-catalysis


en: The C-F activation is one of the important processes in chemical synthesis. Here, we studied the hydrodefluorination of PhCF3 with SiMe2Ph-H catalyzed by Ni(0) complexes. The mechanisms involve three main steps: C-F bond cleavage of PhCF3 on the nickel complex, transmetalation of Ni-F with SiMe2Ph-H to form a nickel hydride complex, and C-H reductive elimination of PhCF2H. We performed density functional calculations on nickel complexes with thirty carbene and phosphine ligands to obtain the relative free energy profiles. Then, linear free energy scaling relationships were determined and molecular volcano plots were constructed. To accurately describe catalytic activity, we found that multiple reference states must be considered.  Thus, the concept of "reference-generalized volcano plots (RGVPs)" was introduced to assist with the selection of the appropriate reference state to determine catalytic activity. Our regression models indicate that electronic properties of ligands significantly influence energy descriptors more than steric size, although steric hindrance affects reference state stability. Optimal ligands are highly electron-donating with small-to-moderate steric sizes. This study highlights the importance of selecting appropriate ligands and metal centers to optimize catalytic activity. Examples of input files for density functional calculations in Gaussian16 to obtain relative free energies for all intermediates and transition states, the spreadsheet tables including the calculated relative free energies for generating LFESRs and volcano plots, and all optimized Cartesian coordinates are provided.

en: Computational Design of Transition Metal Catalysts for Hydrodefluorination of Trifluoromethylarenes using Hydrosilane

carbenes

display_name: carbenes

volcano-plot

display_name: volcano-plot


en: The development of spintronic devices demands the existence of materials with some kind of spin splitting (SS). In this work, we have built a database of ab initio calculated SS in 2D materials. More than that, we propose a workflow for materials design integrating an inverse design approach and a Bayesian inference optimization.  We use the prediction of SS prototypes for spintronic applications as an illustrative example of the proposed workflow. The prediction process starts with the establishment of the design principles (the physical mechanism behind the target properties), that are used as filters for materials screening, and followed by density functional theory (DFT) calculations. Applying this process to the C2DB database, we identify and classify 315 2D materials according to SS type at the valence and/or conduction bands. The Bayesian optimization captures trends that are used for the rationalized design of 2D materials with the ideal conditions of band gap and SS for potential spintronics applications. This repository then contains the main source of data generated in this work, which encompasses full information regarding the materials structure and band structure calculations results, and a database of all the identified spin splittings for these compounds, available in multiple formats.

en: High throughput inverse design and Bayesian optimization of functionalities: spin splitting in two-dimensional compounds

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).

bayesian-optimization

display_name: bayesian-optimization

spin-splitting

display_name: spin-splitting

spintronics

display_name: spintronics


en: Raman spectroscopy is a widely-used non-destructive material characterization method, which provides information about the vibrational modes of the material and therefore of its atomic structure and chemical composition. Raman spectra can be simulated using atomistic first-principles methods but these are computationally demanding and thus the existing databases of computational Raman spectra are fairly small. We developed an optimized workflow to efficiently calculate the Raman tensors, from which the Raman spectra can be straightforwardly simulated. The workflow was benchmarked and validated by comparison to experiments and previous computational methods for select technologically relevant material systems. Using the workflow, we performed high-throughput calculations for a large set of materials (5099) belonging to many different material classes, and collected the results to a database.

en: High-throughput computation of Raman spectra from first principles

materialscloud

en: Materials Cloud

density-functional-theory

display_name: density-functional-theory

raman-tensors

display_name: raman-tensors


en: Physics-inspired molecular representations are the cornerstone of similarity-based learning applied to solve chemical problems. Despite their conceptual and mathematical diversity, this class of descriptors shares a common underlying philosophy: they all rely on the molecular information that determines the form of the electronic Schrödinger equation. Existing representations take the most varied forms, from non-linear functions of atom types and positions to atom densities and potential, up to complex quantum chemical objects directly injected into the ML architecture. In this work, we present the Spectrum of Approximated Hamiltonian Matrices (SPAᴴM) as an alternative pathway to construct quantum machine learning representations through leveraging the foundation of the electronic Schrödinger equation itself: the electronic Hamiltonian. As the Hamiltonian encodes all quantum chemical information at once, SPAᴴM representations not only distinguish different molecules and conformations, but also different spin, charge, and electronic states. As a proof of concept, we focus here on efficient SPAᴴM representations built from the eigenvalues of a hierarchy of well-established and readily-evaluated "guess" Hamiltonians. These SPAᴴM representations are particularly compact and efficient for kernel evaluation and their complexity is independent of the number of different atom types in the database

en: SPAᴴM: the spectrum of approximated hamiltonian matrices representations

envi

en: Environment

erc

display_name: erc

guess-hamiltonians

display_name: guess-hamiltonians

machine-learning

display_name: machine-learning

molecular-representation

display_name: molecular-representation

snsf

display_name: snsf

spahm

display_name: spahm


en: Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous ``on-the-fly'' training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H2 turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment.

en: Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

molecular-dynamics

display_name: molecular-dynamics