en: We analyze the effects on the structural and electronic properties of vanadium dioxide (VO₂) of adding an empirical inter-atomic potential within the density-functional theory+V (DFT+V) framework. We use the DFT+V machinery founded on the extended Hubbard model to apply an empirical self-energy correction between nearest-neighbor vanadium atoms in both rutile and monoclinic phases, and for a set of structures interpolating between these two cases. We observe that imposing an explicit intersite interaction V along the vanadium-vanadium chains enhances the characteristic bonding-antibonding splitting of the relevant bands in the monoclinic phase, thus favoring electronic dimerization and the formation of a band gap. We then explore the effect of V on the structural properties and the relative energies of the two phases, finding an insulating global energy minimum for the monoclinic phase, consistent with experimental observations. With increasing V, this minimum becomes deeper relative to the rutile structure, and the transition from the metallic to the insulating state becomes sharper. We also analyze the effect of applying the +V correction either to all or only to selected vanadium-vanadium pairs, and both in the monoclinic as well as in the metallic rutile phase. Our results suggest that  DFT+V can indeed serve as a computationally inexpensive unbiased way of modeling VO₂ which is well suited for studies that, e.g., require large system sizes.

en: Incorporating static intersite correlation effects in vanadium dioxide through DFT+V

dftuv

display_name: dftuv


$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

dft

display_name: dft


en: In the field of orthopedic biomechanics, synthetic bone surrogates are essential for evaluating surgical techniques, implant stability, and fracture risks due to their reproducible geometry and consistent mechanical behavior compared to human cadaveric tissue. Fourth-generation (4th Gen) Sawbones(R) composite models, which utilize a short fiber-filled epoxy (SFFE) cortical shell and a polyurethane foam (PUF) cancellous core, represent the industry standard. However, a significant gap remains in the literature regarding the full anisotropic characterization of these individual constituents, which is critical for high-fidelity numerical simulations. This study presents a comprehensive experimental dataset covering uniaxial tension (ASTM D638), compression (ASTM D695), and fracture toughness (ASTM E399) tests, alongside specialized shear characterization using a modified Single Cube Apparatus (SCA). The dataset includes 93 successful mechanical tests: 56 for the SFFE cortical surrogate and 37 for the PUF cancellous surrogate. The results validate the transverse isotropy of the SFFE and the isotropic behavior of the PUF. These findings provide the required mechanical properties for accurate finite element studies, facilitating the rigorous validation of synthetic bone surrogates as representations of biological human tissue.

en: Dataset for characterization of fracture and elastic properties of commercially available polyurethane foam and short fiber filled epoxy for bone models

anisotropy

display_name: anisotropy

bone-substitutes

display_name: bone-substitutes

characterization

display_name: characterization

experimental

display_name: experimental

fracture-toughness

display_name: fracture-toughness

sawbones

display_name: sawbones


en: Strain soliton networks strongly influence the structural and electronic properties of heterodeformed bilayer systems, yet their design remains challenging due to the high dimensionality of heterodeformation space and the absence of a direct map between deformation and network geometry. In this work, we introduce a geometric framework that establishes a one-to-one mapping between heterodeformations and the geometry of the strain soliton network expressed as line vector-Burgers vector pairs. The admissible networks are constrained by topology dictated by the generalized stacking fault energy landscape. We show that the moiré Bravais lattice, corresponding to a uniform heterodeformation, alone is insufficient to characterize the interface: distinct heterodeformations can share identical moiré Bravais lattices while producing different soliton networks, reflecting an inherent many-to-one mapping when only translational symmetry is considered. In contrast, the soliton network encodes the full multilattice geometry of the interface, including topology and connectivity, which are not captured by the moiré Bravais lattice alone. The proposed framework enables the direct construction of heterodeformations from target networks, providing a systematic route for inverse design of moiré interfaces beyond conventional twist-based approaches.

en: Inverse design of heterodeformations for strain soliton networks in bilayer 2D materials

heterodeformations

display_name: heterodeformations

inverse-design

display_name: inverse-design

strain-soliton-networks

display_name: strain-soliton-networks


en: Deep Generative models have shown impressive capabilities in several applications, e.g., image, video, and audio synthesis. Importantly, they can infer probability distributions from data implicitly. For this reason, they have promising applications in learning stochastic dynamics, with potential applications in computational condensed matter physics and materials science. Indeed, random thermal fluctuations have a key role in several phenomena, such as surface roughening, nucleation, crystal growth, and phase transitions. Here we provide a comprehensive dataset of trajectories of one of such systems, i.e., the progressive roughening of monoatomic steps on top of a simple cubic (100) surface. The system has been simulated with a Kinetic Monte Carlo approach, implementing the edge diffusion conditions. Among other characteristics, this system is convenient because its dynamics can be represented as a series of binary images, with white and black pixels corresponding to occupied and unoccupied surface sites, respectively. The repository contains 3 KMC datasets composed of 300 independent trajectories each. Every trajectory contains 1000 subsequent states. The datasets differ in the initial condition (one flat stripe and one undulated one on a 64x64 computational cell) and the domain size (one undulated stripe on a 72x64 computational cell). Evolutions predicted by a Generative Adversarial Network approach trained on the flat stripe dataset are also provided, serving as a benchmark for comparing alternative implementations on the same problem. These comprise the evolution of flat profiles on domains of different lengths (32, 48, 64, 72, 80, 96, and 128) to the equilibrium value and relaxation dynamics from undulated profiles conformal to those used in the KMC datasets.

en: A kinetic Monte Carlo dataset of monoatomic step roughening for deep generative models approximation of stochastic dynamics

deep-learning

display_name: deep-learning

edge-diffusion

display_name: edge-diffusion

generative-adversarial-network

display_name: generative-adversarial-network

kinetic-monte-carlo

display_name: kinetic-monte-carlo

neural-networks

display_name: neural-networks

roughness

display_name: roughness

stochastic-dynamics

display_name: stochastic-dynamics

surface-science

display_name: surface-science


en:
Machine Learning (ML) can be conveniently applied to continuum materials simulations, allowing for the investigation of larger systems and longer timescales, pushing the limits of tractable systems. Here we provide a comprehensive dataset of strained Ge films on Si and their corresponding strain states, which can be used to train a ML model capable of such acceleration. Approximately 80k 2D cases are included, reporting the profiles h(x) and the corresponding elastic energy densities and strain fields. The profiles are conveniently sampled using Perlin-noise and pure-sine waves. A 100nm-large computational domain is considered. The mechanical equilibrium problem is solved using Finite Element Method (FEM). Ge is modeled as an isotropic material and an eigenstrain of 3.99% is used, as in Ge/Si(001).
The database has been exploited for training a (fully) Convolutional Neural Network (CNN) which maps the free surface profile h(x) to the corresponding energy density. If plugged into the proper time-dependent Partial Differential Equation, this term can be used to accelerate continuum simulations of the morphological evolution of strained films while retaining FEM-level accuracy. Tests of the reliability of such CNN model are also provided in the repository, together with the output of surface morphology minimization procedures and morphological evolution simulations during coarsening and growth. In the latter, evolution by surface diffusion has been considered as an important case, but applications to other mechanisms are possible. Generalization examples to larger computational cells with respect to those in the dataset are also available.

en: A FEM dataset of Ge film profiles and elastic energies for machine learning approximation of strain state and morphological evolution

continuum-models

display_name: continuum-models

convolutional-neural-network

display_name: convolutional-neural-network

elastic-energy

display_name: elastic-energy

finite-element-method

display_name: finite-element-method

germanium

display_name: germanium

machine-learning

display_name: machine-learning

strained-films

display_name: strained-films

surface-evolution

display_name: surface-evolution

thin-films

display_name: thin-films


en: Crafting neural-network interatomic potentials (NNIPs) remains a complex task, demanding specialized expertise in both machine learning and electronic-structure calculations. Here, we introduce AiiDA-TrainsPot, an automated, open-source, and user-friendly workflow that streamlines the creation of accurate NNIPs by orchestrating density-functional-theory calculations, data augmentation strategies, and classical molecular dynamics.Our active-learning strategy leverages on-the-fly calibration of committee disagreement against ab initio reference errors to ensure reliable uncertainty estimates. We use electronic-structure descriptors and dimensionality reduction to analyze the efficiency of this calibrated criterion, and show that it minimizes both false positives and false negatives when deciding what to compute from first principles. AiiDA-TrainsPot has a modular design that supports multiple NNIP backends, enabling both the training of NNIPs from scratch and the fine-tuning of foundation models. We demonstrate its capabilities through automated training campaigns targeting pristine and defective carbon allotropes, including amorphous carbon, as well as structural phase transitions in monolayer WxMo1-xTe2 alloys.

en: AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials

active-learning

display_name: active-learning

aiida

display_name: aiida

aiida-trainspot

display_name: aiida-trainspot

carbon-allotropes

display_name: carbon-allotropes

mace

display_name: mace

neural-network-interatomic-potentials

display_name: neural-network-interatomic-potentials

uncertainty-quantification

display_name: uncertainty-quantification


en: We present a combined density-functional theory and dynamical mean-field theory (DFT+DMFT) study of the full structural phase space of rutile-based vanadium dioxide (VO2), including also the less studied M2 and T phases, using an unconventional bond-centered orbital basis. The use of bond-centered orbitals allows us to treat all main phases of VO2, and the structural transitions between them, using one consistent approach with moderate computational cost and without pre-pattering of the structure into dimerized and undimerized V--V pairs. We obtain two distinct insulating states on the two different types of vanadium chains in the M2 phase, a singlet-insulator on the dimerized chains and a Mott-insulator on the zigzag-distorted chains, which, however, are strongly coupled in the M2 phase and thus the metal-insulator transition always occurs concomitantly for both types of sites. We also demonstrate that the M2 phase corresponds to a local energy minimum in the structural phase space of VO2, the stability of which, apart from the internal structural distortion, depends crucially on the unit cell strain relative to the undistorted rutile phase. Our calculations further indicate that the symmetry-distinct triclinic T phase corresponds electronically to either an M1 or an M2-type insulator with an abrupt transition as a function of distortion. Finally, we disentangle the effect of the dimerization and zigzag distortions by constructing hypothetical structures that contain only one site type, finding that the zigzag distortion strongly favors emergence of the Mott-insulating state, both as function of distortion and on-site interaction.

en: Towards a unified first-principles-based description of VO2 using DFT+DMFT with bond-centered orbitals

dft

display_name: dft

dftdmft

display_name: dftdmft

mit

display_name: mit


en:
This is a summary of previous work, presenting a simulation dataset for the interactions between functionalized/unfunctionalized carbon nanomaterials and PVA. The primary objective is to investigate flow-assisted orientation and surface properties. Unreliable LAMMPS stress-strain simulations were excluded, and the rationality of the pull-out, nanoscratch, and cooling simulations was validated.

The synergistic reinforcement of polyvinyl alcohol (PVA) fibers using carbon nanomaterials is heavily constrained by interfacial compatibility and dynamic spatial orientation during the wet-spinning process. In this study, confined-shear molecular dynamics (MD) simulations were employed to systematically investigate the interfacial mechanics, structural evolution, and shear-induced orientation of PVA composites reinforced with one-dimensional (SWNT) and two-dimensional (graphene/graphene oxide) carbon nanofillers. The results reveal that surface functionalization (-COOH, -OH) significantly enhances interfacial adhesion via dense hydrogen bond networks, shifting the failure mechanism to highly dissipative plastic deformation and improving wear resistance. Interestingly, under static equilibrium, the functionalized 1D/2D hybrid system (GO+SWNT-COOH/PVA) exhibits an anomalous antagonistic effect in mechanical moduli due to spatial crowding and conformational frustration. However, the application of a dynamic shear flow field effectively overcomes this static steric hindrance, inducing a highly ordered axial alignment of both polymer chains and nanofillers. Consequently, the post-shear hybrid system unlocks its multi-scale synergistic reinforcement potential, achieving a remarkable axial Young's modulus of 8.10 GPa. These atomistic insights elucidate the critical transition from static interfacial chemistry to dynamic macroscopic orientation, providing robust theoretical guidance for the fabrication of high-performance PVA composite fibers.

en: Molecular dynamics insights into the shear-induced orientation and interfacial enhancement in functionalized CNTs and graphene/PVA composite fibers

carbon-nanotubes

display_name: carbon-nanotubes

graphene

display_name: graphene

md

display_name: md

pva

display_name: pva


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: Vacancy-ordered double perovskites have emerged as lead-free alternatives, offering remarkable stability and compositional tunability for optoelectronic applications. In this study, we provide first-principles insights into their electronic properties, surface stability, and energy level alignment using a non-empirical dielectric-dependent hybrid functional. For representative family of the Cs2MX6, with M = Zr, Sn, Te, and X= Cl, Br, I, the predicted electronic band gaps are comparable to those obtained with the state-of-the-art GW method. We investigate the stability of these materials under simulated experimental conditions, considering both the rich and poor chemical potentials of their precursor salts. Our results indicate distinct regions of surface energy stability that favor CsX terminations. In contrast, MX4 terminations show in-gap surface states, which can act as trap states and reduce carrier lifetime. Finally, based solely on the intrinsic absolute energy levels, we identify promising candidates as charge transport/injection layers for typical photovoltaic and light-emitting applications.

en: Materials Cloud

en: Dataset for Energy level alignment of vacancy-ordered halide double perovskites

absolute-energy-levels

display_name: absolute-energy-levels

charge-transport-layer

display_name: charge-transport-layer

density-functional-theory

display_name: density-functional-theory

dielectric-dependent-hybrid-functional

display_name: dielectric-dependent-hybrid-functional

surface

display_name: surface

vacancy-ordered-double-perovskites

display_name: vacancy-ordered-double-perovskites


envi

en: Environment


en: Germanium–silicon–germanium (Ge/SixGe1-x) heterostructures have emerged as a prominent platform for high-mobility electronic devices and hole-spin–based quantum technologies. In this work, we present an ab initio study of strained germanium, demonstrating that biaxial compressive strain in a Ge quantum well strongly alters the valence-band structure. Specifically, the strain lifts the heavy-hole/light-hole degeneracy and modifies the effective masses (significantly enhances hole mobility). Our findings offer a comprehensive theoretical description of the combined effects of strain and quantum confinement on the valence bands of Ge quantum wells, providing a solid foundation for predictive modeling of Ge-based high-mobility electronics and hole-spin qubits.

en: Ab initio study of strain and quantum confinement shaping the valence-band structure of Ge quantum wells

aiida

display_name: aiida

quantum-confinement

display_name: quantum-confinement

quantum-espresso

display_name: quantum-espresso

strained-germanium

display_name: strained-germanium

tight-binding

display_name: tight-binding

wannier-interpolation

display_name: wannier-interpolation


dft

display_name: dft


en: Niobium diselenide (NbSe2) has garnered significant attention due to the coexistence of superconductivity and charge density waves (CDWs) down to the monolayer limit. However, realistic modeling of CDWs—capturing effects such as layer number, twist angle, and strain—remains challenging due to the high computational cost of first-principles methods. Here, we develop a physically informed workflow for training machine-learning interatomic potentials (MLIPs) based on the E(3)-equivariant Allegro architecture, tailored to capture the subtle structural and dynamical signatures of CDWs in mono- and bilayer NbSe2. We find that while CDW lattice distortions are relatively easy to learn, modeling vibrational properties remains more challenging. It requires targeted dataset design and careful hyperparameter tuning, pushing the boundaries and testing the extensibility of current MLIP frameworks. Our MLIPs enable reliable simulations of commensurate and incommensurate CDW phases, including their sensitivity to dimensionality and stacking, as well as CDW dynamics, phonons, and transition temperatures estimated via the stochastic self-consistent harmonic approximation. This work opens new possibilities for studying and tuning CDWs in NbSe2 and other two-dimensional systems, with implications for electron-phonon coupling, superconductivity, and advanced materials design.

en: Exploring charge density waves in two-dimensional NbSe2 with machine learning

charge-density-waves

display_name: charge-density-waves

machine-learned-interatomic-potentials

display_name: machine-learned-interatomic-potentials


en:
Magnet/superconductor hybrid systems have been put forward as a platform for realizing topological superconductivity. We investigated the heterostructure of ferromagnetic monolayer CrCl₃ and superconducting NbSe₂. Using low-temperature scanning tunneling microscopy, we observe topologically trivial Yu-Shiba-Rusinov (YSR) states localized at the edge of CrCl₃ islands. DFT simulations reveal that the Cr atoms at the edge have an enhanced d-orbital DOS close to the Fermi energy. This leads to an exchange coupling between these atoms and the substrate that rationalizes the edge-localization of the YSR states.

This dataset contains the first-principles calculations performed on a nanoribbon of CrCl₃ on NbSe₂ and the associated notebooks used to generate figures from this data.

en: One dimensional edge localized YSR states in CrCl₃ on NbSe₂

aiida-siesta

display_name: aiida-siesta

heterostructure

display_name: heterostructure

horizon-europe

display_name: horizon-europe

magnetism

display_name: magnetism

prace

display_name: prace

siesta

display_name: siesta

superconductivity

display_name: superconductivity

van-der-waals

display_name: van-der-waals

yu-shiba-rusinov

display_name: yu-shiba-rusinov


aiida

display_name: aiida

density-functional-theory

display_name: density-functional-theory


en:
Accelerating the explorations of stationary points on potential energy surfaces building local surrogates spans decades of effort. Done correctly, surrogates reduce required evaluations by an order of magnitude while preserving the accuracy of the underlying theory. We present a unified Bayesian Optimization view of minimization, single point saddle searches, and double ended saddle searches through a unified six-step surrogate loop, differing only in the inner optimization target and acquisition criterion. The framework uses Gaussian process regression with derivative observations, inverse-distance kernels, and active learning. The Optimal Transport GP extensions of farthest point sampling with Earth mover's distance, MAP regularization via variance barrier and oscillation detection, and adaptive trust radius form concrete extensions of the same basic methodology, improving accuracy and efficiency. We also demonstrate random Fourier features decouple hyperparameter training from predictions enabling favorable scaling for high-dimensional systems. Accompanying pedagogical Rust code demonstrates that all applications use the exact same Bayesian optimization loop, bridging the gap between theoretical formulation and practical execution.

This dataset contains the raw data and models for generating the results in the manuscript.

en: Bayesian optimization with Gaussian processes to accelerate stationary point searches

bayesian

display_name: bayesian

gaussian-process-regression

display_name: gaussian-process-regression

minimization

display_name: minimization

saddle-search

display_name: saddle-search


en:
The nudged elastic band (NEB) method is the standard approach for finding minimum energy paths and transition states on potential energy surfaces. Practical NEB calculations require several pre-processing steps: endpoint minimization, structural alignment, and initial path generation. These steps are typically handled by ad-hoc scripts or manual intervention, introducing errors and hindering reproducibility. We present a fully automated, open-source Snakemake workflow for small gas phase molecules that couples modern machine learning potentials (PET-MAD) to the eOn saddle point search software. Each step of the calculation lifecycle is encoded as an explicit dependency graph, from model retrieval and endpoint preparation through path initialization and band optimization. The workflow resolves all software dependencies from conda-forge, ensuring identical execution across platforms. Validation on the HCN to HNC isomerization demonstrates that the automated pipeline recovers the known single-barrier energy profile and product energy without manual intervention.

This record contains the data needed to "rehydrate" the Github repository to the point of submission including the models and results.

en: Reproducible orchestration of best practices for reaction path optimization with the nudged elastic band

computational-chemistry

display_name: computational-chemistry

nudged-elastic-band

display_name: nudged-elastic-band

transition-state

display_name: transition-state


en: Density-functional theory with on-site U and inter-site V Hubbard corrections (DFT+U+V) is widely used to predict properties of transition-metal and rare-earth compounds, but its accuracy depends critically on how these parameters are obtained. While they can be determined empirically, first-principles approaches offer better consistency, though their results can differ and a systematic comparison is lacking. Here, we compare two widely used approaches, linear-response theory (LRT) and the Hartree–Fock-based pseudohybrid functional formalism, applied to representative oxides (MnO, NiO, CoO, FeO, BaTiO3, ZnO, and ZrO2). For partially occupied transition-metal d states, both methods yield comparable U values, but they differ significantly for nearly empty or fully filled d shells. For O-2p states, LRT systematically predicts large U values (~10 eV), whereas the pseudohybrid approach gives system-dependent values. Even larger differences appear for the inter-site V: the former yields small values (<1 eV), while the latter gives larger values (~3 eV) due to its dependence on charge redistribution. We further show that the pseudopotential choice, which defines Hubbard projectors, affects the parameters and their agreement. Overall, while parallels between these two methods exist, they rely on different assumptions, leading to variations in predictions of material properties.

en: Comparing Hubbard parameters from linear-response theory and a Hartree-Fock-based approach

coo

display_name: coo

dftu

display_name: dftu

dftuv

display_name: dftuv

feo

display_name: feo

mno

display_name: mno

nio

display_name: nio

quantum-espresso

display_name: quantum-espresso

zno

display_name: zno


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 establish a set of exact sum rules that relate the interatomic force constants to the frequency-dependent electromagnetic susceptibility of a solid or molecule, thereby generalizing the long-established principles of rototranslational symmetry to the nonadiabatic regime. Crucially, we show that in practical numerical implementations these sum rules are violated, unless special precautions are taken in the treatment of the atomic pseudopotentials. We solve these issues once and for all by correctly adapting the pseudopotential to the motion of the corresponding nucleus, with a velocity dependence of the nonlocal operator. This prescription restores the correct Galilean covariance of the Schr"odinger equation, and the expected identity between mechanical rototranslations and electromagnetic perturbations. These results conclusively fix a number of worrisome inconsistencies that were pointed out over the years in the context of linear-response theory restoring, e.g., the validity of the Larmor theorem, and the equivalence between the inertial and electrical definitions of the Drude weight in metals.

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

en: Rototranslational sum rules for nuclear dynamics via traveling pseudopotentials

nonadiabatic-regime

display_name: nonadiabatic-regime

pseudopotentials

display_name: pseudopotentials


materialscloud

en: Materials Cloud

density-functional-theory

display_name: density-functional-theory


en: We present a density-functional theory study of the effects of oxygen vacancies on the structural and electronic properties of vanadium dioxide (VO2). Our motivation is the reported suppression of the metal-insulator transition by oxygen vacancies and the lack of a clear consensus on its origin. We use the DFT+V method with a static intersite vanadium-vanadium interaction term, V, to calculate the properties of the oxygen-deficient metallic rutile and insulating monoclinic M1 phases of VO2 on the same footing. We find that oxygen vacancies induce local distortions in the M1 phase, but do not destroy the dimerization usually associated with the insulating behavior. In spite of this, we find that the M1 phase becomes metallic as a result of the partial filling of the conduction band due to a rigid-band-like doping effect.

en: Oxygen vacancies in vanadium dioxide: A DFT+V study

envi

en: Environment

dft

display_name: dft

vacancy

display_name: vacancy


en: We present a high-throughput computational screening for fast lithium-ion conductors aimed at identifying candidate materials for application in all-solid-state electrolytes. Beginning with more than 30,000 experimentally reported Li-containing structures drawn from the Inorganic Crystal Structure Database, the Materials Platform for Data Science (Pauling file), and the Crystallography Open Database, we apply a series of automated structural and compositional filters to obtain 1500 unique crystal structures suitable for electronic-structure calculations which yields nearly 1,000 electronic insulators. We then estimate Li-ion diffusivities for these insulating candidates using molecular dynamics simulations at multiple temperatures. To make simulations computationally feasible at this scale while preserving near first-principles fidelity, we employ a foundational machine-learned interatomic potential which is carefully fine-tuned on relevant Li-chemistries. We discuss the details of the fine-tuning strategies and data-consistency considerations required to obtain a very accurate and robust model. From the MD results, we identify three particularly promising novel oxide candidates for room-temperature solid-state electrolytes, including LiSn2(AsO4)3, LiIn(IO3)4, and LiB6S4(Cl3O4)2, which all exhibit ionic conductivity greater than 1 mS/cm at room temperature with diffusion barrier between 0.20 and 0.25 eV. We provide the full screening protocol as well as a prioritised list of materials for experimental follow-up, demonstrating the value of provenance-aware, ML-driven simulations in accelerating solid-state electrolyte discovery.

en: High-throughput screening for solid-state Li-ion conductors combining machine learning and first-principles calculations

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

li-ion-conductors

display_name: li-ion-conductors

molecular-dynamics

display_name: molecular-dynamics

solid-state-electrolytes

display_name: solid-state-electrolytes


en: Materials exhibiting coexisting and coupled ferroelectric and magnetic orders are rare. Here we show, using density functional theory calculations, that introducing FeLa antisite defects into pristine, non-ferroelectric, G-type antiferromagnetic (AFM) LaFeO3 renders the material simultaneously ferroelectric and a defect-induced local ferrimagnetic phase. We assess the thermodynamic stability of FeLa antisites and identify synthesis conditions under which they are likely to form. Nudged elastic band calculations indicate an accessible barrier for ferroelectric polarization switching. Interestingly, we observe an inverse magnetoelectric coupling in which the ferroelectric polarization controls the magnetic order. We further propose two defect-mediated magnetization-control mechanisms enabled by ferroelectric switching in LaFeO3, both involving a 180° reversal: (i) switching the FeLa antisite moments while the pristine G-type AFM order remains unchanged, and (ii) of the pristine G-type AFM order while the FeLa antisite moments are pinned by a static magnetic field. In both cases, exchange coupling between the antisite and pristine Fe sublattices enables magnetization switching, highlighting a defect-engineering route to electrically controlled magnetic states for data storage.

en: Multiferroicity and 180° magnetization switching in LaFeO3 via antisite defects

antisite-defect

display_name: antisite-defect

multiferroic

display_name: multiferroic


en: Nanoscale twin boundaries (TBs) effectively restrict the free motion of dislocations by intersecting with other TBs to realize high strength. However, the dislocations parallel to the TBs can glide along the TBs, resulting in detwinning for low strength. Herein, we present a triple-interlocked-nanotwinned (TIT) strategy for overcoming these inherent deficiencies. An Mg-9Li alloy was used as a proof-of-concept. The treated alloy had a large volume fraction of TIT interfaces (65.7%). It not only had a high yield strength of 508.6 MPa (approximately eleven times higher than that of the TIT-free sample) but also offered unprecedented ageing resistance, with a reduction in hardness of 6% after 730 days. The martensite-like phase transformation process concurrently triggers multiple-variant twins to form TIT structures. These TIT nets can effectively prohibit dislocation motion, reduce strengthening anisotropy, and disperse localized stress during the deformation process. This strategy of phase transformation twinning open a gate to fabricate other stronger metallic or ceramic nanotwinned bulk materials.

en: Triple-interlocked-nanotwinned bulk magnesium alloys with exceptional strength and ageing resistance

ageing-resistance

display_name: ageing-resistance

magnesium-alloys

display_name: magnesium-alloys

strength

display_name: strength

triple-interlocked-nanotwin

display_name: triple-interlocked-nanotwin


en: Sodium (Na) metal batteries are considered promising solutions for next-generation electrochemical energy storage because of their low costs and high energy densities. However, the slow Na dynamics result in unfavourable Na deposition and dendrite growth, which compromise cycling performance. Here we propose a nanotwinned alloy strategy prepared by high-pressure solid solution followed by Joule-heating treatment to address sluggish Na dynamics, achieving homogeneous Na deposition. By employing cost-effective Al-Si alloys for validation, Si solubility of 10 wt.% is extended through a high-pressure solid solution, and nanotwinned-Si particles, with a volume fraction of 82.7%, are subsequently formed through Joule-heating treatment. The sodiophilic nanotwinned-Si sites exhibit a high diffusion rate, which reduces the nondimensional electrochemical Damköhler number to far below 1, shifting the diffusion-controlled deposition behavior to reaction-controlled deposition. This transition facilitates spherical Na deposition and dendrite-free growth, allowing a symmetric cell to achieve stable Na plating/stripping over 5300 hours at 5 mA cm⁻² with a cumulative capacity of 13.25 Ah cm⁻². This strategy is also demonstrated in another CuAg system with nanotwinned Ag structures.The record contains data that support the findings described in the publication.

en: A nanotwinned-alloy strategy enables fast sodium deposition dynamics

dynamics

display_name: dynamics

nanotwinned-alloy

display_name: nanotwinned-alloy

sodium-metal-battery

display_name: sodium-metal-battery


en:
This dataset contains the minimal set of AiiDA calculations supporting the results presented in the paper "Multiband Anisotropic Superconductivity in 2H-NbSe₂ Revealed by Ab-Initio Kohn--Sham Bogoliubov--de Gennes Calculations".

It includes the fully relativistic Kohn--Sham Bogoliubov--de Gennes (KS-BdG) calculations for pristine bulk 2H-NbSe₂ performed with the JuKKR code and the AiiDA-KKR plugin. The calculations use a single, physically transparent pairing strength λ_Nb applied only to the Nb sites and quantitatively reproduce the experimentally observed multigap density of states with coherence peaks at 1.85, 1.55, and 1.4 meV together with the ~30% momentum-dependent superconducting gap anisotropy. The archive provides the normal-state electronic structure and the self-consistent superconducting state, including all input parameters required for full reproducibility.

en: Multiband anisotropic superconductivity in 2H-NbSe2 revealed by ab-initio Kohn--Sham Bogoliubov--de Gennes calculations

aiida

display_name: aiida

aiida-kkr-ksbdg

display_name: aiida-kkr-ksbdg

bogoliubov-de-gennes

display_name: bogoliubov-de-gennes

superconductivity

display_name: superconductivity


materialscloud

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

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


en: We develop an extensive dataset of Allen-Cahn simulations of crystal growth in 2D, including kinetic anisotropy to produce hexagonally faceted profiles, and a variable supersaturation parameter to control the growth rates and local faceting. A phase-field numerical approach based on a finite-difference explicit time integration scheme is used to generate such evolution sequences. Two Convolutional Recurrent Neural Network architectures are then exploited to train quantitative surrogate models reproducing such evolution sequences conditioned to the supersaturation value. The first infers the supersaturation parameter implicitly from an input mini-sequence of a few evolution frames while the second takes it as an explicit input along with a single initial frame. The predictive performances of the two models are evaluated by extensive testing on dedicated testsets. In particular, the analysis includes both the testing on a large dataset of the same kind of the training one, and on dedicated datasets with assigned values of the supersaturation parameter. While the architecture using the parameter as explicit input yields the best performance, with quantitative reproduction of the true phase-field solutions, the mini-sequence model produce satisfactory results only by taking comparatively larger dataset sizes. The generalization capabilities of the trained models are finally inspected by considering larger domains, longer evolution times and different initial coverages.

en: Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioning

allen-cahn-equation

display_name: allen-cahn-equation

convolutional-recurrent-neural-network

display_name: convolutional-recurrent-neural-network

crystal-growth

display_name: crystal-growth

surrogate-model

display_name: surrogate-model


materialscloud

en: Materials Cloud


en: In rarefied atmospheric environments, where continuum fluid dynamics breaks down, aerodynamic drag is governed by gas–surface momentum exchange, making surface structure and chemistry key design knobs. Using molecular dynamics simulations, we show that coating the α-Al2O3(0001) surface with graphene markedly reduces the tangential momentum accommodation coefficient (TMAC) of N2, shifting scattering toward more specular reflection and thereby lowering drag; we further benchmark this response against graphite. The reduction is robust and strengthens up to 900 K. While structural defects can increase TMAC – and hence drag – via defect-induced corrugation and local atomic and electronic rearrangements, graphene retains near-ideal low-TMAC performance at experimentally relevant defect densities. These results identify graphene as a thermally resilient, low-drag coating for aerospace-relevant surfaces in rarefied flows.

en: Defects, corrugation and temperature govern rarefied-air drag on graphene coatings

accommodation-coefficients

display_name: accommodation-coefficients

air

display_name: air

alumina

display_name: alumina

coating

display_name: coating

defects

display_name: defects

drag

display_name: drag

graphene

display_name: graphene

graphite

display_name: graphite

molecular-dynamics

display_name: molecular-dynamics

rarefied-gas

display_name: rarefied-gas


envi

en: Environment


en: The dataset provides records of creep properties for structural materials used in nuclear applications, focusing on the influence of specimen dimensions on time-dependent deformation behavior. It was developed through an extensive literature search of peer-reviewed articles, technical reports, and institutional databases, including contributions from 50 publications and databases. The search targeted studies presenting quantitative creep data for both standard and sub-sized specimens, with detailed information on testing conditions, specimen geometries, and material compositions. The compiled database contains 1,413 creep testing records covering parameters that include alloy type and grade, chemical composition, thermal and mechanical treatment history, irradiation conditions, specimen geometry and dimensions, and measured creep properties. The dataset encompasses material classes such as stainless steels (A709, SS304, SS316), reactor pressure vessel steels (SA533, SA508), nickel-based alloys (IN600, IN617), and zirconium alloys (Zr-2, Zr-3, Zr-4). Experts in materials science reviewed and validated all records, verifying material specifications, treatment conditions, and testing parameters for consistency and accuracy.

en: Creep dataset from peer-reviewed literature of nuclear structural materials

creep-test

display_name: creep-test

nuclear-structural-materials

display_name: nuclear-structural-materials

size-effect

display_name: size-effect

sub-sized-specimens

display_name: sub-sized-specimens


en: Cs2KInI6 is a promising lead-free halide double perovskite with a calculated direct band gap of 1.94 eV, ideal for solar cell applications. Our first-principles calculations reveal that its cubic phase (Fm3̅m) is dynamically unstable. Using an accelerated machine learning approach, we identify 42 dynamically stable structures and further validate these findings using first-principles calculations on 11 of these. The most stable phase has Cmc21 symmetry with 20 atoms/unit cell. It lies 13 meV/atom above the convex hull but lacks octahedral cation coordination. The most stable perovskite-like structure has P3̅ symmetry with 10 atoms/unit cell and low octahedral connectivity. Structure-property trade-offs are highlighted, with calculated distortions generally widening the band gap, shifting it from direct to indirect, and flattening the band edges. This work showcases the synergy of genetic algorithms, machine-learned potentials, and first principles validation for discovering stable, complex materials. This archive contains all our input, output files, the data and scripts.

en: From symmetry to stability: structural and electronic transformation in Cs₂KInI₆

first-principles

display_name: first-principles

halide-double-perovskite

display_name: halide-double-perovskite

machine-learned-interatomic-potentials

display_name: machine-learned-interatomic-potentials

phonons

display_name: phonons

polymorphs

display_name: polymorphs


en: Materials underpin modern technologies, from energy harvesting, storage, and conversion to information and communication technologies. Their functionality is often governed by the interplay between competing phases, as thermodynamic behavior shapes microscopic properties and ultimately determines technological performance; for instance, the light absorption of inorganic metal-halide perovskites in solar cells. Accurately predicting crystal thermodynamics, however, remains a major challenge for computational approaches because strong anharmonic effects require extensive sampling of the potential energy surface. Here, we present an on-the-fly Bayesian framework, combined with the stochastic self-consistent harmonic approximation, for learning first-principles interatomic potentials. This approach enables the prediction of thermodynamic properties over a broad temperature range with first-principles accuracy while requiring training on only a few tens to a few hundreds of atomic configurations. To demonstrate its power, we investigate the thermodynamic and dynamical properties of Li2O, α-CsPbI3, and δ-CsPbI3, requiring only 44, 256, and 50 total-energy calculations, respectively. Notably, we show that this framework accurately captures the phase diagram of CsPbI3, which explains its spontaneous degradation into the non-absorbing yellow phase, predicting the transition temperature with remarkable accuracy and efficiency. More broadly, the method presented opens a novel route toward accelerated materials engineering under realistic conditions for a wide range of technologically relevant applications, including solid-state batteries, optoelectronic devices, and memristors.

en: Predicting challenging phase transitions with Bayesian active learning

active-learning

display_name: active-learning

anharmonicity

display_name: anharmonicity

machine-learning

display_name: machine-learning

phase-transitions

display_name: phase-transitions

thermodynamics

display_name: thermodynamics


en: Solvent reverse osmosis is a pressure-driven, liquid-phase process for separating solvent mixtures, offering a potential low-energy alternative to thermal operations. Graphene oxide (GO) laminates provide tunable nanochannels to probe confined solvent transport, yet solvent–solvent separations remain underexplored due to solvation-induced structural instabilities and the small molecular sizes. Here we construct solvent-stable, supported GO nanochannel membranes that preserves integrity under pressurized solvents, and tune interlayer confinement and surface polarity via controlled chemical reduction. Across 51 solvent systems and 5 distinct nanochannels, we demonstrate that separation is governed by coupled nanoconfinement and solvent affinity, where selective interfacial association can surpass simple size-exclusion expectations. Maximum permselectivity arises from balancing channel size with retained polarity, indicating that channel shrinking alone does not optimize performance. These findings identify channel surface chemistry as a key design factor for polarity-rich solvent systems and provide a framework for rationally tailoring nanochannels for complex solvent separations.

en: Solvent reverse osmosis beyond size exclusion in 2D nanochannel membranes

graphene

display_name: graphene

reverse-osmosis

display_name: reverse-osmosis

solventsolvent-separation

display_name: solventsolvent-separation


en: The FAIR principles have transformed how computational materials data and workflows are shared, yet existing repositories can only serve pre-computed entries — their coverage is perpetually incomplete and cannot adapt to new questions on demand. We built OptiMat Alloys, a large-language-model-powered conversational agent for multi-principal element alloy exploration, on three pillars: a living database that stores every calculation with provenance, low-barrier accessibility through a web interface that requires zero programming expertise, and built-in uncertainty quantification via cross-potential and cross-configuration validation. By coupling foundational machine-learning interatomic potentials that span nearly the entire periodic table with natural-language interaction, the agent enables targeted, on-demand computation guided by the user's own domain knowledge — extending FAIR from pre-computed repositories to on-demand knowledge generation, and making computational alloy screening accessible to any materials scientist.

en: OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent

fair-principle

display_name: fair-principle

llm-agent

display_name: llm-agent

materials-informatics

display_name: materials-informatics

multi-principal-element-alloys

display_name: multi-principal-element-alloys

universal-machine-learning-potentials

display_name: universal-machine-learning-potentials


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: Correlated electron molecular orbital (CEMO) materials host emergent electronic states built from molecular orbitals localized over clusters of transition metal ions yet have historically been discovered sporadically and generally been treated as isolated case studies. Here we establish CEMO materials as a systematically discoverable class and introduce a graph-based framework to identify, classify, and organize transition-metal cluster motifs in inorganic solids. Starting from crystal structures in the Materials Project, we construct transition metal connectivity graphs, extract cluster motifs using a bond-cutting algorithm, and determine cluster point groups, effective cluster sublattice dimensionality, and translational symmetry. Applying this approach in a high-throughput screen of 34,548 compounds yields 5,306 cluster-containing materials, including 2,627 stable or metastable compounds with isolated clusters and 984 materials featuring mixed-metal clusters. The resulting dataset reveals symmetry and element dependent trends in cluster formation. By integrating cluster classification with flat band lattice topology and battery-relevant information, we provide further relevant information to multiple scientific communities. The accompanying open dataset, Cluster Finder software, and interactive web platform enable systematic exploration of cluster driven electronic phenomena and establish a general pathway for discovering correlated quantum materials and functional materials with cluster-based or extended metal-metal bonding in inorganic solids.

en: Discovery of correlated electron molecular orbital materials using graph representations

battery-materials

display_name: battery-materials

correlated-electron-materials

display_name: correlated-electron-materials

flat-band

display_name: flat-band


materialscloud

en: Materials Cloud


en: This electrochemical, X-ray diffraction, and operando X-ray abosorption spectroscopy data characterize and decouple the effects of bulk vs. electrolyte Fe incorporation in crystalline, defective crystalline, and amorphous catalysts derived from the same CoSn(OH)₆ precursor. Electrolyte Fe incorporation equalizes the OER activity across all three structural variants, demonstrating that surface Fe chemistry can dominate initial structural effects on the reconstructed catalyst interface.

en: Characteristic data of activation of crystalline and amorphous OER catalysts with bulk and surface Fe incorporation.

catalyst

display_name: catalyst

cobalt

display_name: cobalt

diffraction

display_name: diffraction

electrocatalysis

display_name: electrocatalysis

iron

display_name: iron

local-structure

display_name: local-structure

operando

display_name: operando

oxygen-evolution

display_name: oxygen-evolution

surface-reconstruction

display_name: surface-reconstruction

x-ray-apbsorption-spectroscopy

display_name: x-ray-apbsorption-spectroscopy


envi

en: Environment


en: The localized states in the band gap of amorphous phase change alloys like Ge2Sb2Te5 control the electrical conduction via the Poole-Frenkel mechanism. Understanding the origin of in-gap states and their evolution in time during aging of the glass is therefore important for the control of the resistance drift in phase change memory devices. Here, we use a machine learning interatomic potential to generate several models of Ge2Sb2Te5 whose electronic structure is then analyzed within density functional theory with a hybrid functional. A detailed statistical analysis of the structural motifs on which the in-gap states are localized, reveals that the vast majority of in-gap states involve wrong bonds (homopolar or Ge-Sb bonds) often accompanied by Ge in tetrahedral configurations or overcoordinated Ge and Sb atoms. Metadynamics simulations mimicking glass aging support the picture that structural relaxations lead to the depletion of in-gap states and then to an increase of resistance. The simulations thus provide important insights for the mitigation of the resistance drift in phase change memory devices.

en: On the origin of in-gap states in amorphous Ge2Sb2Te5

electronic-memories

display_name: electronic-memories

electronic-structure

display_name: electronic-structure

machine-learning

display_name: machine-learning

molecular-dynamics

display_name: molecular-dynamics

phase-change-materials

display_name: phase-change-materials


en: AMaRaNTA stands for Automating Magnetic paRAmeters iN a Tensorial ApproachThe aim of the AMaRaNTA package is to implement and automatize the widely used energy-mapping method, namely the 4-state method using density functional theory by removing the laborious, multi-step manual procedures. Based on the AiiDA framework, it extracts full spin-orbit coupling included exchange and anisotropy parameters for 2D magnets. In its current implementation (full workchain publicly available at https://github.com/AMaRaNTA-code), AMaRaNTA returns the nearest-neighbour exchange tensor, complemented by scalar parameters for second- and third-nearest-neighbour exchange interactions as well as single-ion anisotropy.Present repository is to make available the results obtained using this workchain run on a set of 29 materials, a representative subset of the Materials Cloud 2D Structure database.

en: AMaRaNTA: automated first-principles exchange parameters in 2D magnets [Results]

aiida

display_name: aiida

dft

display_name: dft

magnetic-exchanges

display_name: magnetic-exchanges


en: The precise atomic-scale structure around Eu2+ activators in the β-Si6-zAlzOzN8-z:Eu2+ commercial green phosphor remains elusive. We use the first-principles ΔSCF excited-state method, embedding of the interatomic force constants for supercells up to 3501 atoms, and Huang-Rhys theory to clarify this issue. Monte Carlo exploration is used to identify representative low-energy structural models spanning different levels of Al/O concentration z.  For the lowest-energy structure at low z, our computed photoluminescence spectrum reproduces the experimental vibronic peaks at 6K with excellent agreement in peak positions and intensities, validating the Eu-N9 coordination model with Al, O, and Eu confined to the same crystallographic plane. Analysis of the low-energy structures reveals that the electron-phonon coupling is weak (S ≈ 2.15) with a robust characteristic phonon signature across different Al/O arrangements, explaining the surprising persistence of resolved phonon replicas with increasing z. We explain the experimentally observed red-shift of emission with increasing z through systematic trends in zero-phonon line energies, modest increases in Huang-Rhys factors, and larger configurational diversity at higher compositions.

en: Micro-environment of the Eu interstitial in the β-Si6-zAlzOzN8-z:Eu2+ green phosphor

electron-phonon

display_name: electron-phonon

phosphors

display_name: phosphors

vibronic

display_name: vibronic


en: This entry provides the training and test datasets, final datasets, trained models, and simulation results associated with four machine learning interatomic potentials (MLIPs) of liquid water: DeePMD and MACE models trained from scratch via active learning, a fine-tuned MACE foundation model, and a DeePMD potential obtained by knowledge distillation. All input scripts for the active learning and AIMD simulations have been included. Source data for reproducing the figures in the manuscript are provided. Detailed explanations for each subdirectory are provided inside the folder.

en: data-efficient and fast machine learning molecular dynamics through integrated active learning and knowledge distillation

active-learning

display_name: active-learning

deepmd

display_name: deepmd

knowledge-distillation

display_name: knowledge-distillation

mace

display_name: mace

machine-learning-interatomic-potential

display_name: machine-learning-interatomic-potential


en: This repository accompanies the manuscript “ChemFit: A framework for automated high-dimensional model parameter optimization” and contains the source code, input structures, and simulation outputs required to reproduce the examples presented in the study. ChemFit is a Python framework for the definition and massively concurrent evaluation of simulation-based objective functions for use with gradient-free and black-box optimization algorithms.The archive includes snapshots of the source code used in the study together with the corresponding simulation metadata, logfiles, reference structures, and initial configuration files necessary to reproduce the reported optimization workflows and results.

en: Data availability for "ChemFit: a framework for automated high-dimensional model parameter optimization"

biocondensates

display_name: biocondensates

chemfit

display_name: chemfit

coarse-grained-model

display_name: coarse-grained-model

computational-physics

display_name: computational-physics

lennard-jones

display_name: lennard-jones

parameter-optimization

display_name: parameter-optimization

water-model

display_name: water-model


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:
Deorbitalization of a conventional meta-generalized-gradient exchange-correlation approximation replaces its dependence upon the Kohn-Sham kinetic energy density with a dependence on the density gradient and Laplacian. In principle, that simplification should provide improved computational performance relative to the original meta-GGA form because of the shift from an orbital-dependent generalized Kohn-Sham potential to a true KS local potential. Often that prospective gain is lost because of problematic roughness in the density caused by the density Laplacian and consequent roughness in the exchange-correlation potential from the resulting higher-order spatial derivatives of the density in it. We address the problem by constructing a deorbitalizer based on the RPP deorbitalizer [Phys. Rev. Mater. 6, 083803 (2022)] with comparative smoothness of the potential along with retention of constraint satisfaction as design goals. Applied to the r2SCAN exchange-correlation functional [J. Phys. Chem. Lett. 11, 8208 (2020)], we find substantial timing improvements for solid-state calculations over both r2SCAN and its earlier deorbitalization for high precision calculations of structural properties, while improving upon the accuracy of RPP deorbitalization for both solids and molecules.

Data archive includes data needed to reproduce the figures and tables of this work, and input scripts for test set calculations performed using VASP and NWChem electronic structure codes.

en: Performance improvement of deorbitalized exchange-correlation functionals

computation

display_name: computation

density-functional-theory

display_name: density-functional-theory

deorbitalization

display_name: deorbitalization

metagga

display_name: metagga


materialscloud

en: Materials Cloud


en:
Advanced fission and fusion technologies require materials that withstand extreme conditions, relying on irradiation-tolerant structures to suppress defect evolution and mechanical degradation. This study introduces a novel approach to immobilize irradiation-induced defects by tuning local lattice distortion in concentrated solid solution alloys. Results demonstrate that higher distortion levels significantly reduce microstructural changes. The single-phase Ni80Mo20 alloy, with the highest recorded distortion (4.82% atomic size mismatch), exhibits frozen defect motion and negligible irradiation effects. This suggests engineering lattice distortion as a straightforward method for designing irradiation-resistant metallic alloys.

This dataset provides the initial and final atomic structures from molecular dynamics simulations of single vacancy and single interstitial diffusion in Ni, NiFe, NiCoV, and Ni80Mo20 alloys, each exhibiting distinct levels of lattice distortion.

en: Achieving enhanced irradiation-resistant metallic alloys by immobilizing induced defects

irradiation-damage

display_name: irradiation-damage

lattice-distortion

display_name: lattice-distortion

metallic-alloys

display_name: metallic-alloys

molecular-dynamics

display_name: molecular-dynamics


envi

en: Environment


en:
This dataset contains the data (as well as the code required to generate the data) for the numerical experiments of our manuscript "Quantum Finite Temperature Lanczos Method".

Abstract:

The computation of thermal properties of quantum many-body systems is a central challenge in our understanding of quantum mechanics. We introduce the Quantum Finite Temperature Lanczos Method (QFTLM), which extends the finite-temperature Lanczos method to quantum computers by combining real-time quantum Krylov methods with efficient preparation of typical states for trace estimation. This approach enables the computation of thermal expectation values while avoiding the exponential scaling inherent to classical exact simulation techniques. Numerical experiments on the transverse-field Ising model show that QFTLM can reproduce thermal observables over a wide temperature range. We further analyze the influence of Krylov dimension, number of trace-estimator states, and Trotter error, and show that suitable regularization is essential for robustness in noisy settings. These results establish QFTLM as a promising framework for finite-temperature quantum simulation.

en: Data for manuscript on the quantum finite temperature Lanczos method

finite-temperature

display_name: finite-temperature

krylov

display_name: krylov

lanczos

display_name: lanczos

quantum

display_name: quantum

simulation

display_name: simulation


en: We establish a quantitative relation between the altermagnetic spin-splitting and different higher order multipoles of the charge and magnetization density around the magnetic atoms. Magnetic multipoles such as octupoles or triakontadipoles have been suggested as potential ferroic order parameters for d- and g-wave altermagnetism, respectively, based mainly on qualitative symmetry arguments. We use first-principles-based electronic structure calculations to establish a clear quantitative relation between the strength of the altermagnetic spin splitting and the magnitude of certain local multipoles. We vary the magnitude of these multipoles either by applying an appropriate constraint on the charge density or by varying a corresponding structural distortion mode, using two simple perovskite materials, SrCrO₃ and LaVO₃, as model systems. Our analysis indicates that in general the altermagnetic spin splitting is not exclusively determined by the lowest order nonzero magnetic multipole, but results from a superposition of contributions from different multipoles with comparable strength, suggesting the need for a multi-component order parameter to describe altermagnetism. We also discuss different measures to quantify the overall spin-splitting of a material, without relying on features that might be specific to only individual bands.

en: Multipoles as quantitative order parameters for altermagnetic spin splitting

altermagnetism

display_name: altermagnetism

dft

display_name: dft

multipoles

display_name: multipoles


en: This repository accompanies the manuscript “Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation” and contains vasprun.xml files of all calculations. The manuscript presents DFT calculations made with VASP of the 2H, 4H and 6H polytypes of Ge and SiGe and discusses their electronic and optical properties. The first part presents transition matrix elements and the radiative lifetimes. The second part presents the dielectric functions and absorptions of the structures from model BSE calculations.

en: Data availability for "Optical selection rules in hexagonal Ge polytypes and their lifting by symmetry perturbation"

condensed-matter-physics

display_name: condensed-matter-physics

hexagonal-ge

display_name: hexagonal-ge

model-bse

display_name: model-bse

optoelectronic-materials

display_name: optoelectronic-materials

polytypes

display_name: polytypes


en: Maximally-localized Wannier functions are quantum wavefunctions resembling atomic orbitals that are used to describe electrons in condensed matter. Since their introduction in 1997, these functions have become ubiquitous in ab initio materials simulations, including applications in linear-scaling methods, strongly correlated electron systems, quantum transport, electron-phonon interactions, and topological materials. Despite their widespread adoption in a vast software ecosystem, Wannier functions have not yet attained their fullest potential in the presence of entangled bands, as their optimization remains challenging and labor-intensive. Here, we introduce a universal meta-optimization method that leverages workflow abstraction and machine-learning techniques like differential evolution and Bayesian optimization to generate globally optimized Wannier functions without human intervention. We demonstrate this approach through three applications: (i) autonomous interpolation of entangled band structures with millielectronvolt accuracy starting from coarse Brillouin zone grids, (ii) thousand-fold acceleration of fully ab initio Boltzmann transport calculations via the use of minimal coarse Brillouin zone grids, and (iii) ultrafast high-throughput calculations of high-precision Wannier functions for large materials libraries. This work brings calculations that previously required supercomputers within the reach of personal computers.The data generated in this work and reported in the manuscript is attached in the Data.tar.gz with full description in the Readme.md.

en: Meta-optimization of maximally-localized Wannier functions

machine-learning

display_name: machine-learning

meta-optimization

display_name: meta-optimization

wannier-functions

display_name: wannier-functions


en: This record contains the computational data associated with the manuscript “Distinct O2 activation regimes and an activation–removal trade-off at 3d-metal-doped MoS2 sulfur vacancies” by Park and Kim. The dataset includes optimized atomic structures, representative VASP input templates, and curated tabulated data supporting the reported adsorption energies, structural parameters, electronic descriptors, and kinetic benchmarks for Fe-, Co-, Ni-, and Cu-doped MoS2 sulfur vacancies. The data are intended to support reproducibility of the figures and tables in the manuscript and the Supporting Information. Detailed descriptions of the individual files and folders are provided in the accompanying README.txt file.

en: Computational dataset for distinct O2 activation regimes and an activation–removal trade-off at 3d-metal-doped MoS2 sulfur vacancies

cohp

display_name: cohp

density-functional-theory

display_name: density-functional-theory

electronic-structure-descriptors

display_name: electronic-structure-descriptors

oxygen-activation

display_name: oxygen-activation

single-atom-catalyst

display_name: single-atom-catalyst

strain-engineering

display_name: strain-engineering

sulfur-vacancy

display_name: sulfur-vacancy

transition-metal-doping

display_name: transition-metal-doping


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: Interatomic potentials remain a critical bottleneck for accurate large-scale atomistic simulations. Machine-learning potentials provide a promising route to overcoming this limitation; however, the construction of property-relevant training datasets remains a major challenge. Here, we propose an active-learning strategy based on property balancing, in which the fraction of atomic environments associated with pre-identified key material properties is dynamically optimized during training. We demonstrate this strategy for pure magnesium, whose macroscopic plastic deformation is governed by several subtle dislocation transformations that place stringent demands on potential accuracy. Using the proposed strategy, we develop a Behler–Parrinello-type neural-network potential that captures these dislocation-mediated mechanisms with a level of accuracy that has been difficult to achieve in previous studies of magnesium. The successful application to magnesium highlights the versatility and effectiveness of property balancing as a general strategy for developing machine-learning potentials for metallic systems with multiple competing property requirements. This approach provides a practical pathway toward predictive mechanistic simulations of structural metals.

en: Property-balancing active learning strategy for machine learning interatomic potentials: case study on dislocation mechanics in magnesium

dislocation

display_name: dislocation

magnesium

display_name: magnesium

neural-network-potential

display_name: neural-network-potential


materialscloud

en: Materials Cloud


en: These data characterize the activation processes of CoSn(OH)6-based electrocatalysts for the oxygen evolution reaction in alkaline conditions. Crystalline (CoSn25), crystalline defective (CoSn150) and amorphous (CoSn300) catalysts with identical metal compositions from the same precursor batch were considered. The operando X-ray diffraciton and X-ray abosorption spectroscopy indicate that crystalline and amorphous catalysts take different reconstruction pathways under the reaction conditions. Co L-edge total electron yield data indicate that a metal-ligand charge transfer surface state is formed during electrochemical activation that correlates well with the observed electrochemical activity.

en: Characteristic data of the CoSn(OH)6 alkaline electrochemical activation processes.

electrocatalysis

display_name: electrocatalysis

oer

display_name: oer

operando

display_name: operando

oxygen-evolution-reaction

display_name: oxygen-evolution-reaction

synchrotron

display_name: synchrotron

x-ray-absorption-spectroscopy

display_name: x-ray-absorption-spectroscopy

x-ray-diffraction

display_name: x-ray-diffraction


envi

en: Environment


en: This dataset contains structural minima, saddle-point structures, and transformation pathways of magic-size gold nanoclusters. Gold nanoclusters exhibit a complex energy landscape characterized by competing structural motifs and narrow energy gaps, which facilitate structural coexistence and rapid interconversion. In this study, we employ a high-accuracy machine-learned potential, trained on 20,000 density functional theory reference points, to investigate the structural transformation pathways of Au55, Au147, Au309, and Au561 nanoclusters. Our analysis encompasses transitions between cuboctahedral, Ino's decahedral, and icosahedral geometries. Saddle point searches reveal that high-symmetry transformations are governed by "jitterbug"-type and slip-dislocation motions. Furthermore, we identify lower-barrier, asymmetric pathways that facilitate the transition into disordered, Jahn–Teller-stabilized distorted icosahedra. Using Minima Hopping sampling, we uncover a variety of low-symmetry minima, including new global minima for Au309 and Au561 that are up to 2.8 eV lower in energy than previously reported structures. In contrast to prior computational results, our predicted transformation pathways are in qualitative agreement with experimental observations, suggesting that these newly identified disordered motifs are central to the structural evolution of gold nanoclusters.

en: The transformation mechanisms among cuboctahedra, Ino's decahedra and icosahedra structures of magic-size gold nanoclusters

dft

display_name: dft

machine-learning

display_name: machine-learning

nanoclusters

display_name: nanoclusters


en: This dataset provides the computational data supporting the manuscript entitled "Molybdenum Oxidation State Governs Brønsted–Lewis Acid Synergy in Dimethyl Oxalate Transesterification" by Li et al., providing all computational data underlying the reported adsorption behaviors on MoO3(010) and MoO2(011) surfaces. It includes optimized bulk and surface structures, representative CP2K input/output files, and post-processed electron density data, enabling reproducibility of the adsorption energies and electron density difference maps for dimethyl oxalate (DMO) and ethanol. The calculations reveal that DMO adsorbs more strongly on MoO3(010) (−0.9 eV) than on MoO2(011) (−0.7 eV), reflecting more facile Lewis acid activation by the Mo6+-dominated surface. Electron density accumulates between the surface Mo and the carbonyl O atom, while the carbonyl C loses density, indicating polarization of the molecule upon adsorption. Ethanol adsorption shows even more pronounced differences: on MoO3(010), it interacts either with a surface Lewis acidic Mo atom or a Brønsted acidic Mo–OH site, both configurations exhibiting similar adsorption energies of approximately −1.1 eV. Adsorption on the Lewis acid site induces electron accumulation at the Mo–O bond and activation of the hydroxyl H, whereas adsorption on the Brønsted acid site increases the overall electronegativity of ethanol, enhancing its nucleophilicity. In contrast, ethanol binds weakly (−0.1 eV) to MoO2(011) via a hydrogen bond with a surface O atom, with negligible interaction with Mo sites. All data are organized in a comprehensive folder structure covering bulk crystal models, isolated molecules, surface optimizations, and adsorption energy evaluations, with hydroxylated and non-hydroxylated MoO3 surfaces included. Computational settings employ CP2K (GPW) with PBEsol for structural optimizations and BEEF-vdW for energy calculations, along with specified basis sets, k-point sampling, and slab models. The dataset enables verification of all figures and tables in the manuscript and facilitates detailed analysis of Brønsted–Lewis acid synergy in MoOx-catalyzed transesterification.

en: Data for the adsorption properties of dimethyl oxalate and ethanol on the MoO3(010) and MoO2(011) surfaces

adsorption

display_name: adsorption

dimethyl-oxalate

display_name: dimethyl-oxalate

electron-density-difference

display_name: electron-density-difference

moox

display_name: moox


en: Functionalizing Silicon Nanowires (SiNWs) through covalent attachment of organic molecules offers diverse advantages, including surface passivation, introduction of new functionalities, and enhanced material performance in applications like electronic devices and biosensors. Given the wide range of available functional molecules, systematic large-scale screening is crucial. Therefore, we developed an automated computational workflow using Python scripts in conjunction with the AiiDa framework to explore structural configurations of functional molecules adsorbed onto silicon surfaces. This workflow generates multiple adhesion configurations corresponding to different binding orientations using surface and functional molecule structures as inputs. This dataset contains data related to the structural optimization of molecules with single, double, and triple carbon-carbon bonds attached to the nanowire surface in various adhesion configurations. We describe the chemisorption on SiNWs using the slab models for the Si facets since our reference are samples with diameters of SiNWs around 50 nm, while the quantum confinement effects are important for diameters below 10 nm. For each configuration, structural characterization was conducted by calculating quantities including the bond distance between the two carbons closest to the surface and their respective bond angle relative to the z-axis, the carbon-silicon bond distance and its respective bond angle relative to the z-axis, along with the molecule's rotation angle in the xy plane. The values obtained are summarized in the main folder. The version v1 of dataset contains data related to the Si(111) surface and alkanes, alkenes, and alkynes with lengths from C2 to C10. The version v2 and v3 include also the moieties from C12 to C18. The version v4 extends the dataset with the data related to the Si(110) surface and alkanes, alkenes, and alkynes with lengths from C2 to C18. The version v5 also includes the optimized structures of SiNWs oriented along ⟨112⟩, exposing Si(110) and Si(111) facets, and featuring three different sizes: small (d = 0.7 nm), medium (d = 1.7 nm), and large (d = 2.6 nm). In this version (v6) the relaxation calculations for all the considered moieties are provided. For each system the most stable configuration is identified, and the analysis of the electronic properties is conducted in the listed references.

en: Density functional theory study of silicon nanowire surfaces functionalized by grafting organic molecules

aliphatic-molecules

display_name: aliphatic-molecules

nanowires

display_name: nanowires

silicon

display_name: silicon

surface-adsorption

display_name: surface-adsorption


en:
Semilocal density functionals such as the Perdew-Burke-Ernzerhof (PBE) functional notoriously underestimate experimental band gaps. Hybrid functionals address this “band gap problem” by admixing a fraction of Fock exchange to semilocal exchange. The optimal mixing parameter depends on the specific material and can be identified as the inverse dielectric constant (dielectric-dependent hybrid functional, DDH). Here, we show that dielectric constants obtained using the r²SCAN meta-GGA functional are significantly more accurate than dielectric constants obtained using the semilocal PBE functional. We propose the DD-r²SCANH functional, a dielectric-dependent hybrid functional based on r²SCAN. DD-r²SCANH can outperform the standard PBE-based DDH in terms of band gaps and other electronic structure properties. Particularly impressive improvements are obtained for narrow-gap semiconductors such as Ge and InAs, where PBE wrongly predicts a metallic phase, but r²SCAN opens a band gap.

Here, we provide VASP input files for DD-r²SCANH, and further we provide the crystal structures used for our band gap benchmark in POSCAR format.

en: Dielectric-dependent hybrid functional from meta-GGA

density-functional-theory

display_name: density-functional-theory

electronic-bands

display_name: electronic-bands

electronic-structure

display_name: electronic-structure


en: Abstract [Accepted in Phys. Rev. Lett.]: Accurate prediction of carrier mobility is critical for the discovery and design of next-generation electronic materials. Despite sustained progress, state-of-the-art ab initio methods remain limited by the approximate treatment of electron-phonon interactions at the density functional theory level. Here, we demonstrate that incorporating many-body GW corrections to both the electronic band structure and electron-phonon couplings when solving the ab initio Boltzmann transport equation yields a mean absolute relative error of just 11% for electron mobilities across benchmark semiconductors, including Si, GaAs, GaP, diamond, and SiC. The common practice of neglecting GW corrections to the electron–phonon interaction can lead to mobility errors exceeding 50%. The present findings highlight the importance of many-body self-energy effects in carrier transport simulations, and provides fundamental insights into how many-body electron--phonon interactions govern charge transport in crystalline solids.

en: Supporting data for "Many-body first principles calculations of carrier mobility with record accuracy using GW perturbation theory"

aibte

display_name: aibte

carrier-transport

display_name: carrier-transport

gw

display_name: gw

gwpt

display_name: gwpt

mobility

display_name: mobility

quantum-many-body

display_name: quantum-many-body

semiconductors

display_name: semiconductors


materialscloud

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

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


en: The ethanol steam reforming reaction, together with the adsorption and decomposition of ethanol was studied on CeO2 and gallium-doped ceria (CeGaOx) by a combined experimental and theoretical approach using infrared spectroscopy (IR), mass spectrometry (MS) and density functional theory (DFT) calculations. At 100 °C, different types of monodentate ethoxy species were identified as standing-up (SU) on Ce4+ and lying-down (LD) on Ce4+ and Ga3+, with the alkyl chain more perpendicular or parallel to the surface, respectively. It is suggested that the incorporation of Ga into the ceria lattice changes the decomposition pathway of LD species, which converts to acetate instead of ethylene, attributed to the increased lattice oxygen lability in the Ce―O―Ga interface upon doping and the propensity to form Ga―H surface species. Under ethanol steam reforming conditions, Ga doping of ceria-based materials has a drastic effect by improving the H2:CO2 ratio, changing the product distribution and reducing coke formation.

en: Controlled selectivity for ethanol steam reforming reaction over doped CeO2 surfaces: The role of gallium

bioethanol

display_name: bioethanol

coke-precursors

display_name: coke-precursors

deactivation

display_name: deactivation

gallia

display_name: gallia

hydrogen-production

display_name: hydrogen-production

prace

display_name: prace


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

materialscloud

en: Materials Cloud

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

database

display_name: database

dft

display_name: dft

electronic-properties

display_name: electronic-properties

high-throughput

display_name: high-throughput

magnetic-properties

display_name: magnetic-properties

marvel

display_name: marvel

monolayer

display_name: monolayer

phonons

display_name: phonons

two-dimensional

display_name: two-dimensional

vibrational-properties

display_name: vibrational-properties


en: The energy level alignment across solvated molecule/semiconductor interfaces is a crucial property for the correct functioning of dye-sensitized photo-electrodes, where, following the absorption of solar light, a cascade of interfacial hole/electron transfer processes has to efficiently take place. In light of the difficulty of performing X-ray photoelectron spectroscopy measurements at the molecule/solvent/metal-oxide interface, being able to accurately predict the level alignment by first-principles calculations on realistic structural models would represent an important step toward the optimization of the device. In this respect dye/NiO surfaces, employed in p-type dye-sensitized solar cells, are undoubtedly challenging for ab initio methods and, also for this reason, much less investigated than the n-type dye/TiO2 counterpart. Here we consider the C343-sensitized NiO surface in water and combine ab initio Molecular Dynamics (AIMD) simulations with GW (G0W0) calculations, performed along the MD trajectory, to reliably describe the structure and energetics of the interface when explicit solvation and finite temperature effects are accounted for. We show that the differential perturbative correction on the NiO and molecule states obtained at GW level is mandatory to recover the correct (physical) interfacial energetics, allowing hole transfer from the semiconductor valence band to the HOMO of the dye. Moreover, the calculated average driving force quantitatively agrees with the experimental estimate.

en: Structure and energetics of dye-sensitized NiO interfaces in water from ab-initio MD and large-scale GW calculations

envi

en: Environment

ab-initio

display_name: ab-initio

energy-level-alignment

display_name: energy-level-alignment

gw

display_name: gw

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

nio

display_name: nio

p-type-dssc

display_name: p-type-dssc


en:
The high-throughput molecular exploration and screening of organic electronic materials often starts with either a 'top-down' mining of existing repositories, or the 'bottom-up' assembly of fragments based on predetermined rules and known synthetic templates. In both instances, the datasets used are often produced on a case-by-case basis, and require the high-quality computation of electronic properties and extensive user input: curation in the top-down approach, and the construction of a fragment library and introduction of rules for linking them in the bottom-up approach. Both approaches are time-consuming and require significant computational resources. Here, we generate a top-down set named FORMED consisting of 117K synthesized molecules containing their optimized structures, associated electronic and topological properties and chemical composition, and use these structures as a vast library of molecular building blocks for bottom-up fragment-based materials design. A tool is developed to automate the coupling of these building block units based on their available Csp2-H bonds, thus providing a fundamental link between the two philosophies of dataset construction. Statistical models are trained on this dataset and a subset of the resulting hybrid top-down/bottom-up compounds (selected dimers), which enable on-the-fly prediction of key ground state (frontier molecular orbital gaps) and excited state (S1 and T1 energies) properties from molecular geometries with high accuracy across all known p-block organic compound space.
With access to ab initio-quality optical properties in hand, it is possible to apply this bottom-up pipeline using existing compounds as molecular building blocks to any materials design campaign. To illustrate this, we construct and screen over a million molecular candidates (predicted dimers) for efficient intramolecular singlet fission, the leading candidates of which provide insight into the structural features that may promote this multiexciton-generating process.

en: Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design

crystal-structures

display_name: crystal-structures

donor-acceptor-copolymers

display_name: donor-acceptor-copolymers

optical-properties

display_name: optical-properties

organic-molecules

display_name: organic-molecules

photophysical-properties

display_name: photophysical-properties


en: The chemical versatility of carbon imparts manifold properties to organic compounds, where magnetism remains one of the most desirable but elusive. Polycyclic aromatic hydrocarbons, also referred to as nanographenes, show a critical dependence of electronic structure on the topologies of the edges and the π-electron network, which makes them model systems with which to engineer unconventional properties including magnetism. In 1972, Erich Clar envisioned a bow-tie-shaped nanographene, C38H18, where topological frustration in the π-electron network renders it impossible to assign a classical Kekulé structure without leaving unpaired electrons, driving the system into a magnetically non-trivial ground state. In this record we include data needed to support our recent work where we demonstrate the experimental realization and in-depth characterization of this emblematic nanographene, known as Clar's goblet. Scanning tunnelling microscopy and spin excitation spectroscopy of individual molecules on a gold surface reveal a robust antiferromagnetic order with an exchange-coupling strength of 23 meV, exceeding the Landauer limit of minimum energy dissipation at room temperature. Through atomic manipulation, we realize switching of magnetic ground states in molecules with quenched spins. Our results provide direct evidence of carbon magnetism in a hitherto unrealized class of nanographenes, and prove a long-predicted paradigm where topological frustration entails unconventional magnetism, with implications for room-temperature carbon-based spintronics.

en: Topological frustration induces unconventional magnetism in a nanographene

clar-goblet

display_name: clar-goblet

erc

display_name: erc

kondo

display_name: kondo

nanographene

display_name: nanographene

snsf

display_name: snsf


en: This archive includes the raw data for atomistic simulations in the work as titled. Transition to a hydrogen-based economy requires a thorough understanding of hydrogen interaction with dislocations in metals, especially in body-centered cubic (BCC) steels. Past experimental and computational investigations regarding these interactions often demonstrate two opposing results: hydrogen-induced mobility or hydrogen-induced pinning of dislocations. Through in-situ scanning electron microscopy experiments enabled by a custom-built setup, we address here this discrepancy. Our experiments reveal hydrogen-induced dislocation motion in a BCC metal at room temperature. Interestingly however, we also observe that the same dislocations are later pinned as well, again induced by the steady hydrogen flux. Molecular dynamics simulations of the phenomena confirm the attraction of the dislocations towards the hydrogen flux, and the pinning that follows after, upon increased hydrogen trapping at the dislocation core. Future experimental or computational studies of hydrogen thus should take into account these different regimes in order to present a full picture of hydrogen defect interactions.

en: Hydrogen can both move or pin dislocations in Body-Centered Cubic metals

body-centered-cubic-metal

display_name: body-centered-cubic-metal

dislocation-hydrogen-interactions

display_name: dislocation-hydrogen-interactions

hydrogen-embrittlement

display_name: hydrogen-embrittlement


a-unified-approach-to-enhanced-sampling

en: The sampling problem lies at the heart of atomistic simulations and over the years many different enhanced sampling methods have been suggested towards its solution. These methods are often grouped into two broad families. On the one hand methods such as umbrella sampling and metadynamics that build a bias potential based on few order parameters or collective variables. On the other hand tempering methods such as replica exchange that combine different thermodynamic ensembles in one single expanded ensemble. We adopt instead a unifying perspective, focusing on the target probability distribution sampled by the different methods. This allows us to introduce a new method that can sample any of the ensembles normally sampled via replica exchange, but does so in a collective-variables-based scheme. This method is an extension of the recently developed on-the-fly probability enhanced sampling method [Invernizzi and Parrinello, J. Phys. Chem. Lett. 11.7 (2020)] that has been previously used for metadynamics-like sampling. The method is thus very general and can be used to achieve different types of enhanced sampling. It is also reliable and simple to use, since it presents only few and robust external parameters and has a straightforward reweighting scheme. Furthermore, it can be used with any number of parallel replicas. We show the versatility of our approach with applications to multicanonical and multithermal-multibaric simulations, thermodynamic integration, umbrella sampling, and combinations thereof.

en: A unified approach to enhanced sampling

title_for_slug: a-unified-approach-to-enhanced-sampling

expanded-ensembles

display_name: expanded-ensembles

free-energy

display_name: free-energy

importance-sampling

display_name: importance-sampling

metadynamics

display_name: metadynamics

parallel-tempering

display_name: parallel-tempering

replica-exchange

display_name: replica-exchange

thermodynamic-integration

display_name: thermodynamic-integration

umbrella-sampling

display_name: umbrella-sampling