en: DFT calculations were carried out using the CP2K and Quantum ESPRESSO packages to study the structural, electronic, and catalytic properties of platinum nanoparticles supported on ceria surfaces. The models included Pt nanoparticles of different sizes on CeO₂ surfaces. The DFT+U approach was used to describe Ce 4f electrons, and 4f-in-core pseudopotentials were employed to model Ce³⁺ species. Adsorption energies, XPS binding energies, and reaction energies were computed to analyze the interaction of Pt with the ceria support and adsorbate (CO), providing insights into the size-dependent electronic behavior and catalytic activity of Pt species.

materialscloud

en: Materials Cloud

en: Quantifying electronic and geometric effects on the activity of platinum catalysts for water-gas shift

dft

display_name: dft

nanoparticle

display_name: nanoparticle


$oid: OBSOLETE

en: The direct conversion of methane to methanol (DCMM) under continuous flow and atmospheric pressure offers notable environmental benefits and industrial promise, but remains a long-standing challenge due to the difficulty of activating CH4 while avoiding over-oxidation of methanol. Here, we demonstrate that pure ceria (CeO2), without any metal promoters, enables gas-phase DCMM with up to 80 % selectivity at 300–350 °C, upon optimization of the H2O/O2 ratio. At 550 °C, methanol and formaldehyde are formed at rates of 24 and 36 μmol g-1 h-1, respectively-both dropping below 1 μmol g-1 h-1 in the absence of O2. Ex situ transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and Raman spectroscopy confirm that CeO2 maintains structural integrity and resists carbon deposition during reaction. Combining kinetic studies, steady-state in-situ diffuse reflectance infrared Fourier transform spectroscopy (in-situ DRIFTS), and density functional theory (DFT) reveals that hydroxyl groups (OH), generated from water dissociation, play a multifaceted role: they facilitate C–H bond activation, promote methoxy formation, and enhance methanol desorption. In-situ ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) directly reveals the evolution of surface intermediates and shows that co-feeding O2 and H2O suppresses CH3O and CHx accumulation while boosting methanol yield—indicating a rapid intermediate turnover as key to sustained activity. AP-XPS O 1s spectra further highlight that O2 promotes H2O dissociation, regenerating reactive OH groups and maintaining performance at elevated temperature. These findings offer molecular-level insights into how water and oxygen cooperatively tune reactivity, enabling efficient methane-to-methanol conversion on a metal-free oxide catalyst.

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).

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

en: Synergy of oxygen and water in ceria-catalyzed direct conversion of methane to methanol under continuous flow

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

ap-xps

display_name: ap-xps

in-situ

display_name: in-situ

in-situ-drifts

display_name: in-situ-drifts

methane-to-methanol

display_name: methane-to-methanol

reaction-mechanism

display_name: reaction-mechanism


en: Transition metal dichalcogenides (TMDCs) are promising candidates for future nano-transistor channels due to their outstanding intrinsic transport properties. However, their electron mobility is highly sensitive to the surrounding dielectric, often falling well below theoretical expectations. In this work, we explore how a stacked Al2O3 dielectric affects electron mobility in monolayer WS2 using first-principles quantum transport simulations. We identify that fluctuations in the electrostatic potential, arising from the disordered structure of Al2O3, significantly degrade mobility, especially when WS2 interfaces with under-coordinated aluminum atoms. Our calculated mobilities (≃1–30 cm2/(V ⋅ s)) align with experimental observations and remain far from the ideal limit (≃300 cm2/(V ⋅ s)). We further demonstrate that encapsulating WS2 with hexagonal boron nitride (hBN) or employing a crystalline oxide can recover high mobility values. However, these strategies introduce trade-offs in electrostatic control and fabrication complexity, underlining the need for careful dielectric engineering in TMDC-based devices.

en: Mobility calculation in disordered WS2-Al2O3 stacks from first principles

mobility

display_name: mobility


en: We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud three-dimensional database (MC3D) of experimentally known inorganic stoichiometric compounds. We first compute the Allen-Dynes critical temperature Tc for 4533 non-magnetic metals using a direct and progressively finer sampling of the electron-phonon couplings. For the candidates with the largest Tc, we use automated Wannierizations and electron-phonon interpolations to obtain a high-quality dataset for the most promising 250 dynamically stable structures, for which we calculate spectral functions, superconducting bandgaps, and isotropic Migdal-Eliashberg critical temperatures. For 140 of these, we also provide anisotropic Migdal-Eliashberg superconducting gaps and critical temperatures. The approach is remarkably successful in finding known superconductors, and we find 24 unknown ones with a predicted anisotropic Tc above 10 K. Among them, we identify a possible double gap superconductor (p-doped BaB2), a non-magnetic half-Heusler ZrRuSb, and the perovskite TaRu3C, all exhibiting significant Tc. Finally, we introduce a sensitivity analysis to estimate the robustness of the predictions.

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

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

migdal-eliashberg

display_name: migdal-eliashberg

superconductivity

display_name: superconductivity


en:
Identifying organic molecules with desirable properties from the extensive chemical space can be challenging, particularly when property evaluation methods are time-consuming and resource intensive. In this study, we illustrate this challenge by exploring the chemical space of large oligomers, constructed from monomeric building blocks, for potential use in organic photovoltaics (OPV). To facilitate this exploration, we developed a Python package called stk-search, which employs a building block approach. For this purpose, we developed a python package to search the chemical space using a building block approach: stk-search. We use stk-search (GitHub link) to compare a variety of search algorithms, including those based upon Bayesian optimization and evolutionary approaches. Initially, we evaluated and compared the performance of different search algorithms within a precomputed search space. We then extended our investigation to the vast chemical space of molecules formed of 6 building blocks (6-mers), comprising over 1014 molecules. Notably, while some algorithms show only marginal improvements over a random search approach in a relatively small, precomputed, search space, their performance in the larger chemical space is orders of magnitude better. Specifically, Bayesian optimization identified a thousand times more promising molecules with the desired properties compared to random search, using the same computational resources.
This record contains the dataset generated during the exploration of the space of molecules formed of 6 building blocks for application as donor molecules for OPV application, with calculated properties such as Ionisation potential, excited state energy and oscillator strength.

en: Exploring different search approaches to discover donor molecules for organic solar cells

bayesian-optimisation

display_name: bayesian-optimisation

building-block-approach

display_name: building-block-approach

genetic-algorithm

display_name: genetic-algorithm

organic-solar-cells

display_name: organic-solar-cells

representation-learning

display_name: representation-learning


en: Rare-earth superhydrides have attracted considerable attention because of their high critical superconducting temperature under extreme pressures. They are known to have localized valence electrons, implying strong electronic correlations. However, such many-body effects are rarely included in first-principles studies of rare-earth superhydrides because of the complexity of their high-pressure phases. In this work, we use a combined density functional theory and dynamical mean-field theory approach to study both electrons and phonons in the prototypical rare-earth superhydride CeH9, shedding light on the impact of electronic correlations on its critical temperature for phonon-mediated superconductivity. Our findings indicate that electronic correlations result in a larger electronic density at the Fermi level, a bigger superconducting gap, and softer vibrational modes associated with hydrogen atoms. Together, the inclusion of these correlation signatures increases the Migdal-Eliashberg superconducting critical temperature from 47 K to 96 K, close to the measured 95 K. Our results reconcile experimental observations and theoretical predictions for CeH9 and herald a path towards the quantitative modeling of phonon-mediated superconductivity for interacting electron systems.

en: Impact of electronic correlations on the superconductivity of high-pressure CeH9

electronic-correlations

display_name: electronic-correlations

high-pressure

display_name: high-pressure


en: We develop a convolutional neural network (NN) approach able to predict the elastic contribution to chemical potential μₑ at the surface of a 2D strained film given its profile h(x). Arbitrary h(x) profiles are obtained by using a Perlin Noise generator and the corresponding μₑ profiles are calculated either by a Green's function approximation (GA) or by Finite Element Method (FEM). First, a large dataset is produced by exploiting the GA method and it is then used for the training of the NN model. The performance of the trained NN is extensively examined, demonstrating its ability to predict μₑ looking both to the training/validation set and to an additional testing set containing different profiles, including sinusoids, gaussians and sharp peaks never considered in the NN training. The NN is then applied to simulate the morphological evolution of strained Ge films, where the predicted μₑ at each integration timestep plays the role of driving force for material redistribution in competition with a surface energy term (proportional to local curvature) and eventually a wetting energy contribution. Both surface diffusion and evaporation/condensation dynamics are considered and the proposed NN approach is shown to well match the evolution expected by using GA. On this basis, a smaller dataset is built with μₑ profiles calculated by FEM and a new NN model is trained on it. Once again the trained NN-model returns reliable prediction of the FEM μₑ. The findings suggest that the proposed NN-based strategy can be used in replacement of the computationally intensive FEM calculations, enabling the simulation of larger scales and longer time scales untreatable by direct FEM calculation.

en: Deep learning of surface elastic chemical potential in strained films: from statics to dynamics

deep-learning

display_name: deep-learning

elastic-chemical-potential

display_name: elastic-chemical-potential

surface-evolution

display_name: surface-evolution

thin-films

display_name: thin-films


en:
The rapid progress in generative models for molecular design has led to extensive libraries of candidate molecules for biological and chemical applications. However, ensuring these molecules are diverse and representative of broader chemical space remains challenging, with researchers often over-exploring limited regions or missing promising candidates due to inadequate monitoring tools. This work presents NaviDiv (Navigating Diversity in Chemical Space), a comprehensive web-based framework for managing chemical diversity in the string-based generative molecular design through three integrated capabilities: multi-metric diversity analysis capturing structural, syntactic, and molecular framework variations; interactive real-time visualization enabling immediate detection of model collapse; and adaptive constraint generation that dynamically guides optimization while preserving diversity. Through a singlet fission material discovery case study using REINVENT4, we demonstrate that different diversity metrics (i.e. structural similarity, fragment composition, and sequence patterns) respond differently during optimization, with constraint effectiveness depending critically on representational alignment with the generative model. N-gram-based constraints outperform fingerprint-based approaches due to direct correspondence with SMILES generation, while combined constraints maintain diversity across all metrics while achieving optimization performance within 15\% of unconstrained baselines. The framework is freely available at : https://github.com/LCMD-epfl/NaviDiv, providing accessible tools for data-driven decisions about diversity-property trade-offs in automated molecular discovery.

This dataset contains all materials generated during the singlet fission material discovery case study presented in this work. It includes: (1) the REINVENT4 prior model used for molecule generation, (2) the trained ChemProp model checkpoints used to evaluate singlet fission potential, and (3) the generated molecules from each experimental run. For each run, molecules are stored in a CSV file alongside the corresponding chemical diversity analysis outputs in the same directory.

en: NaviDiv: a web app for monitoring chemical diversity in generative molecular design

chemical-diversity

display_name: chemical-diversity

generative-models

display_name: generative-models

singlet-fission

display_name: singlet-fission


en: Diboron trioxide (B2O3) represents an unusual case among polymorphic oxides, for its vitrified state features superstructural units – planar boroxol groups – that are never observed in its three-dimensional crystalline polymorphs. Crystalline polymorphs that incorporate boroxol groups have only been predicted theoretically, although their formation is crucial to rationalize the ability of B2O3 to vitrify. Here we present the synthesis of a two-dimensional crystalline B2O3 polymorph constituted by boroxol groups arranged in an atomically thin honeycomb lattice. By combining surface science experimental techniques with ab initio calculations, we characterize the structural and electronic properties of this B2O3 polymorph down to the atomic level. This discovery enlarges the family of two-dimensional materials and enables the atomic tracking of individual structural units in trioxides.

materialscloud

en: Materials Cloud

en: Two-dimensional diboron trioxide crystal composed by boroxol groups

boroxol-groups

display_name: boroxol-groups


en: Recent advances in the synthesis of graphene fragments that possess unpaired π-electrons and display high-spin ground states have unlocked possibilities to explore exotic physical phenomena related to magnetism. The high degree of spin-delocalisation makes these non-metal-based systems ideal building blocks for the construction of chains and lattices with strongly correlated magnetic ground states, which is the main requisite for measurement-based quantum computation.In a recent publication, we demonstrate the magnetic bistability of a diradical nanographene that allows direct spin manipulation at the single-molecule level. To this end, we made use of solution-phase synthesis and tip-induced activation on a metallic surface to construct a helical non-Kekulé hydrocarbon spin switch, with a reversible transformation between a magnetic ground state and a non-magnetic one via intramolecular bond formation/breaking. The switching process was monitored by scanning tunneling spectroscopy measurements, illustrating that this, and related systems, hold potential as spin-switch units for direct manipulation of magnetism and quantum information in entangled spin systems. This record contains data that support the results discussed in the publication.

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: Controlled magnetic bistability of a helical non-Kekulé hydrocarbon on a Au(111) surface

ab-initio-simulations

display_name: ab-initio-simulations

hydrocarbons

display_name: hydrocarbons

magnetic-nanocarbons

display_name: magnetic-nanocarbons

scanning-probe-microscopy

display_name: scanning-probe-microscopy

snsf

display_name: snsf


en: Optimal exploitation of supercomputing resources for the evaluation of electrostatic forces remains a challenge in molecular dynamics simulations of very large systems. The most efficient methods are currently based on particle-mesh Ewald sums and achieve semi-logarithmic scaling in the number of particles. These methods solve the problem in reciprocal space, requiring extensive use of Fast Fourier transforms (FFTs). While highly efficient in many contexts, FFTs may encounter scalability challenges at very large processor counts due to their communication requirements. To mitigate these problems, the development and scalable coding of real-space approaches to solve the Poisson equation on a grid is an active field of research. In this work, we introduce a novel real-space approach that provides some advantages over alternatives. Our method exploits an extended Lagrangian in which the values of the field at the grid points are treated as auxiliary variables of zero inertia and the discretized Poisson equation is enforced as a dynamical constraint. The solution of the constraints leads to a linear system - different from those appearing in other real-space approaches - that can be efficiently solved via state-of-the-art iterative methods. The method inherits the numerical scaling of the adopted iterative solver, e.g. linear with a multigrid (MG) approach, but converges with fewer cycles. We analyze this approach considering realistic simulations of molten NaCl that validate its ability to reproduce structural and transport properties. Using this non-trivial benchmark, we demonstrate linear scaling and illustrate some features of our algorithm.

en: Mass-zero constrained molecular dynamics for electrostatic interactions

envi

en: Environment

cecam

display_name: cecam

electrostatic-interactions

display_name: electrostatic-interactions

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation

molten-salts

display_name: molten-salts


en: PROTACs are of high interest because of their ability to target previously challenging disease-related proteins. PROTACs promote the degradation of targets by ensuring the proximity of the E3-ligase and the target. The relation between structure and function of the ternary complex is of great interest. Of equal importance is the delivery of these drugs and determining the key interactions that stabilize the solid drug forms. Here, we determine the complete atomic-level structure of an amorphous VHL-based SMARCA2 PROTAC (PROTAC 2). The structure is solved using ultra-fast MAS, and DNP enhanced, NMR crystallography. We found that PROTAC 2 is more disordered as compared to previously studied amorphous formulations, and that the three functional units of the molecule have three distinct structural types. Overall, in contrast to structures of smaller drug molecules, where intermolecular H-bonding interactions were found to be the main stabilization mechanism for the amorphous solid form, for PROTAC 2 we postulate that, in analogy to glassy polymers, the main stabilization mechanism of the amorphous form is the entropic contribution introduced by the overall flexibility, especially in the linker region of the molecule. We also note that the most populated conformations found in the amorphous form differ from those of bound PROTAC 2 found in the ternary protein complex as determined via X-ray crystallography. Our results provide insight into key structural features that stabilize amorphous formulations, specifically for molecules that can target proteins previously considered undruggable.

en: Determination of key functional structures of an amorphous VHL-based SMARCA2 PROTAC

amorphous

display_name: amorphous

drug-formulation

display_name: drug-formulation

nmr

display_name: nmr


en: Strongly correlated physics arises from electron-electron scattering within partially filled orbitals. Organic molecules in open-shell configurations are therefore good candidates to exhibit many-body effects. We focus on electron transport in a two-terminal single-molecule junction setup, where the molecular bridge consists of an organic radical with a molecular orbital that hosts a single unpaired electron (SOMO). We perform beyond state-of-the-art numerical simulations combining an ab-initiodescription of the chemical environment, with quantum field-theoretical techniques that account for many-body effects. The key observation is that the SOMO resonance is prone to splitting and we identify a giant electronic scattering rate as the driving many-body mechanism, akin to that of the Mott metal-to-insulator transition. By comparing linear and cyclic radicals, we show that the spatial distribution of the SOMO and its projection on the molecular backbone have dramatic consequences for the transport properties of the junction. We argue that the phenomenon and the underlying microscopic mechanism apply to a broad family of open-shell molecular systems, and can explain puzzling experimental observations such as suppressed conductance in radical junctions.

en: Strongly correlated physics in organic open-shell quantum systems

dft

display_name: dft

dmft

display_name: dmft

exact-diagonalization

display_name: exact-diagonalization

transport

display_name: transport


en: We present a high-throughput computational screening for fast lithium-ion conductors to identify promising materials for application in all solid-state electrolytes. Starting from more than 30,000 Li-containing experimental structures sourced from Crystallography Open Database, Inorganic Crystal Structure Database and Materials Platform for Data Science, we perform highly automated calculations to identify electronic insulators. On these ~1000 structures, we use molecular dynamics simulations to estimate Li-ion diffusivities using the pinball model, which describes the potential energy landscape of diffusing lithium with accuracy similar to density functional theory while being 200-500 times faster. Then we study the ~60 most promising and previously unknown fast conductors with full first-principles molecular dynamics simulations at several temperatures to estimate their activation barriers. The MD trajectories of these structures are included along with all the structures on which electronic strutcure calculations were performed. The entire screening provenance is provided for one structure to facilitate the reproduction of this screening.

en: Novel fast Li-ion conductors for solid-state electrolytes from first-principles

computational-high-throughput-screening

display_name: computational-high-throughput-screening

first-principles

display_name: first-principles

li-ion-conductors

display_name: li-ion-conductors

molecular-dynamics

display_name: molecular-dynamics

solid-state-electrolytes

display_name: solid-state-electrolytes


en: Core-shell (CS) microgels–comprising a solid core chemically linked to a crosslinked polymer shell–are an important class of stimuli-responsive colloids widely employed for both fundamental and applied studies. Despite extensive experimental investigations, their numerical modeling remains underdeveloped; in particular, a detailed, temperature-resolved insight of their effective interactions is missing. While pairwise descriptions known for standard microgels are expected to be partially extensible to CS-microgels, the effect of the solid core and in turn, the extent and character of many-body contributions remain elusive. To clarify these open issues, this work introduces a computational method to generate realistic CS-microgels at different crosslinker concentrations, accurately reproducing the structure of experimental silica core–poly-N-isopropylacrylamide (pNIPAM) shell microgels at different temperatures across the Volume Phase Transition (VPT) and for different shell-to-core ratios. After validating the model, effective interactions between CS-microgels are calculated, extracting not only two-body interactions but also providing the first quantitative investigation of three-body effects in any microgel system. The present findings show that, albeit small, the latter contributions are non-negligible and exhibit an intriguing sign reversal: while being predominantly attractive at low temperatures, they become repulsive at high ones, nearly canceling out at the VPT. These results reveal a subtle interplay between microgel architecture, temperature and many-body correlations, with direct implications on the design of responsive soft materials and their mutual interactions.

en: Numerical investigation of realistic core-shell microgels: insights on two- and three-body effective interactions

coarse-grained-models

display_name: coarse-grained-models

core-shell-microgels

display_name: core-shell-microgels

effective-interaction

display_name: effective-interaction

many-body-effects

display_name: many-body-effects

molecular-simulation

display_name: molecular-simulation


en: Microgels made of poly(N-isopropylacrylamide) are the prototype of soft, thermoresponsive particles widely used to study fundamental problems in condensed matter physics. However, their internal structure is far from homogeneous, and existing mean-field approaches, such as Flory–Rehner theory, provide only qualitative descriptions of their thermoresponsive behavior. Here, we combine machine learning and numerical simulations to accurately predict the concentration and spatial distribution of crosslinkers, the latter hitherto unknown experimentally, as well as the full swelling behavior of microgels, using only polymer density profiles. Our approach provides unprecedented insight into the structural and thermodynamic properties of any standard microgel.

en: Predicting the structure and swelling of microgels with different crosslinker concentrations by combining machine learning with numerical simulations

machine-learning

display_name: machine-learning

microgels

display_name: microgels

volume-phase-transition

display_name: volume-phase-transition


en: Understanding how oxide nanomorphology directs metal–support interactions is key to designing selective, low-cost catalysts. The preferential oxidation of CO (CO-PROX) is vital for purifying H₂ streams for fuel cell applications, as even trace CO strongly poisons the electrode catalysts. Cu/CeO2 systems provide a cost-effective alternative to noble metals, yet the influence of ceria morphology on performance remains unclear. Here, we compare low-loaded Cu catalysts supported on CeO2 nanospheres and nanocubes. Although distinct in shape, electron microscopy, low-temperature CO adsorption infrared spectroscopy, and DFT calculations reveal surface reconstructions in nanocubes that diminish structural differences between the two supports. Nevertheless, the Cu/nanosphere catalyst shows higher CO oxidation activity, while the Cu/nanocube catalyst offers superior CO₂ selectivity and a broader full-conversion temperature window. In situ DRIFTS and DFT attribute these contrasts to stronger CO adsorption sites in the nanocube system. Copper speciation and the nature of surface carbonyls were resolved through complementary techniques including STEM-HAADF imaging, XEDS mapping, EPR, and CO adsorption IR spectroscopy, together with DFT. These results demonstrate that subtle variations in ceria morphology steer interfacial chemistry and reaction pathways, providing design principles for next-generation Cu-based catalysts for CO-PROX and related oxidation reactions.

materialscloud

en: Materials Cloud

en: Fine-tuning catalysts: The role of support nanomorphology in shaping Cu/CeO2 CO-PROX properties

carbonyls-infrared

display_name: carbonyls-infrared

co-prox

display_name: co-prox

dft

display_name: dft

electron-microscopy

display_name: electron-microscopy

hydrogen

display_name: hydrogen


en: This dataset accompanies the study "Getting the manifold right: The crucial role of orbital resolution in DFT+U for mixed d-f electron compounds" and provides input and output files used to investigate the electronic structure of AUO4 compunds (A = Ni, Mn, Co). The calculations systematically compare standard PBEsol density-functional theory (DFT), shell-averaged DFT+U, and orbital-resolved DFT+U approaches based on orthogonalized atomic orbital (OAO) projectors. The dataset includes self-consistent-field calculations, geometry optimizations, and projected density of states data. In addition, hybrid-functional HSE06 calculations performed with VASP are provided for benchmarking purposes. A detailed molecular orbital and bonding analysis for NiUO4, carried out using the LOBSTER code, is also included to elucidate orbital hybridization and chemical bonding effects.

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).

actinide-materials

display_name: actinide-materials

dftu

display_name: dftu

electronic-structure

display_name: electronic-structure

orbital-resolved

display_name: orbital-resolved


en: Maximally-localized Wannier functions (MLWFs) are widely employed as an essential tool for calculating the physical properties of materials due to their localized nature and computational efficiency. Projectability-disentangled Wannier functions (PDWFs) have recently emerged as a reliable and efficient approach for automatically constructing MLWFs that span both occupied and lowest unoccupied bands. Here, we extend the applicability of PDWFs to magnetic systems and/or those including spin-orbit coupling, and implement such extensions in automated workflows. Furthermore, we enhance the robustness and reliability of constructing PDWFs by defining an extended protocol that automatically expands the projectors manifold, when required, by introducing additional appropriate hydrogenic atomic orbitals. We benchmark our extended protocol on a set of 200 chemically diverse materials, as well as on the 40 systems with the largest band distance obtained with the standard PDWF approach, showing that on our test set the present approach delivers a success rate of over 98% in obtaining accurate Wannier-function interpolations, defined as an average band distance below 20 meV between the DFT and Wannier-interpolated bands, up to 2 eV above the Fermi level for metals or above the conduction band minimum for insulators (and a 100% success rate when including only bands up to 1 eV above these values).

en: Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement

aiida

display_name: aiida

automation

display_name: automation

marvel

display_name: marvel

projectability-disentanglement

display_name: projectability-disentanglement

snsf

display_name: snsf

wannier-functions

display_name: wannier-functions

wannierization

display_name: wannierization


en: Here we present an on-demand strategy for electrolytes design to surpass 99.9% Coulombic efficiency (CE) in zinc metal anode. This strategy synergizes various effects by specifically targeting the two critical factors: plating morphology and the anode-electrolyte interface.  In this dataset, we simulated the solvation structures and bilayer structures of various electrolytes by molecular dynamics simulations. We found Triethyl phosphate and dimethylformamide can  induce the free-water-poor inner Helmholtz plane and reduce the interfacial water activity. Furthermore, the MD results imply that the dual-salt introduces more anions into the Zn2+ primary solvation sheath, and the DMF co-solvent is also able to enter the solvation sheath.

en: Electrolyte design for reversible zinc metal chemistry

envi

en: Environment

batteries

display_name: batteries

electrolyte

display_name: electrolyte

molecular-dynamics

display_name: molecular-dynamics


en: Recent advances in nanomaterials have pushed the boundaries of nanoscale fabrication to the limit of single atoms, particularly in heterogeneous catalysis. Single atom catalysts, comprising minute amounts of transition metals dispersed on inert substrates, have emerged as prominent materials in this domain. However, overcoming the tendency of these single atoms to cluster beyond cryogenic temperatures and precisely arranging them on surfaces with desired local environments pose significant challenges. Employing organic templates for orchestrating and modulating the activity of single atoms holds promise. In recent work, we introduce a novel single-atom platform wherein atoms are firmly anchored to specific coordination sites distributed along carbon-based polymers, synthesised via on-surface synthesis. These platforms exhibit atomic-level structural precision and stability, even at elevated temperatures. The asymmetry in the structure and electronic states at the active sites anticipates the enhanced reactivity of these precisely defined reactive centers. Upon exposure to CO and CO2 gases at low temperatures, the platform demonstrates excellent trapping and conversion capabilities. Fine-tuning the structure and properties of the coordination sites offers unparalleled flexibility in tailoring functionalities, thus opening avenues for previously untapped potential in catalytic applications. This record contains data that support the results presented in the published work.

en: On-surface synthesis of tailored organic platforms for single metal atoms

asymmetric-active-sites

display_name: asymmetric-active-sites

cscs

display_name: cscs

dft

display_name: dft

on-surface-synthesis

display_name: on-surface-synthesis

single-atom-catalyst

display_name: single-atom-catalyst

spm

display_name: spm


en: Hollow microgels are elastic polymer shells easily realizable in experiments. Recent works have shown the emergence of buckling events in dilute hollow microgels under the effect of an added osmotic pressure. Here, we perform large-scale simulations to show that these microgels at high enough packing fractions undergo spontaneous symmetry-breaking deformations ranging from single large dents to multiple indentations, even in the absence of any externally applied stress. This self-induced buckling phenomenon is thus solely driven by interparticle crowding. We construct a phase diagram inspired by vesicle shape theories, mapping local curvature metrics as a function of the reduced volume, to quantify these findings, and we also propose ways to observe the occurrence of buckling in experiments. The present results thus rationalize the deformations occurring in suspensions of micro- and nanoscale elastic shells, offering a synthetic analogue to biological ones and allowing direct control on buckling instabilities for potential applications. Beyond materials design, these insights may also help to describe shape regulation in natural systems such as cells and vesicles, where similar deformations are observed.

en: Self-induced buckling in hollow microgels

buckling-instability

display_name: buckling-instability

microgels

display_name: microgels

monomer-resolved-simulations

display_name: monomer-resolved-simulations


en:
The past few years have seen the development of ``universal'' machine-learning interatomic potentials (uMLIPs) capable of approximating the ground-state potential energy surface across a wide range of chemical structures and compositions with reasonable accuracy. While these models differ in the architecture and the dataset used, they share the ability to compress a staggering amount of chemical information into descriptive latent features. Herein, we systematically analyze what the different uMLIPs have learned by quantitatively assessing the relative information content of their latent features with feature reconstruction errors as metrics, and observing how the trends are affected by the choice of training set and training protocol. We find that the uMLIPs encode chemical space in significantly distinct ways, with substantial cross-model feature reconstruction errors. When variants of the same model architecture are considered, trends become dependent on the dataset, target, and training protocol of choice. We also observe that fine-tuning of a uMLIP retains a strong pre-training bias in the latent features. Finally, we discuss how atom-level features, which are directly output by MLIPs, can be compressed into global structure-level features via concatenation of progressive cumulants, each adding significantly new information about the variability across the atomic environments within a given system.

This repository contains the scripts to reproduce results presented in the associated paper/preprint.

en: Comparing the latent features of universal machine-learning interatomic potentials

atomistic-machine-learning

display_name: atomistic-machine-learning

feature-comparison

display_name: feature-comparison

interatomic-potentials

display_name: interatomic-potentials

machine-learning

display_name: machine-learning


en: This dataset contains a list of ComputedStructureEntry objects used to parameterize an embedded cluster expansion in the paper https://doi.org/10.1016/j.actamat.2025.121752. We used this model to predict finite-temperature vacancy concentrations in 9-component refractory alloys (Ti-Zr-Hf-V-Nb-Ta-Cr-Mo-W). The symmetrically distinct orderings of group 5 and 6 elements in small cells correspond to calculations reported in https://doi.org/10.1038/s41524-025-01543-3.

en: Modeling the equilibrium vacancy concentration in multi-principal element alloys from first-principles

embedded-cluster-expansion

display_name: embedded-cluster-expansion

equilbrium-vacancy-concentration

display_name: equilbrium-vacancy-concentration


en: In this work we provide the results for ionization potential (IP) and electron affinity (EA) of all the 100 molecules of the set, as computed at the G0W0 level within the Yambo code. In this way, we enlarge the GW100 benchmark considering the largely used Godby-Need Plasmon Pole Approximation (GNPPA) and the recently introduced multipole approximation (MPA), both used in Yambo to describe the frequency dependence of the screening potential and not yet included in previous GW100 studies. Fully converged results on HOMO and LUMO are provided here for each molecule of the GW100 set, as extrapolated from Yambo calculations with different value of the involved parameters (plane wave cutoff for the microscopic dielectric matrix and empty state summations).

materialscloud

en: Materials Cloud

en: Benchmarking the plasmon-pole and multipole approximations in the Yambo Code using the GW100 dataset

aiida

display_name: aiida

aiida-yambo

display_name: aiida-yambo

first-principles

display_name: first-principles

godby-need-plasmon-pole-approximation

display_name: godby-need-plasmon-pole-approximation

gw

display_name: gw

high-throughput

display_name: high-throughput

mpa

display_name: mpa

multipole-approximation

display_name: multipole-approximation

yambo

display_name: yambo

yambo-code

display_name: yambo-code


en: The overall development of the machine learning force field (MLFF) has advanced rapidly, with a wide range of models emerging in recent years. However, some fundamental questions remain underexplored, such as why certain systems are intrinsically more difficult to train than others. Understanding this question can help us to propose different models and prepare appropriate datasets for different situations. We constructed Metal-43, a high-quality dataset comprising elemental structures of 43 metallic elements. Through systematic analysis, we reveal regular trends of fitting accuracies of these elemental metals in the periodic table. Unlike previous approaches that generally attribute fitting challenges to a vague notion of a “complex potential energy surface (PES)”, which is almost a synonym of the fitting difficulty, we provide a physical picture which connects the Fermi surface complexity to this complexity of PES. Furthermore, we demonstrate that current MLFF models still face clear limitations in capturing the complex PES even for elemental materials. These findings can provide a theoretical foundation and directional guidance for the development of more general and accurate MLFF models in the future.

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: Origin of the machine learning forces field errors across metal elements

dataset

display_name: dataset

density-functional-theory

display_name: density-functional-theory

machine-learning-force-field

display_name: machine-learning-force-field


en: Achieving n- and p-type symmetric and excellent two-dimensional Schottky barrier field-effect transistors (SBFETs) remains a significant challenge, particularly when employing a homogeneous channel. The electrode contact properties for bilayer (BL) MoS2 and the corresponding device performances are investigated using ab initio calculations. Stable van der Waals-type heterojunctions are formed between BL MoS2 and monolayer (ML) Sc2NX2 (X = F or O) in primitive cells, resulting in Ohmic contacts at both vertical and lateral interfaces. Consequently, excellent and symmetric n- and p-type device performances are obtained for the 10-nm-gate BL MoS2 SBFETs with ML Sc2NF2 and Sc2NO2 electrodes operating at 0.5 V for high-performance applications. These results surpass the International Roadmap for Devices and Systems targets specified as ‘5am eq’ note for 12 nm gate devices operating at 0.6 V. The outstanding and symmetric performance indicates that BL MoS2 SBFETs with ML Sc2NF2 and Sc2NO2 electrodes are promising post-Si candidates for high-performance logic circuits.

en: Monolayer Sc2NF2 and Sc2NO2 electrodes for bilayer MoS2: Achieving symmetric and excellent performances

ab-initio-transport-calculation

display_name: ab-initio-transport-calculation

symmetric-performance

display_name: symmetric-performance


en: The conversion of semimetallic suspended graphene (Gr) to a large-gap semiconducting phase is realized by controlled adsorption of atomic hydrogen (deuterium) on free-standing Gr veils in nanoporous graphene. The effects of local rehybridization from sp² to sp³ chemical bonding are investigated by combining X-ray photoelectron spectroscopy and high-resolution electron energy-loss spectroscopy (HREELS) with ab-initio based modelling. The hydrogen adatoms on the C sites induce a stretching frequency, clearly identified in vibrational spectra thanks to the use of the D isotope, which is compatible with the predicted fingerprints of adsorption on both sides of Gr corresponding to the graphane configuration. HREELS of the deuterated samples shows a wide opening of the optical band gap, consistent with the modified spectral density observed in the valence band photoemission. The results are in agreement with ab-initio calculations by GW and Bethe-Salpeter equation approaches, showing a large quasiparticle gap opening and huge excitonic binding energy

en: Dielectric response and excitations of hydrogenated free-standing graphene

envi

en: Environment

big-map

display_name: big-map

bse

display_name: bse

hreels

display_name: hreels

hydrogenated-graphene

display_name: hydrogenated-graphene

max

display_name: max

nanoporous-graphene

display_name: nanoporous-graphene


en: Kohn anomalies are kinks or dips in phonon dispersions which are pronounced in low-dimensional materials. We investigate the effects of non-adiabatic phonon self-energy on Kohn anomalies in one-dimensional metals by developing a model that analyzes how the adiabatic phonon frequency, electron effective mass, and electron-phonon coupling strength influence phonon mode renormalization. We introduce an electron-phonon coupling strength threshold for low-temperature system instability, providing experimentalists with a tool to predict them. Finally, we validate the predictions of our model against first-principles calculations on a 4 Å-diameter carbon nanotube. The dataset includes dft input files used to produce the ab-initio data and scripts to reproduce the plots present in the paper

en: Importance of non-adiabatic effects on Kohn anomalies in 1D metals

electron-phonon-coupling

display_name: electron-phonon-coupling

electronic-structure

display_name: electronic-structure

kohn-anomalies

display_name: kohn-anomalies

phonon-self-energy

display_name: phonon-self-energy

phonons

display_name: phonons


en: In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the electronic-structure calculations they are trained on. Here we demonstrate that these generally-applicable models can also be built to predict explicitly the electronic structure of materials and molecules. We focus on the electronic density of states (DOS), and develop PET-MAD-DOS, a rotationally unconstrained transformer model built on the Point Edge Transformer (PET) architecture, and trained on the Massive Atomistic Diversity (MAD) dataset. We demonstrate our model's predictive abilities on samples from diverse external datasets, showing also that the DOS can be further manipulated to obtain accurate band gap predictions. A fast evaluation of the DOS is especially useful in combination with molecular simulations probing matter in finite-temperature thermodynamic conditions. To assess the accuracy of PET-MAD-DOS in this context, we evaluate the ensemble-averaged DOS and the electronic heat capacity of three technologically relevant systems: lithium thiophosphate (LPS), gallium arsenide (GaAs), and a high entropy alloy (HEA). By comparing with bespoke models, trained exclusively on system-specific datasets, we show that our universal model achieves semi-quantitative agreement for all these tasks. Furthermore, we demonstrate that fine-tuning can be performed using a small fraction of the bespoke data, yielding models that are comparable to, and sometimes better than, fully-trained bespoke models.

en: A universal machine learning model for the electronic density of states

bandgap

display_name: bandgap

density-of-states

display_name: density-of-states

electronic-bands

display_name: electronic-bands

machine-learning

display_name: machine-learning

universal

display_name: universal


en: The choice of structural resolution is a fundamental aspect of protein modelling, determining the balance between descriptive power and interpretability. Although atomistic simulations provide maximal detail, much of this information is redundant to understand the relevant large-scale motions and conformational states. Here, we introduce an unsupervised, information-theoretic framework that determines the minimal number of atoms required to retain a maximally informative description of the configurational space sampled by a protein. This framework quantifies the informativeness of coarse-grained representations obtained by systematically decimating atomic degrees of freedom and evaluating the resulting clustering of sampled conformations. Application to molecular dynamics trajectories of dynamically  diverse proteins shows that the optimal number of retained atoms scales linearly with system size, averaging about four heavy atoms per residue--remarkably consistent with the resolution of well-established coarse-grained models, such as MARTINI and SIRAH. Furthermore, the analysis shows that the optimal retained atoms number depends not only on molecular size but also on the extent of conformational exploration, decreasing for systems dominated by collective motions. The proposed method establishes a general criterion to identify the minimal structural detail that preserves the essential configurational information, thereby offering a new viewpoint on the structure-dynamics-function relationship in proteins and guiding the construction of parsimonious yet informative multiscale models.

en: Determining the optimal structural resolution of proteins through an information-theoretic analysis of their conformational ensemble

coarse-graining

display_name: coarse-graining

model-resolution

display_name: model-resolution

molecular-dynamics

display_name: molecular-dynamics


en: Graphene nanoribbons (GNRs) with zigzag edges are promising materials for spintronic devices due to their tunable bandgaps and spin-polarized edge states. Porphyrins offer complementary benefits, including desirable optoelectronic properties and the incorporated metal centers as qubits for quantum computing. In a recent manuscript, we combine these features in a hybrid system via the on-surface synthesis of zigzag-edge GNRs embedded with porphyrins laterally fused along the ribbon backbone. Using scanning probe microscopy and spectroscopy, we show that this innovative design achieves strong electronic coupling between porphyrin and GNR, with the porphyrin metal center inducing significant alterations in the electronic band structure. For transition metal porphyrins, significant exchange coupling between distant metal centers is mediated by the GNR's π-electron system, along with enhanced spin polarization of the ZGNR backbone edge state. Such hybrid d- and π-electron ribbon system introduces spin-orbit coupling and magnetic anisotropy to carbon nanomaterials, and holds great promise for the fabrication of one-dimensional spin qubit arrays. This record contains data supporting the results discussed in the manuscript.

en: Zigzag graphene nanoribbons with periodic porphyrin edge extensions

cscs

display_name: cscs

dftu

display_name: dftu

feii-porphyrin

display_name: feii-porphyrin

graphene-nanoribbons

display_name: graphene-nanoribbons

on-surface-synthesis

display_name: on-surface-synthesis

snsf

display_name: snsf


en:
Emergent quasiparticles in solids often exhibit unique topological properties as a result of the complex interplay between charge, orbital, spin and lattice degrees of freedom. Among these quasiparticles, the polaron occupies a special place as the first known manifestation of the interaction between a fermion and a boson field. While polarons have been investigated for almost a century, whether these quasiparticles exhibit topological properties and why remain open questions. Here, we establish the universal symmetry principles governing the topology of polar textures in large polarons. Using a group-theoretic analysis, we identify four distinct classes of polar textures in time-reversal-invariant systems, and we show that they carry integer topological charges. We validate this classification by performing state-of-the-art first-principles calculations of materials representative of each class. For these materials, we compute the fingerprints of polaron topology in Huang diffuse scattering, and propose ultrafast electron and X-ray scattering experiments to detect these quasiparticles.

This archive contains the data necessary to reproduce the results of the "Symmetry-protected topological polarons" article by Kaifa Luo, Jon Lafuente-Bartolome, and Feliciano Giustino.

materialscloud

en: Materials Cloud

en: Symmetry-protected topological polarons

diffuse-scattering

display_name: diffuse-scattering

polarons

display_name: polarons

topology

display_name: topology


en: This study introduces new models that incorporate layer corrugation and interface roughness into standard approaches for measuring interface stress in nanomultilayers (NMLs). Applied to Cu/W NMLs, these models show that ignoring such features can inflate measured interface stress by up to 0.4 J/m^2. However, corrugation and roughness alone cannot account for the extreme stresses reported, suggesting that atomic-scale phenomena (e.g., intermixing and metastable phase formation at the interfaces) dominate. These findings highlight the importance of balancing bilayer counts and thickness-to-roughness ratios for reliable stress quantification, providing a practical pathway to designing and characterizing advanced nanocomposite coatings with improved accuracy.

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: Refining interface stress measurement in nanomultilayers through layer corrugation and interface roughness corrections

corrugations

display_name: corrugations

interface-stress

display_name: interface-stress

model

display_name: model

nanomultilayers

display_name: nanomultilayers

roughness

display_name: roughness


en:
This dataset contains the model training scripts, training sets, evaluation results, and analysis notebooks associated with the work "Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials" by Chong et al. Abstract of the work is shared below:

"In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit when the model is directly built on neighbor density correlation descriptors, and implicit when the model captures the correlations through non-linear functions of low body-order terms. In both cases, the "effective body-orderedness" of MLIPs remains largely unexplained: how do the models decompose the total energy into body-ordered contributions, and how does their body-orderedness affect the accuracy and learning behavior? In answering these questions, we first discuss the complexities in imposing the many-body expansion on ab initio calculations at the atomic limit. Next, we train a curated set of MLIPs on datasets of hydrogen clusters and reveal the inherent tendency of the ML models to deduce their own, effective body-order trends, which are dependent on the model type and dataset makeup. Finally, we present different trends in the convergence of the body-orders and generalizability of the models, providing useful insights for the development of future MLIPs."

en: Resolving the body-order paradox of machine learning interatomic potentials

atomic-cluster-expansion

display_name: atomic-cluster-expansion

body-orderedness

display_name: body-orderedness

machine-learning-interatomic-potentials

display_name: machine-learning-interatomic-potentials

many-body-expansion

display_name: many-body-expansion


en: A key technical challenge inhibiting fossil-free energy generation, which is especially pronounced  in solar plants, is the inherent intermittency. Since thermochemical energy storage represents a  solution that has yet to see large-scale utilization, the goal of the study at hand has been to find  suitable materials using a hybrid high-throughput approach, encompassing both data mining and  first principles calculations. Through these efforts, thermal properties have been estimated for more  than 50 000 mono-, bi-, and trimetallic oxides, as well as alloys, retrieved from the Materials Project  database.Thereafter, close to 18 000 convex hulls were created, each representing a unique metal ratio, in order  to determine the most stable phases at different oxygen chemical potentials. In turn, this allowed  almost 300 000 potential transitions to be identified. After filtering out potentially toxic and rare  compositions, a little over 51 000 remained, which were further reduced, to about 3000, by requiring  that the phase transformation must occur at a temperature between 400 °C and 1300 °C.A thorough analysis of the results led to the discovery of several promising energy storage materials.  In addition, Chromium; manganese; calcium; and magnesium were found to be associated with high  reaction enthalpies. When the sensible heat was taken into account, however, light elements such as  lithium; boron; iron; and sodium dominated among the top-ranking candidates. This study therefore  demonstrates how high-throughput data mining can be efficiently enhanced through first-principles  calculations, enabled by machine-learning interatomic potentials, to facilitate material discovery.

en: Discovery of thermochemical energy storage materials via a hybrid data-mining approach

envi

en: Environment

data-mining

display_name: data-mining

first-principles

display_name: first-principles

high-throughput

display_name: high-throughput

machine-learning-potentials

display_name: machine-learning-potentials

metal-oxides

display_name: metal-oxides

thermochemical-energy-storage

display_name: thermochemical-energy-storage

thermodynamics

display_name: thermodynamics


en:
Accurate determination of transition states is central to an understanding of reaction kinetics. Double-endpoint methods where both initial and final states are specified, such as the climbing image nudged elastic band (CI-NEB), identify the minimum energy path between the two and thereby the saddle point on the energy surface that is relevant for the given transition, thus providing an estimate of the transition state within the harmonic approximation of transition state theory. Such calculations can, however, incur high computational costs and may suffer stagnation on exceptionally flat or rough energy surfaces. Conversely, methods that only require specification of an initial set of atomic coordinates, such as the minimum mode following (MMF) method, offer efficiency but can converge on saddle points that are not relevant for transition of interest. Here, we present an adaptive hybrid algorithm that dynamically switches between the CI-NEB with the MMF method ("off-path" CI-NEB) so as to get faster convergence to the relevant saddle point. The method is benchmarked for the Baker-Chan (BC) saddle point test set using the PET-MAD machine-learned potential as well as 59 transitions of a heptamer island on Pt(111) from the OptBench benchmark set. A Bayesian analysis of the performance shows a median change in energy and force calculations of -46% [95% CrI: -55%, -37%] relative to CI-NEB for the BC set, while a 28% reduction is found for the transitions of the heptamer island. A fixed cutoff static switch mechanism for the BC leads to a median increase of 31% calculations compared to the OCI-NEB algorithm.  These results establish the OCI-NEB method as a highly effective tool for high-throughput automated chemical discovery of atomic rearrangements.

This record provides the reproduction data, transition state search logs, and statistical models for the Off-path Climbing Image Nudged Elastic Band (OCI-NEB) algorithm. It includes the complete outputs to accompany the Github repository of scripts which support the manuscript.

en: Enhanced Climbing Image Nudged Elastic Band method with Hessian Eigenmode Alignment

machine-learning

display_name: machine-learning

nudged-elastic-band

display_name: nudged-elastic-band

saddle-search

display_name: saddle-search

transition-state

display_name: transition-state


en: One essential ingredient in many machine learning (ML) based methods for atomistic modeling of materials and molecules is the use of locality. While allowing better system-size scaling, this systematically neglects long-range (LR) effects, such as electrostatics or dispersion interaction. We present an extension of the long distance equivariant (LODE) framework that can handle diverse LR interactions in a consistent way, and seamlessly integrates with preexisting methods by building new sets of atom centered features. We provide a direct physical interpretation of these using the multipole expansion, which allows for simpler and more efficient implementations. The framework is applied to simple toy systems as proof of concept, and a heterogeneous set of molecular dimers to push the method to its limits. By generalizing LODE to arbitrary asymptotic behaviors, we provide a coherent approach to treat arbitrary two- and many-body non-bonded interactions in the data-driven modeling of matter.

en: Physics-inspired equivariant descriptors of non-bonded interactions

dispersion

display_name: dispersion

electrostatics

display_name: electrostatics

erc

display_name: erc

long-range-interactions

display_name: long-range-interactions


en: This file contains all the data, as well as the code necessary to reproduce the results of Jae-Mo Lihm and Samuel Ponce, "Beyond-quasiparticle transport with vertex correction: self-consistent ladder formalism for electron-phonon interactions." We present a self-consistent many-body framework for computing phonon-limited electronic transport from first principles, incorporating both beyond-quasiparticle effects and vertex corrections. Using the recently developed first-principles scGD0 method, we calculate spectral functions with nonperturbative effects such as broadening, satellites, and energy-dependent renormalization. We show that the scGD0 spectral functions outperform one-shot G0D0 and cumulant approximations in model Hamiltonians and real materials, eliminating unphysical spectral kinks and correctly predicting the phonon emission continuum. Building on this, we introduce the self-consistent ladder formalism for transport, which captures vertex corrections due to electron-phonon interactions. This approach unifies and improves upon the two state-of-the-art approaches for first-principles phonon-limited transport: the bubble approximation and the Boltzmann transport equation. Moreover, as a charge-conserving approximation, it enables consistent calculations of the optical conductivity and dielectric function. We validate the developed method against numerically exact results for model Hamiltonians and apply it to real materials. Our results show quantitative agreement with the experimental dc conductivities in intrinsic semiconductors Si and ZnO and the SrVO3 metal, as well as excellent agreement with the experimental THz optical and dielectric properties of Si and ZnO. This work unifies first-principles and many-body approaches for studying transport, opening new directions for applying many-body theory to materials with strong electron-phonon interactions.

electron-phonon-coupling

display_name: electron-phonon-coupling

transport

display_name: transport


en: Enhancing cycling reversibility in high-energy-density lithium metal batteries (LMBs) necessitate precise management of electrolyte-derived electrochemical reactions at electrodes and interphases, yet the recently developed and most commonly used localized high-concentration electrolytes (LHCEs) technology suffers from limited tunability on these reactions due to the non-solvation-participating nature of oxygen-proximal fluorinated diluents. Here we address this issue by designing and synthesizing a new oxygen-distal fluorinated diluent. Unlike traditional oxygen-proximal fluorinated analogs, the molecule skeleton was strategically edited to position fluorine atoms distal to oxygen centers that attenuates electron-withdrawing effects at these Li+-coordination sites, enabling: enhanced diluent/anion participation and reduced volatile solvents in solvation shells. This work establishes oxygen-distal fluorination as a new electrolyte molecular skeleton editing principle for stable energy-dense LMBs.

en: Skeletal editing of electrolyte molecule by oxygen-distal fluorination for 500 Wh kg–1, cycling stable Li metal battery

lithium-metal-battery

display_name: lithium-metal-battery

molecule-design

display_name: molecule-design

theoretical-calculation

display_name: theoretical-calculation


en: Leveraging our recent development, which incorporates hole and particle information into the multi-channel molecular orbital image (MolOrbImage), to generate exceptional accuracy (mean absolute error, MAE < 0.1 eV) in predicting excited-state energies of practical photofunctional materials containing several hundred atoms, we have advanced the implementation of a new approach to overcome the high computational cost of mean-field ground-state calculations that limits its application in high-throughput materials discovery. In this work, low-cost approaches for generating approximate orbitals, including the superposition of atomic densities technique and the semi-empirical tight-binding method, have been employed to construct cost-effective multi-channel MolOrbImages. By connecting with a convolutional neural network, the performance of our model is evaluated for both small organic molecules (MAE < 0.1 eV) and practical photofunctional materials (MAE < 0.14 eV). Perturbation analysis of MolOrbImages highlights the importance of frontier orbital energies, which further motivates the adoption of transfer learning techniques to reduce prediction errors in excited-state energies.

materialscloud

en: Materials Cloud

en: Cost-effective multi-channel MolOrbImage for machine-learned excited-state properties of practical photofunctional materials

excited-state

display_name: excited-state

photofunctional-materials

display_name: photofunctional-materials


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

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

epfl

display_name: epfl

guess-hamiltonians

display_name: guess-hamiltonians

molecular-representation

display_name: molecular-representation

snsf

display_name: snsf

spahm

display_name: spahm


en: Structure determination of molecular solids through NMR crystallography relies on the generation of a comprehensive set of candidate crystal structures and on the comparison of chemical shifts computed for those candidates with experimental values. Exploring the polymorph landscape of molecular solids requires extensive computational power, which leads to a significant bottleneck in the generation of the set of candidate crystals by crystal structure prediction (CSP) protocols. Here, we use a database of crystal structures with associated chemical shifts to construct three-dimensional interaction maps in molecular crystals directly derived from a molecular structure and its associated set of experimentally measured chemical shifts. We show how the maps obtained can be used to identify structural constraints for accelerating CSP protocols and to evaluate the likelihood of candidate crystal structures without requiring DFT-level chemical shift computations.

en: Chemical shift-dependent interaction maps in molecular solids

chemical-shift

display_name: chemical-shift

nmr

display_name: nmr

statistics

display_name: statistics


en: This document contains all raw data for microstructure and electromagnetic parameters from the unpublished thesis titled: Influence of Ho3+ Substitution on the Microstructure and Electromagnetic Properties of NiZnCo Ferrites. Its primary purpose is to provide empirical evidence demonstrating the reliability of the thesis's data.

en: Source files for microstructure and electromagnetic property data used in the paper "Influence of Ho³⁺ Substitution on the Microstructure and Electromagnetic Properties of NiZnCo Ferrites"

electromagnetic-performance

display_name: electromagnetic-performance

niznco-ferrites

display_name: niznco-ferrites

solid-phase-reaction-method

display_name: solid-phase-reaction-method


en: We propose an affinitive additive strategy featuring high donor number and dipole moment, exemplified by N,N-dimethylurea (DMU), to dynamically modulate Zn2+ solvation and the structure of the electric double layer under operational electric fields.  And this repository contains all computational data underlying the study, including: DFT-optimized molecular structures; initial and equilibrated configurations from MD simulations of bulk electrolyte and Zn/electrolyte interfaces; RDF and MSD analysis files; high-throughput xTB results (AIE, AEA, ion-binding energies); and machine learning scripts for descriptor screening. Data are organized by module with detailed READMEs to ensure full reproducibility.

en: Electric-field-reinforced affinitive electrolytes for highly reversible aqueous zinc metal batteries

envi

en: Environment

batteries

display_name: batteries

descriptor

display_name: descriptor

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


en: Long-range interactions and electric response are essential for accurate modeling of condensed-phase systems, but capturing them efficiently remains a challenge for atomistic machine learning. Traditionally, these two phenomena can be represented by static charges, that participate in Coulomb interactions between atoms, and dynamic charges such as atomic polar tensors - aka Born effective charges - describing the response to an external electric field. We critically compare different approaches to learn both types of charges, taking bulk water and water clusters as paradigmatic examples: (1) Learning them independently; (2) Coupling static and dynamic charges based on their physical relationship with a single global coupling constant to account for dielectric screening; (3) Coupled learning with a local, environment-dependent screening factor. In the coupled case, correcting for dielectric screening is essential, yet the common assumption of homogeneous, isotropic screening breaks down in heterogeneous systems such as water clusters. A learned, environment-dependent screening restores high accuracy for the dynamical charges. However, the accuracy gain over independent dynamic predictions is negligible, while the computational cost increases compared to using separate models for static and dynamical charges. This suggests that, despite the formal connection between the two charge types, modeling them independently is the more practical choice for both condensed-phase and isolated cluster systems.

en: Simultaneous learning of static and dynamic charges

atomistic-simulations

display_name: atomistic-simulations

born-effective-charges

display_name: born-effective-charges

density-functional-theory

display_name: density-functional-theory

interatomic-potentials

display_name: interatomic-potentials

long-range-models

display_name: long-range-models

machine-learning

display_name: machine-learning


en: Chiral graphene nanoribbons offer a versatile playground to control electronic and magnetic prop-erties in a onedimensional graphene nanostructure. In a recent publication, we report on the on-surface synthesis of (4,1,4) chiral graphene nanoribbons (chGNRs) and characterize their structure, edge states and band gaps using scanning probe techniques and DFT simulations, the latter revealing a critical dependence on the environment of the electronic and magnetic properties of (4,1,4)-chGNRs. While gas phase calculations predict an open-shell ground state, the influence of the metallic substrate upon adsorption modifies the electronic properties as predicted by GW calculations. In this theoretical frame, the experimental band gap of surface-supported (4,1,4)-chGNRs can only be reproduced assuming a closed-shell configuration.This record contains data to support the results discussed in our manuscript.

en: Electronic properties of on-surface synthesized (4,1,4) chiral graphene nanoribbons

graphene-nanoribbons

display_name: graphene-nanoribbons

gw-approximation

display_name: gw-approximation

marvelasm

display_name: marvelasm

on-surface-synthesis

display_name: on-surface-synthesis

stm

display_name: stm


en:
Density-functional theory (DFT) is a cornerstone of computational materials science. The Density-Functional Toolkit (DFTK) is a recent plane-wave DFT code, written with a flexible architecture and in a high-level language to facilitate development of new methods and support fundamental mathematical research on DFT. DFTK has been instrumental in a good dozen of recent papers, and its core DFT routines are considered stable. So far a systematic precision benchmark against other codes and approaches had not yet been performed. In this work, we apply the verification protocol established by E. Bosoni et al. [Nat. Rev. Phys. 6, 45-58 (2024)] based on the AiiDA high-throughput engine, to validate the precision of DFTK against the existing all-electron and pseudopotential data. This record contains our results, as well as the scripts needed to reproduce them. Out of the 720 systems, DFTK achieves excellent agreement with the all-electron average (ε<0.06) on 181 systems and good-but-not-excellent agreement (0.06<=ε<0.2) on 329 systems. Comparing DFTK with Quantum ESPRESSO (QE@PW|PseudoDojo-v0.4), the agreement is excellent on 624 systems and good-but-not-excellent on 9 systems; the agreement is bad (ε>=0.2) for most systems in the lower right corner of the periodic table (elements: Te, I, Xe, Tl, Pb, Bi, Po, Rn) as well as Ba systems.

We acknowledge and thank Yihan Wu for the initial versions of aiida-dftk and the DFTK support in aiida-common-workflows.

en: Precision verification results data for DFTK@PW|PseudoDojo-v0.5

automation

display_name: automation

dft

display_name: dft

equation-of-state

display_name: equation-of-state

pseudopotentials

display_name: pseudopotentials

verification

display_name: verification


en: Based on ab initio molecular dynamics simulations, we explain how graphene-based ion transistors work, using channels filled with K⁺ ions. Our results show that electrons in graphene allow confined ions to influence each other over long distances. This explains why the transistor responds sensitively to changes in ion density, which are controlled by the gate voltage. The ON/OFF switching behavior comes from the competition between π–π stacking and cation–π interactions, which changes with ion density. The increase in ion transport efficiency—also called signal amplification—is caused by the ions moving together in a coordinated way that depends on their density. Additionally, when ions outside the channel resonate with those inside, rapid dehydration occurs. This leads to very fast ion movement through the transistor. These atomic-scale insights can help guide the design of future ultra-low-energy neuromorphic chips.

materialscloud

en: Materials Cloud

en: Quantum correlation of channel-confined ions in graphene-based transistors for energy-efficient neuromorphic chips

--stacking

display_name: --stacking

collective-ion-oscillation

display_name: collective-ion-oscillation

ion-transistor

display_name: ion-transistor

ionics

display_name: ionics


en: We calculated the interaction energies of hydrogen-benzene and hydrogen-hydrogen complexes using dispersion-inclusive exchange-correlation functionals in density functional theory and compared them with results from the coupled cluster method with single, double, and perturbative triple excitations and those from the adiabatic-connection fluctuation-dissipation theorem within the random phase approximation. For both complexes, the recent generations of nonlocal van der Waals density functionals show overall improvement over semilocal exchange-correlation functionals with dispersion corrections, yielding good agreement with the coupled cluster method. We discuss their accuracy and recommend suitable exchange-correlation functionals based on different evaluation metrics.

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: Assessing the accuracy of exchange-correlation functionals in density functional theory for hydrogen-benzene and hydrogen-hydrogen interactions

beef

display_name: beef

beef-vdw

display_name: beef-vdw

benzene

display_name: benzene

ccsdt

display_name: ccsdt

cof

display_name: cof

hydrogen

display_name: hydrogen

hydrogen-storage

display_name: hydrogen-storage

lda

display_name: lda

mof

display_name: mof

molgw

display_name: molgw

orca

display_name: orca

pbe

display_name: pbe

pbesol

display_name: pbesol

quantum-espresso

display_name: quantum-espresso

rpa

display_name: rpa

rpbe

display_name: rpbe

scan

display_name: scan

vasp

display_name: vasp

vdw-df

display_name: vdw-df

vdw-df-cx

display_name: vdw-df-cx


en:
Dataset: Interoperable DFT OCV Workflows (Final Alignment)

We present a dataset enabling cross‑engine alignment and reproducibility of open‑circuit voltage (OCV) calculations for battery cathode materials within standardized density functional theory (DFT) workflows. The dataset implements a common, JSON‑based input/output schema and a semantic JSON‑LD description to express the workflow’s scientific intent, inputs, methods, and outputs in a code‑agnostic manner, supporting FAIR reuse. Using shared OPTIMADE‑style input structures, we execute an identical OCV protocol across multiple workflow managers and DFT engines (VASP via PerQueue and SimStack, GPAW, Quantum ESPRESSO, and CASTEP). Engine‑specific archives provide standardized output JSON files for cross‑comparison, and, where available, native files to enable full reproduction.

We focus on five representative cathode chemistries (Li2Mn3NiO8, LiCoO2, LiTiS2, LiFePO4, and MgMo3S4), chosen to probe diverse structural and electronic regimes. The dataset documents strong cross‑engine agreement for average OCVs derived from pristine charged/discharged cells, and highlights where alignment is challenging, notably for vacancy‑containing supercells at low/high states of charge due to sensitivity to smearing, symmetry breaking, and optimization procedures. By releasing harmonized inputs, standardized outputs, and semantic metadata, this work provides a practical resource for benchmarking interoperable DFT workflows, facilitates transparent comparison across codes, and supports reproducible studies and future extensions (e.g., spin polarization and Hubbard corrections) in high‑throughput materials discovery.

en: OCV intercalation workflow implemented across DFT and workflow engines

battery

display_name: battery

big-map

display_name: big-map

dft

display_name: dft

ocv

display_name: ocv

workflows

display_name: workflows


en: This record contains the two-dimensional substructures identified using a novel  approach based on the crystal graph construction via Voronoi partition, interaction strength estimation via bond valence theory and the iterative removal of weak links, strating from experimentally known bulk compounds. We also report the results of DFT calculations on a subset of newly identified easily exfoliable and robust two-dimensional materials.

en: Search for low-periodic substructures in crystalline solids: a novel approach

electronic-bands

display_name: electronic-bands

high-throughput

display_name: high-throughput


en: Photothermal (photonic) sintering crystallizes as-deposited amorphous LiCoO2 (LCO) cathodes for solid-state thin-film batteries using millisecond, surface-localized heating. However, process design often relies on 1D models with phase-averaged, temperature-independent properties, which can mispredict peak temperatures and thermal damage margins. Here we develop a multiscale, data-driven framework that provides phase- and grain-size–resolved thermophysical inputs for stoichiometric LCO. We train an Allegro neural network potential with near-ab initio accuracy, enabling Green–Kubo calculations of thermal conductivity for crystalline and amorphous phases. The low, weakly density-dependent conductivity of amorphous LCO motivates its use as an effective intergranular phase in a thin-interface model that reproduces observed grain-size–dependent thermal transport. Combined with measured wavelength-resolved optical properties in 1D multiphysics simulations, we show amorphous LCO absorbs more strongly and reaches higher peak temperatures than crystalline LCO; thus crystalline, constant-property models systematically overestimate safe operating windows.

en: Critical role of phase-dependent properties in modeling photothermal sintering of LiCoO2 cathodes

envi

en: Environment

heat-transfer

display_name: heat-transfer

molecular-dynamics

display_name: molecular-dynamics

neural-network-potential

display_name: neural-network-potential

photonic-sintering

display_name: photonic-sintering

thermal-conductivity

display_name: thermal-conductivity


en: Mechanical properties of ultrathin coatings can deviate from bulk values due to growth-stage and interface effects. Elastic moduli of bulk amorphous alumina were calculated using a near-ab initio-accurate neural-network interatomic potential to provide a modeling benchmark for hydroxylated alumina coatings deposited by atomic layer deposition at 50 °C. Experimentally, elastic moduli were evaluated by both spherical nanoindentation and laser-induced surface acoustic wave spectroscopy (LiSAWS). For coatings thicker than 7 nm, both techniques indicated a weakly decreasing modulus from ~137 GPa to ~126 GPa (nanoindentation) and ~116 GPa to ~108 GPa (LiSAWS). These values are consistent with the simulated Young’s modulus.

en: Modulus and yield strength determination at ultra-thin atomic layer deposited hydroxylated alumina coatings using spherical nanoindentation and surface acoustic waves

atomic-layer-deposition-ald

display_name: atomic-layer-deposition-ald

foundation-model

display_name: foundation-model

hydroxylated-alumina

display_name: hydroxylated-alumina

spherical-nanoindentation

display_name: spherical-nanoindentation

ultrathin-coatings

display_name: ultrathin-coatings

yield-strength

display_name: yield-strength

youngs-modulus

display_name: youngs-modulus


en: Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction that has been shown to perform especially well for small materials datasets, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.

en: Optical materials discovery and design with federated databases and machine learning

electronic-structure

display_name: electronic-structure

modnet

display_name: modnet

optical-materials

display_name: optical-materials

optimade

display_name: optimade

refractive-index

display_name: refractive-index


en: The strongly constrained and appropriately normed (SCAN) meta-GGA functional is a milestone achievement of electronic structure theory. Recently, a revised and restored form (r²SCAN) has been suggested as a replacement for SCAN in high-throughput applications. Here, we assess the accuracy and reliability of the r²SCAN meta-GGA functional for the group IV elemental solids carbon (C), silicon (Si), germanium (Ge), and tin (Sn). We show that the r²SCAN functional agrees closely with its parent functional SCAN for elastic constants, bulk moduli, and phonon dispersions, but the numerical stability of r²SCAN is superior. Both meta-GGA functionals outperform standard GGA (Perdew-Burke-Ernzerhof) in terms of accuracy and approach the level of common hybrid functionals (Heyd-Scuseria-Ernzerhof). However, we find that r²SCAN performs much worse than SCAN for the α ↔ β phase transition of both Ge and Sn, yielding larger phase energy differences and transition pressures.Here we make available the raw phonon dispersion data and VASP input files for an example phonon calculation.

en: Lattice dynamics and structural phase stability of group IV elemental solids with the r²SCAN functional

dft

display_name: dft

phonons

display_name: phonons

vasp

display_name: vasp


en: Physics-inspired machine learning (ML) models can be categorized into two classes: those relying solely on three-dimensional structure and those incorporating electronic information. In this work, we benchmark both classes for predicting quantum-chemical properties of transition metal complexes with diverse charge and spin states, using three complementary datasets. The evaluated methods include molecular representations (SLATM, FCHL, SOAP, and SPAHM family) combined with kernel ridge regression, as well as geometric deep learning models (MACE and 3DMol). We examine how the inclusion of electronic information affects predictive accuracy across datasets and target properties. Models that incorporate electronic information consistently outperform purely structure-based models for properties whose distributions are strongly governed by electronic characters, such as spin-splitting energies and frontier orbital energies. In contrast, structure-only models perform well for predicting the HOMO–LUMO gap and dipole moment magnitude, whose distributions are relatively insensitive to electronic characteristics. Geometric deep learning models with charge and spin embeddings (MACE-QS and 3DMol-QS) show the highest overall accuracy, with 3DMol offering the best computational efficiency among the tested models. These results clarify when geometric information is sufficient and when incorporating electronic information becomes essential, providing practical guidance for selecting effective physics-based ML models for transition metal complexes.

materialscloud

en: Materials Cloud

en: Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states

benchmark

display_name: benchmark

geometric-deep-learning

display_name: geometric-deep-learning

machine-learning

display_name: machine-learning

representations

display_name: representations

transition-metal-complex

display_name: transition-metal-complex


en: This dataset presents the results of a comprehensive crystal structure search for ternary compounds in the Lu–N–H system. Thousands of candidate structures were evaluated, and their thermodynamic stability was assessed through the construction of the convex hull of formation enthalpies. To ensure reliable energetic ordering, all calculations were performed using the highly accurate r2SCAN density functional in an all-electron framework, avoiding possible pseudopotential-related inaccuracies.  A previously unreported lutetium hydride phase (HLu₂) is identified on the convex hull, indicating thermodynamic stability. However, subsequent electron–phonon coupling analysis shows that this structure is not a promising candidate for near-ambient superconductivity. Apart from this additional phase, the results are in close agreement with earlier structure prediction studies, demonstrating strong reproducibility across independent computational approaches. This consistency highlights the maturity and reliability of modern crystal structure prediction methods. Overall, the dataset provides further evidence that no thermodynamically stable structure within the Lu–N–H system is likely to exhibit conventional high-temperature superconductivity under near-ambient conditions, as would be required to explain previously reported experimental claims.

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: Ternary phase diagram of nitrogen doped lutetium hydrides

minimahopping

display_name: minimahopping

superconductor

display_name: superconductor


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


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