en: Efficient O₂ reduction to H₂O₂, vital for energy conversion and environmental cleanup, relies on precise control of heterogeneous catalysts interacting with reaction species. Through high-throughput density functional theory calculations, consisting of 369 single atom catalysts, we identified the polarized descriptor (electric dipole strength) on two-dimensional carbon materials, revealing insights into the catalytic effect of support polarization. Surprisingly, this descriptor exhibits advanced scaling relationships towards H₂O₂ synthesis, incorporating factors such as active metals, coordination environments, and surface curvatures, highlighting its widespread significance. Furthermore, it demonstrates reliable predictability for O₂ adsorption in dynamic water environments, with optimal reactivity observed within the range of -1.40 to -1.00 e·Å, as confirmed by dynamic and static simulations of the 2e- pathway of O₂ reduction. In essence, these findings offer valuable insights for the rational design of electrocatalysts tailored for selective O₂ reduction.

en: Scaling relations and dynamical predictiveness of electric dipole strength on 2e- ORR catalytic property

density-functional-theory-calculations

display_name: density-functional-theory-calculations

electric-dipole-strength

display_name: electric-dipole-strength

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction

support-polarization

display_name: support-polarization


$oid: OBSOLETE

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente


en:
This study presents the initial implementation of the Fast INtention-Agnostic LEarning Server (FINALES) in a demonstration of a distributed Materials Acceleration Platform (MAP) including experimental and computational methods and a machine learning (ML)-based optimizer. In this demonstration, the optimizer was configured to minimize the density of the electrolyte solutions while maximizing the viscosity by exploiting experimental and computational results. The tenants (the units connected to FINALES in the MAP) are shortly described in the following:

- Autonomous Synthesis and Analysis of Battery electrolytes (ASAB) setup: an experimental tenant providing density and viscosity data using a densimeter of the type DMA 4100M and a viscometer of type Lovis 2000 both by Anton Paar Germany
- Molecular dynamics tenant: a computational tenant capable of providing radial distribution functions, diffusion coefficients, ionic conductivity, transference numbers, heat capacity and density data to the MAP
- Optimizer: the tenant guiding the optimization by processing the available data and generating requests for electrolyte formulations to be tested subsequently

This dataset accompanies the publication:
Vogler, M., Busk, J., Hajiyani, H., Jørgensen, P. B., Safaei, N., Castelli, I. E., Ramirez, F. F., Carlsson, J., Pizzi, G., Clark, S., Hanke, F., Bhowmik, A. & Stein, H. S. Brokering between tenants for an international materials acceleration platform. Matter 6, 2647–2665 (2023). DOI: https://doi.org/10.1016/j.matt.2023.07.016

en: FINALES (06/2022) – Electrolyte Optimization for Minimum Density and Maximum Viscosity

battery-electrolyte

display_name: battery-electrolyte

big-map

display_name: big-map

conductivity

display_name: conductivity

density

display_name: density

materials-acceleration-platform-map

display_name: materials-acceleration-platform-map

polis

display_name: polis

radial-distribution-function-rdf

display_name: radial-distribution-function-rdf

viscosity

display_name: viscosity


en: Proximity-induced superconductivity in hybrid devices of topological insulators and superconductors offers a promising platform for the pursuit of elusive topological superconductivity and its anticipated applications, such as fault-tolerant quantum computing. To study and harness such hybrid devices, a key challenge is the realization of highly functional material interfaces with a suitable superconductor featuring 2e-periodic parity-conserving transport to ensure a superconducting hard-gap free of unpaired electrons, which is important for Majorana physics. A superconductor well-known for this characteristic is Al, however, its direct integration into devices based on tetradymite topological insulators has so far been found to yield non-transparent interfaces. By focusing on Bi₂Te₃-Al heterostructures, this study identifies detrimental interdiffusion processes at the interface through atomically resolved structural and chemical analysis, and showcase their mitigation by leveraging different interlayers – namely Nb, Ti, Pd, and Pt – between Bi₂Te₃ and Al. Through structural transformation of the interlayer materials (X) into their respective tellurides (XTe₂) atomically-sharp epitaxial interfaces are engineered and further characterized in low-temperature transport experiments on Al-X-Bi₂Te₃-X-Al Josephson junctions and in complementary density functional theory calculations. By demonstrating functional interfaces between Bi₂Te₃ and Al, this work provides key insights and paves the way for the next generation of sophisticated topological devices.

en: Engineering epitaxial interfaces for topological insulator – superconductor hybrid devices with Al electrodes

dft

display_name: dft

epitaxial-interfaces

display_name: epitaxial-interfaces

fleur

display_name: fleur

hybrid-devices

display_name: hybrid-devices

interface-engineering

display_name: interface-engineering

josephson-junctions

display_name: josephson-junctions

jukkr

display_name: jukkr

majorana-platform

display_name: majorana-platform

superconductors

display_name: superconductors

tmdc

display_name: tmdc

topological-insulator

display_name: topological-insulator

topological-materials

display_name: topological-materials

topological-superconductivity

display_name: topological-superconductivity


en: The nature of the gap observed at the zone border in the spin excitation spectrum of CrI₃ quasi-two-dimensional single crystals is still controversial. We perform first-principles calculations based on time-dependent density functional perturbation theory, which indicate that the observed gap results from a combination of spin-orbit and interlayer interaction effects. The former give rise to the anisotropic spin-spin interactions that are responsible for its very existence, while the latter determine both its displacement from the K point of the Brillouin zone, due to the in-plane lattice distortions induced by them, and an enhancement of its magnitude, in agreement with experiments and previous theoretical work based on a lattice model.

en: First-principles study of the gap in the spin excitation spectrum of the CrI₃ honeycomb ferromagnet

dirac-cone

display_name: dirac-cone

magnons

display_name: magnons

marvel

display_name: marvel

quantum-espresso

display_name: quantum-espresso

tddft

display_name: tddft


en:
Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be combined explicitly with physically-grounded operations. We present an example of an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on, and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parameterization corresponding to a minimal atom-centered basis. Our results on a comprehensive dataset of hydrocarbons emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency, and providing a blueprint for developing ML-augmented electronic-structure methods.
Here we include the dataset, accompanying the paper linked below, of hydrocarbons including ethane, ethene, butadiene, hexane, hexatriene, isoprene, styrene, polyalkenes (dodecahexaene, tetradecaheptaene, hexadecaoctaene, octadecanonaene, eicosadecaene), aromatics (benzene, azulene, naphthalene, biphenyl), anthracene, beta-carotene, fullerene. We also provide scripts to generate the Fock and overlap matrices in this dataset. The code for machine learning can be found at the Software reference below.

en: Electronic excited states from physically-constrained machine learning

epfl

display_name: epfl

erc

display_name: erc

excited-states

display_name: excited-states

fiamma

display_name: fiamma

hamiltonian

display_name: hamiltonian

lifetimes

display_name: lifetimes

machine-learning

display_name: machine-learning


en: We study self-interaction effects in solvated and strongly-correlated cationic molecular clusters, with a focus on the solvated hydroxyl radical. To address the self-interaction issue, we apply the DC-r²SCAN method, with the auxiliary density matrix approach. Validating our method through simulations of bulk liquid water, we demonstrate that DC-r²SCAN maintains the structural accuracy of r²SCAN while effectively addressing spin density localization issues. Extending our analysis to solvated cationic molecular clusters, we find that the hemibonded motif in the [CH₃S∴CH₃SH]⁺ cluster is disrupted in the DC-r²SCAN simulation, in contrast to r²SCAN that preserves the (three-electron-two-center)-bonded motif. Similarly, for the [SH∴SH₂]⁺ cluster, r²SCAN restores the hemibonded motif through spin leakage, while DC-r²SCAN predicts a weaker hemibond formation influenced by solvent-solute interactions. Our findings demonstrate the potential of DC-r²SCAN combined with the auxiliary density matrix method to improve electronic structure calculations, providing insights into the properties of solvated cationic molecular clusters. This work contributes to the advancement of self-interaction corrected electronic structure theory and offers a computational framework for modeling condensed phase systems with intricate correlation effects.

en: Radicals in aqueous solution: Assessment of density-corrected SCAN functional

aqueous-hydroxyl-radical

display_name: aqueous-hydroxyl-radical

aqueous-sulfanyl-radical

display_name: aqueous-sulfanyl-radical

density-corrected-dft

display_name: density-corrected-dft

density-functional-theory

display_name: density-functional-theory

hydrogen-sulfide

display_name: hydrogen-sulfide

methanethiol

display_name: methanethiol


en: The poor ductility of hcp Mg is attributed to the low activity of non-basal slip systems and so activation of prismatic slip can aid ductility in rolled sheets by providing three additional  Burgers vector slip systems.  Experimental studies show that dilute additions of alloying elements such as Zn and Al leads to softening of prismatic slip at low temperatures but strengthening at higher temperatures.  Here, the role of solute strengthening of prismatic edge dislocations is investigated as a possible explanation for the higher-T strengthening.  Mg-Zn is studied using first-principles inputs in a parameter-free solute strengthening theory.  First-principles DFT is necessary to accurately assess the strong solute chemical interaction energies in the core of the compact edge dislocation.   Such calculations are subtle due to motion of the dislocation in the presence of the solute, and methods to obtain reliable results with acceptable computational cost are discussed.   While interaction energies of Zn in the prism edge core can be quite large (+/- 0.34 eV), the edge solute strengthening of prismatic slip in dilute Mg-Zn remains well below experiments at high temperatures.  However, the large difference (0.68 eV) in Zn/dislocation interaction energies across the core of the edge dislocation suggests strengthening by dynamic strain aging as an explanation for the higher-T strengthening.

en: Solute strengthening of prism edge dislocations in Mg alloys

dft

display_name: dft

edge-solute-strengthening

display_name: edge-solute-strengthening

mg-zn

display_name: mg-zn

prismatic-slip

display_name: prismatic-slip

solutedislocation-interaction-energy

display_name: solutedislocation-interaction-energy


marvel

display_name: marvel


en: The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential can accurately capture silicate-water reactivity. The model was trained on a new dataset comprising 400,000 energies and forces of molecular clusters at the 𝜔-B97XD\def2-TVZP level. To ensure the robustness of the model, we introduce a new and general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. Our trained potential is found to reproduce static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.

en: Learning a reactive potential for silica-water through uncertainty attribution

active-learning

display_name: active-learning

chemical-reactions

display_name: chemical-reactions

free-energy

display_name: free-energy

neural-network-potential

display_name: neural-network-potential

silicates

display_name: silicates


materialscloud

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

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


en: Low-crosslinked polymer networks have recently been found to behave auxetically when subjected to small tensions, that is, their Poisson's ratio ν becomes negative. In addition, for specific state points, numerical simulations revealed that diamond-like networks reach the limit of mechanical stability, exhibiting values of ν = −1, a condition that we define as hyper-auxeticity. This behavior is interesting per se for its consequences in materials science but is also appealing for fundamental physics because the mechanical instability is accompanied by evidence of criticality. In this work, we deepen our understanding of this phenomenon by performing a large set of equilibrium and stress–strain simulations in combination with phenomenological elasticity theory. The two approaches are found to be in good agreement, confirming the above results. We also extend our investigations to disordered polymer networks and find that the hyper-auxetic behavior also holds in this case, still manifesting a similar critical-like behavior as in the diamond one. Finally, we highlight the role of the number density, which is found to be a relevant control parameter determining the elastic properties of the system. The validity of the results under disordered conditions paves the way for an experimental investigation of this phenomenon in real systems, such as hydrogels.

en: Materials Cloud

en: Auxetic polymer networks: The role of crosslinking, density, and disorder

auxetic-materials

display_name: auxetic-materials

elasticity

display_name: elasticity

hydrogels

display_name: hydrogels


envi

en: Environment


en: The anharmonic infrared spectrum of adsorbed CO is simulated using density functional theory (DFT) to gain insight into the nature of Pd nanoparticles (NPs) supported on ceria. The authors systematically determine how the simulated infrared spectra are affected by CO coverage, NP size (0.5–1.5 nm), NP morphology (octahedral, icosahedral), and metal-support contact angle, by exploring a diversity of realistic models inspired by ab initio molecular dynamics.

en: Identifying the structure of supported metal catalysts using vibrational fingerprints from ab initio nanoscale models

carbon-monoxide

display_name: carbon-monoxide

ceria

display_name: ceria

infrarred

display_name: infrarred

palladium

display_name: palladium

spectroscopy

display_name: spectroscopy


en: Transition metal oxides (e.g., cobalt oxides) often undergo a dynamic surface reconstruction under oxygen evolution reaction (OER) conditions to form the active state, which differs in response to the electrolyte pH. The resulting pH-dependency of OER activity is commonly observed but poorly understood. Herein, we demonstrate that operando X-ray absorption spectroscopy (XAS) characterization enables tracking of the Co oxidation transformation at different pH-directed (hydr)oxide/electrolyte interfaces. Combined with in situ electrochemical analyses, correlations between Co redox dynamics, flat band potential and Co oxidation transformations are established to explain the pH-dependency of OER activity. In alkaline environments, the low flat band potential allows a low-potential Co redox transformation, which in turns favors surface reconstruction. In neutral and acidic environments, an anodic shift of the Co redox transformation increases the OER overpotential, particularly in an acidic environment. The largest OER overpotential, in a neutral environment, is further attributable to the poor polarizability of Co atoms and the slowest Co oxidation transformation with respect to the change in applied potential (or OER current). These findings reveal that the Co oxidation transformation at the interface is the factor directly determining the pH-dependency of OER activity, therefore providing insight into designing efficient OER catalysts in different pH environments.

en: Oxidation of interfacial cobalt controls the pH-dependence of the oxygen evolution reaction

cobalt-oxides

display_name: cobalt-oxides

electrocatalytic-interface

display_name: electrocatalytic-interface

operando-x-ray-adsorption-spectroscopy

display_name: operando-x-ray-adsorption-spectroscopy

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction


en: Materials demonstrating positive thermal expansion (PTE) or negative thermal expansion (NTE) are quite common, whereas those exhibiting zero thermal expansion (ZTE) are notably scarce. In this work, we identify the mechanical descriptors, namely in-plane tensile stiffness and out-of-plane bending stiffness, that can effectively classify PTE and NTE 2D crystals. By utilizing high throughput calculations and the state-of-the-art symbolic regression method, these descriptors aid in the discovery of ZTE or 2D Invar monolayers with the linear thermal expansion coefficient (LTEC) within ±2×10⁻⁶ K⁻¹ in the middle range of temperatures. Additionally, the descriptors assist the discovery of large PTE and NTE 2D monolayers with the LTEC larger than ±15×10⁻⁶ K⁻¹, which are so-called 2D anti-Invar monolayers. Advancing our understanding of materials with exceptionally low or high thermal expansion is of substantial scientific and technological interest, particularly in developing next-generation electronics at the nanometer even Ångstrom scale.

en: Machine learning enables the discovery of 2D Invar and anti-Invar monolayers

bending-stiffness

display_name: bending-stiffness

linear-coefficient-of-thermal-expansion

display_name: linear-coefficient-of-thermal-expansion

machine-learning

display_name: machine-learning

symbolic-regression

display_name: symbolic-regression

thermal-expansion

display_name: thermal-expansion

zero-thermal-expansion

display_name: zero-thermal-expansion


en:
We performed a computational study using Density Functional Theory calculations on a copper-graphene system. A global minima search was performed using the Minima Hopping algorithm to find multiple graphene isomers that can appear on different substrates. As a model of copper substrate, we used a 112 copper atom cluster and as a model of graphene flake, we used 40 carbon atoms on top of the copper surface. The system was placed in vacuum to guarantee no interaction between images.

The dataset contains coordinates of graphene flake isomers on crystalline and amorphous Cu substrates.

en: Comparative study of defects in graphene flake grown on amorphous and crystalline copper surfaces

copper

display_name: copper

density-functional-theory

display_name: density-functional-theory

graphene

display_name: graphene

graphene-growth

display_name: graphene-growth

metal-substrate

display_name: metal-substrate


en: Determining the atomic structure of a surface is essential for reliable simulations and in-depth exploration of chemical and atomic-scale physical processes. Using Ge(110) c(8×10) as a case study, this work employs Density Functional Theory (DFT) calculations to examine the role of vibrational entropy in surface reconstruction stability. The Ge(110) c(8×10) unit cell consists of interstitial-based pentamers (Universal Building Block model, UBB) interspersed with regions appearing in STM images as unreconstructed. DFT calculations predict that adding more pentamers lowers the surface energy, contradicting experimental findings. This discrepancy is resolved when vibrational entropy is accounted for and sur-face divacancies are introduced in addition to the UBB pentamers. These divacancies are similar to those proposed earlier in the Tetramer-Heptagonal and Tetragonal Ring (THTR) reconstruction model. The nearest neighbors of the vacancy sites are rebonded as on monatomic step edges. The differences in the vibrational entropy contributed by pentamers, divacancies, and unreconstructed surface stabilize Ge(110) c(8×10) reconstructions with the pentamer density observed experimentally. The presence of divacancies is conceptually consistent with the presence of monatomic steps in Ge(110) "16×2", the most stable reconstruction of this surface.

en: Ge(110) c(8×10) reconstructions stabilized by vibrations

dft

display_name: dft

pbe

display_name: pbe


en:
The presence of spin-orbit coupling or non-collinear magnetic spin states can have dramatic effects on the ground-state and spectral properties of materials, in particular on the band structure. Here, we develop non-collinear Koopmans-compliant functionals based on Wannier functions and density-functional perturbation theory, targeting accurate spectral properties in the quasiparticle approximation. Our non-collinear Koopmans-compliant theory involves functionals of four-component orbitals densities, that can be obtained from the charge and spin-vector densities of Wannier functions. We validate our approach on four emblematic non-magnetic and magnetic semiconductors where the effect of spin-orbit coupling goes from small to very large: the III-IV semiconductor GaAs, the transition-metal dichalcogenide WSe₂, the cubic perovskite CsPbBr₃, and the ferromagnetic semiconductor CrI₃.
The predicted band gaps are comparable in accuracy to state-of-the-art many-body perturbation theory and include spin-dependent interactions and screening effects that are missing in standard diagrammatic approaches based on the random phase approximation. While the inclusion of orbital- and spin-dependent interactions in many-body perturbation theory requires self-screening or vertex corrections, they emerge naturally in the Koopmans-functionals framework.

en: Spin-dependent interactions in orbital-density-dependent functionals: non-collinear Koopmans spectral functionals

metal-halide-perovskite

display_name: metal-halide-perovskite

non-collinear-magnetism

display_name: non-collinear-magnetism

snsf

display_name: snsf

spectral-functionals

display_name: spectral-functionals

spin-dependent-interactions

display_name: spin-dependent-interactions

spin-orbit-coupling

display_name: spin-orbit-coupling

spin-torque

display_name: spin-torque

transition-metal-dichalcogenide

display_name: transition-metal-dichalcogenide


materialscloud

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

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


en: In the pursuit of selective conversion of methane directly to methanol in the liquid phase, a common challenge is the concurrent formation of undesirable liquid oxygenates or combustion byproducts. However, we demonstrate that monometallic Pd-CeO2 catalysts, modified by carbon, created by a simple mechanochemical synthesis method exhibit 100% selectivity towards methanol at 75°C, using hydrogen peroxide as oxidizing agent. The solvent-free synthesis yields a distinctive Pd-iC-CeO2 interface, where interfacial carbon (iC) modulates metal-oxide interactions and facilitates tandem methane activation and peroxide decomposition, thus resulting in an exclusive methanol selectivity of 100% with a rate of 117 µmol/gcat at 75°C. Notably, solvent interactions of H2O2 (aq) were found to be critical for methanol selectivity through a DFT-simulated Eley-Rideal-like mechanism. This mechanism uniquely enables the direct conversion of methane into methanol via a solid-liquid-gas process.

en: From Methane to Methanol: Pd-iC-CeO2 Catalysts Engineered for High Selectivity via Mechano-Chemical Synthesis

eley-rideal-mechanism

display_name: eley-rideal-mechanism


materialscloud

en: Materials Cloud


en: The design of stealthy hyperuniform (SHU) materials has been a critical topic in realizing bandgap materials without crystalline order. Most previous approaches to constructing SHU materials, such as the collective coordinate method, have assumed the closed system, maintaining the number of particles inside a system during the design process. Here, I develop the concept of evolving wave networks, allowing for the open-system design of disordered materials based on the evolution process. The programs are applied to introduce the concept of evolving wave networks (Code_Set_Fig_2), classify material states according to network parameters (Code_Set_Fig_3), generate the stealthy hyperuniformity (SHU) shielding of existing materials (Code_Set_Fig_4), and realize preferential attachment in evolving wave networks (Code_Set_Fig_5).

en: Evolving scattering networks for material classification, stealthy hyperuniform shielding, and preferential attachment

envi

en: Environment

disordered-material

display_name: disordered-material

evolution

display_name: evolution

network

display_name: network

scattering

display_name: scattering


en: The design of stealthy hyperuniform (SHU) materials has been a critical topic in realizing bandgap materials without crystalline order. Most previous approaches to constructing SHU materials, such as the collective coordinate method, have assumed the closed system, maintaining the number of particles inside a system during the design process. Here, I develop the concept of evolving wave networks, allowing for the open-system design of disordered materials based on the evolution process. The programs are applied to introduce the concept of evolving wave networks (Code_Set_Fig_2), classify material states according to network parameters (Code_Set_Fig_3), generate the stealthy hyperuniformity (SHU) shielding of existing materials (Code_Set_Fig_4), realize preferential attachment in evolving wave networks (Code_Set_Fig_5), and obtain the following phase diagram of disordered materials (Code_Set_Fig_6).

en: Evolving scattering networks for material classification, stealthy hyperuniform shielding, preferential attachment, and material phase diagram


en: We present a combined density-functional theory and single-site dynamical mean-field theory (DMFT) study of vanadium dioxide (VO₂) using an unconventional set of bond-centered orbitals as the basis of the correlated subspace. VO₂ is a prototypical material undergoing a metal-insulator transition (MIT), hosting both intriguing physical phenomena and the potential for industrial applications. With our choice of correlated subspace basis, we investigate the interplay of structural dimerization and electronic correlations in VO₂ in a computationally cheaper way compared to other state-of-the-art methods, such as cluster DMFT. Our approach allows us to treat the rutile and M1 monoclinic VO₂ phases on an equal footing and to vary the dimerizing distortion continuously, exploring the energetics of the transition between the two phases. The choice of basis presented in this work hence offers a complementary view on the long-standing discussion of the MIT in VO₂ and suggests possible future extensions to other similar materials hosting molecular-orbital-like states.

en: Single-site DFT+DMFT for vanadium dioxide using bond-centered orbitals

dftdmft

display_name: dftdmft


en: The phase of the quantum-mechanical wave function can encode a topological structure with wide-ranging physical consequences, such as anomalous transport effects and the existence of edge states robust against perturbations. While this has been exhaustively demonstrated for electrons, properties associated with the elementary quasiparticles in magnetic materials are still underexplored. Here, we show theoretically and via inelastic neutron scattering experiments that the bulk ferromagnet Mn₅Ge₃ hosts gapped topological Dirac magnons. Although inversion symmetry prohibits a net Dzyaloshinskii-Moriya interaction in the unit cell, it is locally allowed and is responsible for the gap opening in the magnon spectrum. This gap is predicted and experimentally verified to close by rotating the magnetization away from the c-axis. Hence, Mn₅Ge₃ is the first realization of a gapped Dirac magnon material in three dimensions. Its tunability by chemical doping or by thin film nanostructuring defines an exciting new platform to explore and design topological magnons.

en: Topological magnons driven by the Dzyaloshinskii-Moriya interaction in the centrosymmetric ferromagnet Mn₅Ge₃

dzyaloshinskii-moriya-interaction

display_name: dzyaloshinskii-moriya-interaction

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

marvel

display_name: marvel

spin-waves

display_name: spin-waves

topological-magnon

display_name: topological-magnon


en:
This record contains three datasets and the scripts used to generate figures in "Expanding density-correlation machine learning representations for anisotropic coarse-grained particles." This paper explores the theory and implementation of machine-learning descriptors for ellipsoidal bodies, extending the popular "Smooth Overlap of Atomic Positions" (SOAP) formalism. These case studies serve to demonstrate the different use cases of this technology.  The three datasets are:
- Generated configurations of nematic and smectic liquid crystal systems, with a range of orientational order, characterized by the nematic order parameter
- Dimers of (1, 1.5, 2) ellipsoids at different interaction cutoffs and rotations, with computed Gay-Berne type energies
- Crystalline configurations of planar benzene molecules, with energetics computed  using QuantumEspresso v7.046 using Perdew–Burke–Ernzerhof (PBE) pseudopotentials and cutoff parameters reported by Prandini et al.,  Grimme D3-dispersion correction, and a 3 × 3 Monkhorst–Pack k-point grid.

benzene

display_name: benzene

liquid-crystals

display_name: liquid-crystals

machine-learning

display_name: machine-learning

methods

display_name: methods

quantum-espresso

display_name: quantum-espresso


en: Expanding density-correlation machine learning representations for anisotropic coarse-grained particles


en:
Seven imidazolium-based ionic liquids (ILs) with different anions and cations were investigated as catholytes for the CO₂ electrocatalytic reduction to CO over silver. A significant activity and stability, but different selectivities for CO₂ reduction or the side H₂ evolution were observed. Density functional theory results show that the role of the IL anions is to tune the ratio between the CO₂ captured and electrochemically converted. Acetate anions (being strong Lewis bases) are more prone to CO₂ capture, enhancing H₂ evolution, while fluorinated anions (being weaker Lewis bases) favour the CO₂ electroreduction. Differently from the hydrolytically unstable 1-butyl-3-methylimidazolium tetrafluoroborate, 1-Butyl-3-Methylimidazolium Triflate was the most promising IL showing the highest Faradaic efficiency to CO (>95%), up to 8h of stable operation at high current rates (-20mA & -60 mA).
These results open the way for a strategic selection of the most suitable IL for the CO₂ electroreduction and its future process scale-up.

en: Understanding imidazolium-based ionic liquids role in the CO₂ electroreduction reaction: experimental and theoretical study

dft

display_name: dft

experimental

display_name: experimental

free-energy

display_name: free-energy


materialscloud

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

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


en:
In this work we study isomers of several representative small clusters to find principles for their stability. Our conclusions about the principles underlying the structure of clusters are based on a huge database of 44'000 isomers generated for 59 different clusters on the density functional theory level by Minima Hopping. We explore the potential energy surface of small neutral, anionic and cationic isomers, moving left to right across the third period of the periodic table and varying the number of atoms n and the cluster charge state q (X^q_n, with X={Na, Mg, Al, Si, Ge}, q=-1,0,1,2). We use structural descriptors such as bond lengths and atomic coordination numbers, the surface to volume ratios and the shape factor as well as electronic descriptors such as shell filling and hardness to detect correlations with the stability of clusters. The isomers of metallic clusters are found to be structure seekers with a strong tendency to adopt compact  shapes. However certain numbers of atoms can suppress the formation of a nearly spherical metallic clusters. Small non-metallic clusters typically also do not adopt compact spherical shapes for their lowest energy structures. In both cases spherical jellium models are not any more applicable. Nevertheless for many  structures, that have frequently a high degree of symmetry, the Kohn-Sham eigenvalues are bunched into shells and if the available electrons can completely fill such shells, a particularly stable structure can result. We call such a cluster whose shape gives rise to shells that can be completely filled by the number of available electrons an optimally matched cluster, since both the structure and the number of electrons must be special and match. In this way we can also explain the stability trends for covalent silicon and germanium cluster isomers, whose stability was previously explained by the presence of certain structural motifs. Thus we propose a unified framework to explain trends in the stability of isomers and to predict their structure for a wide range of small clusters.

This data record contains structure coordinates (compressed file all_minima_xyz_structures.tar.gz) of all minima found by the minima hopping method for all neutral and charged clusters, that are 43606 different structures. The python dictionary all_minima_names.npy stores all minima names. The python dictionary all_minima_data.npy stores all structural and electronic properties calculated for all minima. The jupyter notebook all_minima_data_analysis.ipynb helps the reading of the all_minima_names.npy and all_minima_data.npy python dictionaries.

en: Principles of isomer stability in small clusters

atomic-clusters

display_name: atomic-clusters

cscs

display_name: cscs

snsf

display_name: snsf


materialscloud

en: Materials Cloud


en:
We present SIESTA-BdG, an implementation of the simultaneous solution of the Bogoliubov-de Gennes (BdG) and Density Functional Theory (DFT) problem in SIESTA, a first-principles method and code for material simulations which uses pseudopotentials and a localized basis set. This unified approach describes both conventional and unconventional superconducting states, and enables a description of inhomogeneous superconductors and heterostructures. We demonstrate the validity, accuracy, and efficiency of SIESTA-BdG by computing physically relevant quantities (superconducting charge density, band structure, superconducting gap features, density of states) for conventional singlet (Nb, Pb) and unconventional (FeSe) superconductors. We find excellent agreement with experiments and results obtained within the KKR-BdG computational framework. SIESTA-BdG forms the basis for modelling quantum transport in superconducting devices and including - in an approximate fashion - the superconducting DFT potential of Oliveira, Gross, and Kohn.

This dataset contains the calculations performed on bulk Nb and the associated notebooks used to generate figures from this data.

en: Density functional Bogoliubov-de Gennes theory for superconductors implemented in the SIESTA code

aiida

display_name: aiida

aiida-siesta

display_name: aiida-siesta

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

density-functional-theory

display_name: density-functional-theory

horizon-europe

display_name: horizon-europe

marie-curie-fellowship

display_name: marie-curie-fellowship

max

display_name: max

nb

display_name: nb

prace

display_name: prace

siesta

display_name: siesta

superconductivity

display_name: superconductivity


envi

en: Environment


dzyaloshinskii-moriya-interaction

display_name: dzyaloshinskii-moriya-interaction

inelastic-neutron-scattering

display_name: inelastic-neutron-scattering

marvel

display_name: marvel

spin-waves

display_name: spin-waves

topological-magnon

display_name: topological-magnon


en:
Interface stress is a fundamental descriptor for interphase boundaries and is defined in strict relation to the interface energy. In nanomultilayered coatings with their intrinsically high interface density, the functional properties are generally dictated by the interface structure, which in turn is governed by the delicate interaction of residual interface and volume stresses in the coating system. In the present work, experimental estimations of the interface stress in Cu/W NMLs (with a variable residual stress state from tensile to compressive) were compared with corresponding theoretical values as calculated using DFT (adopting an incoherent bcc W{110}/fcc Cu{111} interface with variable in-plane strain). The Cu/W interface stress was experimentally tuned monotonically from positive to negative values by changing the residual stress in the W nanolayers by increasing the Ar pressure during the W deposition steps. Qualitative agreement between experiment and simulation was achieved, both confirming a decrease of the interface stress from the compressive to the tensile regime. The DFT simulations showed that Cu atoms in the vicinity of the strained Cu/W interfaces are displaced along the in-plane and out-of-plane directions in response to the acting
interface stress. Using a preliminary developed neural network potential for the immiscible Cu-W system, specific in-plane crystallographic orientation relationships for the Cu/W interfaces were also tested, which improved quantitative agreement between experiment and theory. Assumptions and limitations in experiments and theory for deriving the interface stress are critically discussed.

en: Experimental and ab initio derivation of interface stress in nanomultilayered coatings: application to immiscible Cu/W system with variable in-plane stress

ab-initio

display_name: ab-initio

copper-tungsten

display_name: copper-tungsten

interface-stress

display_name: interface-stress

neural-network-potential

display_name: neural-network-potential

xrd

display_name: xrd


en: Phenalenyl (C₁₃H₉) is an open-shell spin-1/2 nanographene. Using scanning tunneling microscopy (STM) inelastic electron tunneling spectroscopy (IETS), covalently-bonded phenalenyl dimers have been shown to feature conductance steps associated with singlet-triplet excitations of a spin-1/2 dimer with antiferromagnetic exchange. In a recent work, we address the possibility of tuning the magnitude of the exchange interactions by varying the dihedral angle between the two molecules within a dimer. Theoretical methods, ranging from density functional theory calculations to many-body model Hamiltonians solved within different levels of approximation, are used to explain STM-IETS measurements of twisted phenalenyl dimers on a h-BN/Rh(111) surface. By means of first-principles calculations, we also propose strategies to induce sizable twist angles in surface-adsorbed phenalenyl dimers via functional groups, including a photoswitchable scheme. This record contains data that support the results discussed in the manuscript.

en: Conformational tuning of magnetic interactions in coupled nanographenes

dmrg

display_name: dmrg

magnetic-nanographenes

display_name: magnetic-nanographenes

snsf

display_name: snsf

stmsts

display_name: stmsts


en:
Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven simulations with a linear-scaling cost, and can also be used to deduce useful structure--property relations as they associate simple atomic motifs with complicated macroscopic properties. However, even though there exist practical justifications for these decompositions, only the global quantity is rigorously defined, and thus it is unclear to what extent the atomistic terms predicted by the model can be trusted.  Here, we introduce a quantitative metric, which we call the local prediction rigidity (LPR), to assess how robust the locally decomposed predictions of ML models are. We investigate the dependence of LPR on the details of model training, e.g. composition of the dataset, for several different problems ranging from simple toy models to real chemical systems. We present strategies to systematically enhance the LPR, which can be used to improve the robustness, interpretability, and transferability of the resulting atomistic ML models.
This repository contains datasets and Jupyter notebooks employed to corroborate the results of the above study.

en: Robustness of local predictions in atomistic machine learning models

epsrc

display_name: epsrc

erc

display_name: erc

marie-curie-fellowship

display_name: marie-curie-fellowship


en: This data presents the characteristics obtained using extracted parameters of multi- junction solar cells using a single diode triple-junction model. The proposed method combines analytical and numerical techniques to solve a nonlinear equation and determine the parameters, including the ideality factor, series resistance, shunt resistance, photocurrent, and saturation current, using manufacturer data and mathematical equations. The results show that the suggested approach achieves satisfactory performance and provide an accurate representation of the multi-junction circuit's parameters. Overall, this study provides a valuable contribution to the field of solar cell parameter extraction and offers insights for further research in this area.

en: Characteristics data for a new parameters extraction method of InGaP/GaAs/Ge multi-junction solar cell

i-v-characteristics

display_name: i-v-characteristics

multi-junction

display_name: multi-junction

pv-parameter-extraction

display_name: pv-parameter-extraction

solar-energy

display_name: solar-energy


en: We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the probability distribution of the off-diagonal elements of a Wishart matrix, we develop a consistent and unbiased estimator for the Seebeck coefficient, whose statistical uncertainty can be arbitrarily reduced in the long-time limit. We assess the efficacy of our method by benchmarking it against extensive equilibrium molecular dynamics simulations conducted on molten CsF using empirical force fields. We then employ this procedure to calculate the Seebeck coefficient of molten NaCl, KCl and LiCl using neural network force fields trained on ab initio data over a range of pressure-temperature conditions.

en: Seebeck coefficient of ionic conductors from Bayesian regression analysis

bayesian

display_name: bayesian

ionic-conductors

display_name: ionic-conductors

seebeck

display_name: seebeck


materialscloud

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

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


en: This study discloses the effect of hydrogen impurities on the local chemical bonding states and structure of amorphous alumina films by predicting measured Auger parameter shifts using a combination of atomistic and electrostatic modeling. Different amorphous alumina polymorphs with variable H-content and density, as grown by atomic layer deposition, were successfully modeled using a universal machine learning interatomic potential. The annealing of highly defective crystalline hydroxide structures with experimental H-contents at the corresponding atomic layer deposition temperatures led to excellent agreement between theory and experiment in the density and structure of the resulting amorphous alumina polymorphs. The measured Auger parameter shifts of Al cations in such polymorphs were accurately predicted with respect to the H content by assuming that all H atoms are present in the form of hydroxyl ligands in the randomly interconnected 4-fold, 5-fold, and 6-fold nearest-coordination spheres of Al. As revealed by a combination of atomistic and electrostatic modeling, the measured Auger shifts with an increase in the H content and an accompanying decrease in the oxide density depend on the complex correlations between local coordination, bond lengths, bond angles, and ligand type(s) around the core-ionized atoms. Moreover, cryogenic X-ray photoelectron spectroscopy is suggested to offer new insights into the local chemical and structural building blocks of crystalline and amorphous oxides by reducing thermal noise. These findings and fundamental knowledge contribute to advancing the design of e.g. hydrogen oxide barrier films, oxide membranes for H separation, H storage materials, and fuel cells for a hydrogen-based economy.

en: Effect of hydrogen on the local chemical bonding states and structure of amorphous alumina by atomistic and electrostatic modeling of auger parameter shifts

amorphous-oxide

display_name: amorphous-oxide

atomic-layer-deposition-ald

display_name: atomic-layer-deposition-ald

auger-parameter

display_name: auger-parameter

density-functional-theory-dft

display_name: density-functional-theory-dft

electrostatic-modeling

display_name: electrostatic-modeling

hydrogen

display_name: hydrogen

molecular-dynamics-md

display_name: molecular-dynamics-md

x-ray-photoelectron-spectroscopy-xps

display_name: x-ray-photoelectron-spectroscopy-xps


en: The phase change compound Ge₂Sb₂Te₅ (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible transition between the crystalline and amorphous phases induced by Joule heating. The crystallization kinetics of GST225 is a key functional feature for the operation of these devices. We report here on the development of a machine-learned interatomic potential for GST225 that allowed us to perform large scale molecular dynamics simulations (over 10000 atoms for over 100 ns) to uncover the details of the crystallization kinetics in a wide range of temperatures of interest for the programming of the devices. The potential is obtained by fitting with a deep neural network (NN) scheme a large quantum-mechanical database generated within Density Functional Theory. The availability of a highly efficient and yet highly accurate NN potential opens the possibility to simulate phase change materials at the length and time scales of the real devices.

materialscloud

en: Materials Cloud

en: Unraveling the crystallization kinetics of the Ge₂Sb₂Te₅ phase change compound with a machine-learned interatomic potential

crystallization

display_name: crystallization

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

neural-network-potential

display_name: neural-network-potential

phase-change-materials

display_name: phase-change-materials

phase-change-memories

display_name: phase-change-memories


en: The effect of hydrogen doping on the crystal structure and the electronic state in SmNiO₃ is investigated by means of density-functional theory with a combinatorial structure-generation approach. While 100% of hydrogen doping per Ni atom has been supposed to be responsible for the experimentally observed insulating phase, we found that 50% of hydrogen doping results in an outstandingly stable atomic structure showing the insulating property. The stable crystal structure shows the peculiar layered pattern of charge disproportionation of Ni²⁺ and Ni³⁺ valences together with the strong Jahn-Teller distortion that causes the eg orbital state splitting and opens the band gap.

en: Hydrogen-induced insulating state accompanied by inter-layer charge ordering in SmNiO₃

envi

en: Environment

dft

display_name: dft

hydrogen-doping

display_name: hydrogen-doping


en: Fluctuations of electric fields can change the position of a gate-defined quantum dot in a semiconductor heterostructure. In the presence of magnetic field gradient, these stochastic shifts of electron's wavefunction lead to fluctuations of electron's spin splitting. The resulting spin dephasing due to charge noise limits the coherence times of spin qubits in isotopically purified Si/SiGe quantum dots. We investigate the spin splitting noise caused by such process caused by microscopic motion of charges at the semiconductor-oxide interface. We compare effects of isotropic and planar displacement of the charges, and estimate their densities and typical displacement magnitudes that can reproduce experimentally observed spin splitting noise spectra. We predict that for defect density of 10¹⁰ cm⁻², visible correlations between noises in spin splitting and in energy of electron's ground state in the quantum dot, are expected.

en: Correlations of spin splitting and orbital fluctuations due to 1/f charge noise in the Si/SiGe Quantum Dot

charge-noise

display_name: charge-noise

quantum-dots

display_name: quantum-dots

simulations

display_name: simulations


en: Recently a highly ordered Moiré dislocation lattice was identified at the interface between a SrTiO₃ (STO) thin film and the (LaAlO₃)₀.₃(Sr₂TaAlO₆)₀.₇ (LSAT) substrate. A fundamental understanding of the local ionic and electronic structure around the dislocation cores is crucial to further engineer the properties of these complex multifunctional heterostructures. Here we combine experimental characterization via analytical scanning transmission electron microscopy with results of molecular dynamics and density functional theory calculations to gain insights into the structure and defect chemistry of these dislocation arrays. Our results show that these dislocations lead to undercoordinated Ta/Al cations at the dislocation core, where oxygen vacancies can easily be formed, further facilitated by the presence of cation vacancies. The reduced Ti³⁺ observed experimentally at the dislocations by electron energy-loss spectroscopy are a consequence of both the structure of the dislocation itself, as well as of the electron-doping due to oxygen vacancy formation. Finally, the experimentally observed Ti diffusion into LSAT around the dislocation core occurs only together with cation-vacancy formation in LSAT or Ta diffusion into STO.

en: Combined theoretical and experimental study of the Moiré dislocation network at the SrTiO₃-(La,Sr)(Al,Ta)O₃ interface

complex-oxide-interface

display_name: complex-oxide-interface

dislocation

display_name: dislocation

point-defects

display_name: point-defects


en: Magnetostriction drives a rhombohedral distortion in the cubic rock salt antiferromagnet MnO at the Nèel temperature TN=118 K.  As an unexpected consequence we show that this distortion acts to localize the site of an implanted muon due to the accompanying redistribution of electron density. This lifts the degeneracy between equivalent sites, resulting in a single observed muon precession frequency.  Above TN, the muon instead becomes delocalized around a network of equivalent sites.  Our first-principles simulations based on Hubbard-corrected density-functional theory and molecular dynamics are consistent with our experimental data and help to resolve a long-standing puzzle regarding muon data on MnO, as well as having wider applicability to other magnetic oxides.

en: Magnetostriction-driven muon localisation in an antiferromagnetic oxide

dftuv

display_name: dftuv

experimental

display_name: experimental

marvel

display_name: marvel


en: We present an ab initio method to calculate the clamped Pockels tensor of ferroelectric materials from density-functional theory (DFT), the modern theory of polarization exploiting the electric-enthalpy functional, and automated first- and second-order finite-difference derivatives of the polarizations and the Hellmann-Feynman forces. Thanks to the functional-independent capabilities of our approach, we can determine the Pockels tensor of tetragonal barium titanate (BTO) beyond the local density approximation (LDA), with arbitrary exchange-correlation (XC) functionals, for example, PBEsol. The latter, together with RRKJ ultra-soft pseudo-potentials (PP) and a supercell exhibiting local titanium off-centering, enables us to stabilize the negative optical phonon modes encountered in tetragonal BTO when LDA and norm-conserving PP are combined. As a result, the correct value range of r51, the largest experimental Pockels coefficient of BTO, is recovered. We also reveal that r51 increases with decreasing titanium off-centering for this material. The lessons learned from the structural, dielectric, and vibrational investigations of BTO will be essential to design next-generation electro-optical modulators based on the Pockels effect.

en: Ab initio functional-independent calculations of the clamped Pockels tensor of tetragonal barium titanate

ab-initio

display_name: ab-initio

aiida-vibroscopy

display_name: aiida-vibroscopy

barium-titanate

display_name: barium-titanate

density-functional-theory

display_name: density-functional-theory

electro-optics

display_name: electro-optics

imaginary-phonons

display_name: imaginary-phonons

pockels

display_name: pockels


materialscloud

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

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


en: Summing all Feynman diagrams with quantitative accuracy is a holy grail in theoretical physics. In condensed matter, the lattice vibration (phonon) field couples with the electrons, leading to the formation of entangled electron-phonon (e-ph) states called polarons.  In the intermediate- and strong-coupling regimes common to many conventional and quantum materials, a many-body treatment of polarons requires adding up a large number of e-ph diagrams. Diagrammatic Monte Carlo (DMC) is an efficient method for diagram summation and has been employed to study polarons within simplified \e-ph models (Holstein, Frohlich, etc.).  Here we show DMC calculations based on accurate first-principles e-ph interactions, enabling numerically exact results for ground-state and dynamical properties of polarons in real materials, including the polaron formation energy, effective mass, spectral weight, phonon cloud distribution, optical conductivity, and mobility. We demonstrate such DMC calculations in systems with polarons ranging from small (localized) to large (delocalized), including LiF, SrTiO3, and TiO2 rutile and anatase. This advance is enabled by our recently developed technique for compressing first-principles e-ph interaction matrices~\cite{SVD-paper}, together with a matrix-product formalism that mitigates the DMC sign problem from multiple electronic bands. Our work enables precise modeling of e-ph interactions and polarons in coupling regimes ranging from weak to strong, opening doors to studies of transport, linear response, and superconductivity in the strong e-ph coupling regime.

en: First-principles diagrammatic Monte Carlo for electron-phonon interactions and polarons

diagrammatic-monte-carlo

display_name: diagrammatic-monte-carlo

first-principles

display_name: first-principles

lif

display_name: lif

many-body-physics

display_name: many-body-physics

polaron

display_name: polaron

strong-e-ph-interactions

display_name: strong-e-ph-interactions


en: Summing all Feynman diagrams with quantitative accuracy is a holy grail in theoretical physics. In condensed matter, the lattice vibration (phonon) field couples with the electrons, leading to the formation of entangled electron-phonon (e-ph) states called polarons.  In the intermediate- and strong-coupling regimes common to many conventional and quantum materials, a many-body treatment of polarons requires adding up a large number of e-ph diagrams. Diagrammatic Monte Carlo (DMC) is an efficient method for diagram summation and has been employed to study polarons within simplified e-ph models (Holstein, Frohlich, etc.).  Here we show DMC calculations based on accurate first-principles e-ph interactions, enabling numerically exact results for ground-state and dynamical properties of polarons in real materials, including the polaron formation energy, effective mass, spectral weight, phonon cloud distribution, optical conductivity, and mobility. We demonstrate such DMC calculations in systems with polarons ranging from small (localized) to large (delocalized), including LiF, SrTiO3, and TiO2 rutile and anatase. This advance is enabled by our recently developed technique for compressing first-principles e-ph interaction matrices, together with a matrix-product formalism that mitigates the DMC sign problem from multiple electronic bands. Our work enables precise modeling of e-ph interactions and polarons in coupling regimes ranging from weak to strong, opening doors to studies of transport, linear response, and superconductivity in the strong e-ph coupling regime.

en: Dataset for first-principles diagrammatic Monte Carlo for electron-phonon interactions and polaron


materialscloud

en: Materials Cloud


en: Calcium carbonate (CaCO₃) plays a crucial role in the global carbon cycle, and its phase diagram is of significant scientific interest. We used molecular dynamics to investigate selected structural phase transitions of calcium carbonate. Using the Raiteri potential, we explored the structural transitions occurring at the constant pressure of 1 bar, with temperatures ranging from 300 to 2500 K, and at the constant temperature of 1600 K, with pressures ranging from 0 to 13 GPa. With increasing temperature, the transitions between calcite, CaCO₃-IV, and CaCO₃-V were characterized. In the calcite structure, the carbonate ions are ordered in a planar triangular arrangement, alternating with layers of calcium ions. As the temperature increases, the transition from calcite to CaCO₃-IV occurs, leading to partial disordering of the carbonate ions. At higher temperatures, CaCO₃-IV transforms into CaCO₃-V. Through free energy analysis, we classified the latter transition as a continuous phase transition. At a temperature of 2000 K, a 'disordered CaCO₃' structure appears, characterized by low order within the calcium and carbonate sublattices and the free rotation of the carbonate ions. With increasing pressure, two calcium carbonate transformations were observed. At P = 2 GPa, the CaCO₃-V phase undergoes a phase transition into CaCO₃-IV, demonstrating that the model can describe the transition between these two phases as pressure- and temperature-driven. At P = 4.25 GPa, CaCO₃-IV undergoes a phase transition into the CaCO₃-Vb phase. This transition is classified as first-order based on free energy calculations.

en: Structural transitions of calcium carbonate by molecular dynamics simulation

calcium-carbonate

display_name: calcium-carbonate

high-pressure

display_name: high-pressure

high-temperature

display_name: high-temperature

metadynamics

display_name: metadynamics

molecular-dynamics

display_name: molecular-dynamics

phase-transitions

display_name: phase-transitions


envi

en: Environment


en: The presence of defects, such as copper and oxygen vacancies, in cuprous oxide films determines their characteristic carrier conductivity and consequently their application as semiconducting systems. There are still open questions on the induced electronic re-distribution, including the formation of polarons. Indeed, to accurately reproduce the structural and electronic properties at the cuprous oxide surface, very large slab models and theoretical approaches that go beyond the standard generalized gradient corrected density functional theory are needed. In this work we investigate oxygen vacancies formed in proximity of a reconstructed Cu₂O(111) surface, where the outermost unsaturated copper atoms are removed, thus forming non-stoichiometric surface layers with copper vacancies. We address simultaneously surface and bulk properties by modelling a thick and symmetric slab, to find that hybrid exchange-correlation functionals are needed to describe the oxygen vacancy in this system. Our simulations show that the formation of oxygen vacancies is favoured in the sub-surface layer. Moreover, the oxygen vacancy leads to a splitting and left-shift of the shallow hole states in the gap, which are associated with the deficiency of copper at the surface. These findings suggest that surface electronic structure and reactivity are sensitive to the presence of oxygen vacancies, also when the latter are formed deeper within the film.

en: Understanding the role of oxygen-vacancy defects in Cu₂O(111) from first-principle calculations

hybrid-dft

display_name: hybrid-dft

oxygen-vacancy-defects

display_name: oxygen-vacancy-defects


en: High-performance n-type organic electrochemical transistors (OECTs) are essential for logic circuits and sensors. However, the performances of n-type OECTs lag far behind that of p-type ones. Conventional wisdom posits that the LUMO energy level dictates the n-type performance. Herein, we show that engineering the doped state is more critical for n-type OECT polymers. By balancing more charges to the donor moiety, we could effectively switch a p-type polymer to high-performance n-type material. Based on this concept, the polymer, P(gTDPP2FT), exhibits a record high n-type OECT performance with μC* of 54.8 F cm⁻¹ V⁻¹ s⁻¹, mobility of 0.35 cm² V⁻¹ s⁻¹, and response speed of τon/τoff = 1.75/0.15 ms. Calculations and comparison studies show that the conversion is primarily due to the more uniform charges, stabilized negative polaron, enhanced conformation, and backbone planarity at negatively charged states. Our work highlights the critical role of understanding and engineering polymers' doped states.

en: Switching p-type to high-performance n-type organic electrochemical transistors via doped state engineering

doped-state-engineering

display_name: doped-state-engineering

n-type

display_name: n-type

organic-electrochemical-transistor

display_name: organic-electrochemical-transistor


en: The electronic and vibrational properties and electron-phonon couplings of one-dimensional materials will be key to many prospective applications in nanotechnology. Dimensionality strongly affects these properties and has to be correctly accounted for in first-principles calculations. Here we develop and implement a formulation of density-functional and density-functional perturbation theory that is tailored for one-dimensional systems. A key ingredient is the inclusion of a Coulomb cutoff, a reciprocal-space technique designed to correct for the spurious interactions between periodic images in periodic-boundary conditions. This restores the proper one-dimensional open-boundary conditions, letting the true response of the isolated one-dimensional system emerge. In addition to total energies, forces and stress tensors, phonons and electron-phonon interactions are also properly accounted for. We demonstrate the relevance of the present method on a portfolio of realistic systems: BN atomic chains, BN armchair nanotubes, and GaAs nanowires. Notably, we highlight the critical role of the Coulomb cutoff by studying previously inaccessible polar-optical phonons and Fröhlich electron-phonon couplings. We also develop and apply analytical models to support the physical insights derived from the calculations and we discuss their consequences on electronic lifetimes. The present work unlocks the possibility to accurately simulate the linear response properties of one-dimensional systems, sheds light on the transition between dimensionalities and paves the way for further studies in several fields, including charge transport, optical coupling and polaritronics.

en: Density functional perturbation theory for one-dimensional systems: implementation and relevance for phonons and electron-phonon interactions

ab-initio

display_name: ab-initio

charge-transport

display_name: charge-transport

density-functional-theory

display_name: density-functional-theory

electron-phonon-coupling

display_name: electron-phonon-coupling

marvel

display_name: marvel

one-dimensional-materials

display_name: one-dimensional-materials

phonons

display_name: phonons


en: Photocatalytic conversion of CO₂ into fuel feed stocks is a promising method for sustainable fuel production. A highly attractive class of materials, inorganic-core@metal–organic-framework heterogeneous catalysts, boasts a significant increase in catalytic performance when compared to the individual materials. However, due to the ever-expanding chemical space of inorganic-core catalysts and metal–organic frameworks (MOFs), identification of these optimal heterojunctions is difficult without appropriate computational screening. In this work, a novel high-throughput screening method of nano-hybrid photocatalysts is presented by screening 65'784 inorganic-core materials and 20'375 MOF-shells for their ability to reduce CO₂ based on their synthesizability, aqueous stability, visible light absorption, and electronic structure; the passing materials were then paired based on their electronic structure to create novel heterojunctions. The results showed 58 suitable inorganic-core materials and 204 suitable MOFs ranging from never-beforesynthesized catalysts to catalysts that have been overlooked for their photocatalytic ability. These materials lay a new foundation of photocatalysts that have passed theoretical requirements and can significantly increase the rate of catalyst discovery.

en: High-throughput screening of nano-hybrid metal–organic-frameworks for photocatalytic CO₂ reduction

coreshell

display_name: coreshell

metal-organic-frameworks

display_name: metal-organic-frameworks

photocatalyst

display_name: photocatalyst


materialscloud

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

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


en:
Using high-throughput automation of ab-initio impurity-embedding simulations we created a database of 3d and 4d transition metal defects embedded into the prototypical topological insulator (TI) Bi₂Te₃. We simulate both single impurities as well as impurity dimers at different impurity-impurity distances inside the topological insulator matrix. We extract changes to magnetic moments, analyze the polarizability of non-magnetic impurity atoms via nearby magnetic impurity atoms and calculate the exchange coupling constants for a Heisenberg Hamiltonian.
We uncover chemical trends in the exchange coupling constants and discuss the impurities' potential with respect to magnetic order in the fields of quantum anomalous Hall insulators. In particular, we predict that co-doping of different magnetic dopants is a viable strategy to engineer the magnetic ground state in magnetic TIs.

en: High-throughput magnetic co-doping and design of exchange interactions in a topological insulator

co-doping

display_name: co-doping

dft

display_name: dft

jukkr

display_name: jukkr

magnetic-doping

display_name: magnetic-doping

quantum-anomalous-hall

display_name: quantum-anomalous-hall

topological-insulator

display_name: topological-insulator

topological-materials

display_name: topological-materials


en: Dislocation-grain boundary play a major role in the strength and ductility of structural materials. An understanding of governing parameters such as grain boundary local atomic structures on the outcome of this interaction can accelerate new alloy design strategies that tailors materials' microstructures. Previous studies have focused only on the subset of minimum-energy grain boundary structures. We present a comprehensive database of dislocation-grain boundary interaction for edge, screw, and 60o mixed dislocation with 449 < 110 > and < 112 > symmetric tilt grain boundary in FCC Cu consisting of 67 minimum-energy grain boundary structures and 389 metastable ones. The dataset contains the outcomes for 5593 unique interactions with a particular dislocation type, grain boundary structure, and applied shear stress.

en: Dislocation-grain boundary interaction dataset for FCC Cu

dislocation-grain-boundary-interaction

display_name: dislocation-grain-boundary-interaction

grain-boundary-structure

display_name: grain-boundary-structure

material-science

display_name: material-science


en: The deposited structure models of DNA adsorbed on prisitne PG/p-Si and metal (Au/W) doped PG/p-Si sheets, have been subjected to first principles calculations  based on DFT (PBE+vdW). The calculated binding affinity on PG and p-Si surface by M062X/6-31G* level of theory and adsorption energies by DFT predicts that PG has higher sensitivity towards DNA nucleic bases compared to p-Si with evident changes in their band structure properties.

materialscloud

en: Materials Cloud

en: 2D pentagonal-graphene and pentagonal-silicene sheets engineered for the detection of DNA nucleobases for genetic biomarker: A DFT study

electronic-structure

display_name: electronic-structure


envi

en: Environment


en: We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud 3-dimensional database (MC3D) of experimentally known inorganic stoichiometric compounds. We first compute the Allen-Dynes critical temperature Tc for 4533 non-magnetic metals using a direct and progressively finer sampling of the electron-phonon couplings. For the candidates with the largest Tc, we use automated Wannierizations and electron-phonon interpolations to obtain a high-quality dataset for the most promising 240 dynamically stable structures, for which we calculate spectral functions, superconducting bandgaps, and isotropic Migdal-Eliashberg critical temperatures. For 110 of these, we also provide anisotropic Migdal-Eliashberg superconducting gaps and critical temperatures. The approach is remarkably successful in finding known superconductors, and we find 24 unknown ones with a predicted anisotropic Tc above 10 K. Among them, we identify a possible double gap superconductor (p-doped BaB2), a non-magnetic half-Heusler ZrRuSb, and the perovskite TaRu3C, all exhibiting significant Tc. Finally, we introduce a sensitivity analysis to estimate the robustness of the predictions.

en: Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

migdal-eliashberg

display_name: migdal-eliashberg

superconductivity

display_name: superconductivity


en:
Using high-throughput automation of ab-initio impurity-embedding simulations we created a database of 3d and 4d transition metal defects embedded into the prototypical topological insulators (TIs) Bi₂Te₃ and Bi₂Se₃. We simulate both single impurities as well as impurity dimers at different impurity-impurity distances inside the topological insulator matrix. We extract changes to magnetic moments, analyze the polarizability of non-magnetic impurity atoms via nearby magnetic impurity atoms and calculate the exchange coupling constants for a Heisenberg Hamiltonian.
We uncover chemical trends in the exchange coupling constants and discuss the impurities' potential with respect to magnetic order in the fields of quantum anomalous Hall insulators. In particular, we predict that co-doping of different magnetic dopants is a viable strategy to engineer the magnetic ground state in magnetic TIs.

en: High-throughput magnetic co-doping and design of exchange interactions in topological insulators


en: Recently, we introduced a class of molecular representations for kernel-based regression methods — the spectrum of approximated Hamiltonian matrices (SPAᴴM) — that takes advantage of lightweight one-electron Hamiltonians traditionally used as an SCF initial guess. The original SPAᴴM variant is built from occupied-orbital energies (\ie, eigenvalues) and naturally contains all the information about nuclear charges, atomic positions, and symmetry requirements. Its advantages were demonstrated on datasets featuring a wide variation of charge and spin, for which traditional structure-based representations commonly fail. SPAᴴM(a,b), as introduced here, expands eigenvalue SPAᴴM into local and transferable representations. It relies upon one-electron density matrices to build fingerprints from atomic or bond density overlap contributions inspired from preceding state-of-the-art representations. The performance and efficiency of SPAᴴM(a,b) is assessed on the predictions for datasets of prototypical organic molecules (QM7) of different charges and azoheteroarene dyes in an excited state. Overall, both SPAᴴM(a) and SPAᴴM(b) outperform state-of-the-art representations on difficult prediction tasks such as the atomic properties of charged open-shell species and of π-conjugated systems.

en: SPAᴴM(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations

epfl

display_name: epfl

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: We present a density functional theory study of the oxygen reduction reaction (ORR) on a single atom catalyst embedded in graphene, namely, TM-N₄-C (TM = Fe and Co), using the effective screening medium method combined with the reference interaction site model (ESM-RISM). It was found that Fe-N₄-C and Co-N₄-C show comparable ORR activities from the constant electrode potential simulations, in contrast to the results obtained using the constant (neutral) charge simulation, in which the superior performance of Co-N₄-C has been predicted. The constant potential method allows the variable charge and thus, resulting in a potential dependence of the reaction free energies different from that with the constant charge method in which the potential dependence is included as an ad hoc manner. We suggest the importance of the variable charge in the simulation of the electrochemical reaction, which is enabled by ESM-RISM.

en: Oxygen reduction reaction on single-atom catalysts from density functional theory calculations combined with an implicit solvation model

density-functional-theory

display_name: density-functional-theory

implicit-solvation-model

display_name: implicit-solvation-model

oxygen-reduction-reaction

display_name: oxygen-reduction-reaction

single-atom-catalyst

display_name: single-atom-catalyst


materialscloud

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

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


en: We present a comprehensive vibrational analysis of hydrogen boride sheet through first-principles calculations, focusing on a variety of local structural configurations including surfaces, edges, and bilayer models - some of which contains vacancy and substitutional defects. A database of vibrational modes and simulated infrared spectra was constructed, revealing distinct spectral features that serve as characteristic fingerprints for different bonding environments. By analyzing mode distributions with respect to coordination environments, we identify the distinct contributions of terminal and bridging hydrogen atoms to specific spectral regions. The results show that structural modifications, such as interlayer stacking and edge termination, have a significant influence on vibrational characteristics, particularly in the intermediate and high frequency regions. The vibrational database enables the precise interpretation of infrared spectra and offers a reliable reference for model selection and peak analysis in both experimental and theoretical studies on hydrogen boride sheet and related materials. This archive contains the database compiled from this study, comprising (i) optimized structure files, (ii) energy and force datasets used to construct the Hessian matrix and (iii) the results from the vibrational analytics, such as simulated IR spectra and mode visualizations. The database was designed for maximum accessibility; consequently, all data are supplied in multiple standard formats.

en: Dataset for vibrational recognition of local structures in hydrogen boride sheets

hydrogen-boride-sheets

display_name: hydrogen-boride-sheets

spectroscopy

display_name: spectroscopy

two-dimensional

display_name: two-dimensional


en:
Lithium ortho-thiophosphate (Li₃PS₄) has emerged as a promising candidate for solid-state-electrolyte batteries, thanks to its highly conductive phases, cheap components, and large electrochemical stability range. Nonetheless, the microscopic mechanisms of Li-ion transport in Li₃PS₄ are far to be fully understood, the role of PS₄ dynamics in charge transport still being controversial. We build machine learning potentials targeting state-of-the-art DFT references (PBEsol, r²SCAN, and PBE0) to tackle this problem in all known phases of Li₃PS₄ (α, β and γ), for large system sizes and timescales. We discuss the physical origin of the observed superionic behavior of Li₃PS₄: the activation of PS₄ flipping drives a structural transition to a highly conductive phase, characterized by an increase of Li-site availability and by a drastic reduction in the activation energy of Li-ion diffusion. We also rule out any paddle-wheel effects of PS₄ tetrahedra in the superionic phases–previously claimed to enhance Li-ion diffusion–due to the orders-of-magnitude difference between the rate of PS₄ flips and Li-ion hops at all temperatures below melting.
This archive provides all the relevant data and input files that were used to fit the ML interatomic potentials used in this work, along with the relevant Density-Functional Theory calculations that were used for the training set construction, the validation of the ML models and the calculation of the electronic band structure of the β and γ structure. Furthermore, it provides input files of all the molecular dynamics trajectories needed to investigate Li-ion diffusion properties of Li₃PS₄ and the rotational dynamics of PS₄ tetrahedra. Finally, it provides the raw data to reproduce the figures of the manuscript associated with this archive.

en: Mechanism of charge transport in lithium thiophosphate

electrical-conductivity

display_name: electrical-conductivity

green-kubo-time-correlation-functions

display_name: green-kubo-time-correlation-functions

lithium-ion-diffusion

display_name: lithium-ion-diffusion

lithium-thiophosphate

display_name: lithium-thiophosphate

marvel

display_name: marvel

molecular-dynamics

display_name: molecular-dynamics

phase-transitions

display_name: phase-transitions

post-gga-density-functional-theory

display_name: post-gga-density-functional-theory

snsf-sinergia

display_name: snsf-sinergia


en:
Our study reveals how Gd adatoms and dimers on a superconducting Nb(110) surface induce Yu-Shiba-Rusinov (YSR) states, offering valuable insights into magnetic interactions of rare-earth atoms on superconducting surfaces. By engineering Gd dimers along the [1-10] and [001] directions, we uncover an indirect coupling between the Gd magnetic moments and the Nb substrate via their valence d electrons, leading to significant alterations in the YSR spectrum around the dimers. We further demonstrate the possibility for Néel-type spin-spiral ground states in chains of Gd atoms on Nb(110). These findings highlight the potential of 4f elements like Gd as a promising platform for controlling a spin-spiral ground state, a crucial prerequisite for realizing a topological superconductor that can host Majorana zero modes. The combination of theoretical modeling based on density functional theory, atomistic spin-dynamics simulations and experimental techniques, including scanning tunneling microscopy and spectroscopy, provides a comprehensive understanding of the coupling mechanisms and their impact on the electronic properties of these systems and establishes rare-earth magnets on Nb as a promising platform in the field.

This dataset collects the experimental and theoretical results of this work.

en: Rare-earth atoms on Nb(110) as a platform to engineer topological superconductivity

adatoms

display_name: adatoms

dftu

display_name: dftu

magnetism

display_name: magnetism

majorana-zero-mode

display_name: majorana-zero-mode

rare-earth

display_name: rare-earth

spin-dynamics

display_name: spin-dynamics

spin-spiral

display_name: spin-spiral

stm

display_name: stm

superconductivity

display_name: superconductivity

topological-materials

display_name: topological-materials

topological-superconductor

display_name: topological-superconductor


materialscloud

en: Materials Cloud


envi

en: Environment

electrical-conductivity

display_name: electrical-conductivity

green-kubo-time-correlation-functions

display_name: green-kubo-time-correlation-functions

lithium-ion-diffusion

display_name: lithium-ion-diffusion

lithium-thiophosphate

display_name: lithium-thiophosphate

machine-learning

display_name: machine-learning


marvel

display_name: marvel

molecular-dynamics

display_name: molecular-dynamics

phase-transitions

display_name: phase-transitions

post-gga-density-functional-theory

display_name: post-gga-density-functional-theory

snsf-sinergia

display_name: snsf-sinergia


en:
Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time-step integration. These NNPs serve to explore a puzzling solute--solvent reactivity route involving the ring opening of N-enoxyphthalimide experimentally observed in methanol but not in 2,2,2-trifluoroethanol (TFE). This reaction represents a challenging example characterized by intricate hydrogen bonding networks and structurally ambiguous solvent-sensitive transition states. The methodology successfully delivers detailed free energy surfaces and relative energy barriers in quantitative agreement with experiment. These barriers are associated with an ensemble of transition states involving direct participation of up to five solvent molecules. While this picture contrasts with the single transition state structure assumed by current static models, no drastic qualitative difference is observed between the formed hydrogen bonding networks and the number of participating solvent molecules in methanol or TFE. The dichotomy between the two solvents thus essentially arises from an electronic effect (i.e., distinct nucleophilicity) and from the larger conformational entropy contributions in methanol. This example  underscores the critical role dynamic simulations at the ab initio levels play in capturing the full complexity of solute-solvent interactions.

The files used in our studies are listed below, ensuring reproducibility and providing resources for future studies related to this work.

en: Capturing dichotomic solvent behavior in solute–solvent reactions with neural network potentials

erc

display_name: erc

free-energy

display_name: free-energy

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


en: Electronic band structures is a cornerstone of condensed matter physics and materials science. Conventional methods like Wannier interpolation (WI), which are commonly used to interpolate band structures onto dense k-point grids, often encounter difficulties with complex systems, such as those involving entangled bands or topological obstructions. In this work, we introduce the Hamiltonian transformation (HT) method, a novel framework that directly enhances interpolation accuracy by localizing the Hamiltonian. Using a pre-optimized transformation, HT produces a far more localized Hamiltonian than WI, achieving up to two orders of magnitude greater accuracy for entangled bands. Although HT utilizes a slightly larger, nonlocal numerical basis set, its construction is rapid and requires no optimization, resulting in significant computational speedups. These features make HT a more precise, efficient, and robust alternative to WI for band structure interpolation, as further verified by high-throughput calculations.

en: Hamiltonian transformation for accurate and efficient band structure interpolation

band-structure-interpolation

display_name: band-structure-interpolation

hamiltonian-transformation

display_name: hamiltonian-transformation

localized-hamiltonian

display_name: localized-hamiltonian


en: Comprehending the structure and dynamics of water is crucial in various fields such as water desalination, ion separation, electrocatalysis, and biochemical processes. While reported works show that the ab-initio molecular dynamics (AIMD) can accu- rately portray water's structure, the artificial high temperature (AHT) from 120K to 30K is needed to mimic the quantum nature of hydrogen-bond network from GGA, metaGGA to hybrid functionals. The AHT proves to be an inadequate approach for systems involving aqueous multiphase mixtures, such as water-solid interfaces and aque- ous solutions. This is due to the activation of additional phonons in other phases, which can lead to an overestimation of the dynamics for nearby water molecules. In this work, we find the regularized SCAN (rSCAN) functional can well capture both the structure and dynamics of liquid water at ambient conditions without AHT. Moreover, rSCAN can well match the experimental results of hydration structures for alkali, alkali earth and halide ions. We anticipate that the versatile and accurate rSCAN functional will emerge as a key tool based on ab-initio simulation for investigating chemical processes in aqueous environments.

en: Ab-initio simulation of liquid water without artificial high temperature

aimd

display_name: aimd

density-functional-theory

display_name: density-functional-theory

machine-learning-potential

display_name: machine-learning-potential

metagga

display_name: metagga

water

display_name: water


en:
Two-dimensional (2D) materials have attracted considerable attention due to their remarkable electronic, mechanical and optical properties, making them prime candidates for next-generation electronic and optoelectronic applications. Despite their widespread use in combination with substrates in practical applications, including the fabrication process and final device assembly, computational studies often neglect the effects of substrate interactions for simplicity.
In this record, we provide the results of the computational study of the stable 2D molybdenum-sulfur (Mo-S) structures on a c-cut sapphire (Al₂O₃). In particular, we provide the results of the evolutionary search in the Mo-S / Al₂O₃ (0001) system, the machine learning interatomic potential (MLIP) used for local relaxation of the systems during the evolutionary search together with its training set, post-processing data on electronic and phonon band structures of the stable 2D Mo-S structures, and the predicted stability patterns from the perspective of CVD synthesis.

en: Substrate-aware computational design of two-dimensional materials

crystal-structure-prediction

display_name: crystal-structure-prediction

machine-learning

display_name: machine-learning


materialscloud

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

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


en: Understanding electronic interactions in high-temperature superconductors is an outstanding challenge. In the widely studied cuprate materials, experimental evidence points to strong electron-phonon (e-ph) coupling and broad photoemission spectra. Yet, the microscopic origin of this behavior is not fully understood. Here we study e-ph interactions and polarons in a prototypical parent (undoped) cuprate, La₂CuO₄ (LCO), by means of first-principles calculations. Leveraging parameter-free Hubbard-corrected density functional theory, we obtain a ground state with band gap and Cu magnetic moment in nearly exact agreement with experiments. This enables a quantitative characterization of e-ph interactions. Our calculations reveal two classes of longitudinal optical (LO) phonons with strong e-ph coupling to hole states. These modes consist of Cu-O plane bond-stretching and bond-bending as well as vibrations of apical O atoms. The hole spectral functions, obtained with a cumulant method that can capture strong e-ph coupling, exhibit broad quasiparticle peaks with a small spectral weight (Z≈0.25) and pronounced LO-phonon sidebands characteristic of polaron effects. Our calculations predict features observed in photoemission spectra, including a 40-meV peak in the e-ph coupling distribution function not explained by existing models. These results show that the universal strong e-ph coupling found experimentally in lanthanum cuprates is an intrinsic feature of the parent compound, and elucidates its microscopic origin.

en: First-principles electron-phonon interactions and polarons in the parent cuprate La₂CuO₄

dftu

display_name: dftu

electron-phonon-coupling

display_name: electron-phonon-coupling

hubbard-u

display_name: hubbard-u

marvel

display_name: marvel

parent-cuprate

display_name: parent-cuprate

phonons

display_name: phonons

polarons

display_name: polarons

spectral-function

display_name: spectral-function