en:
In this paper, we present and benchmark our first-principles calculations of magnon dispersions and wave functions in monolayer Cr trihalides using the finite-momentum Bethe-Salpeter equation (BSE) to describe collective spin-flip excitations. We study the dependence of magnon dispersions on the halide species and resolve the small topological gap at the Dirac point in the magnon spectrum by including spin-orbit coupling. We analyze the obtained magnon wave functions to gain insight into magnon states in real and reciprocal space. We also compare our method with the Heisenberg spin model. This work advances the quantitative modeling of magnons, providing the starting point for studying magnon interactions in a first-principles BSE framework.

This archive includes all the inputs for DFT calculations in QuantumEspresso and GW-BSE with the Yambo code to reproduce the data in our paper.

en: Magnons in chromium trihalides calculated with the ab initio Bethe-Salpeter equation

bethe-salpeter-equation

display_name: bethe-salpeter-equation

magnons

display_name: magnons


$oid: OBSOLETE

en:
Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, directly from compositions and geometries of atomic configurations. With the emergence of ML approaches to predict the "ingredients" of a QM calculation, such as the ground state charge density or the effective single-particle Hamiltonian, it has become possible to obtain multiple properties through analytical physics-based operations on these intermediate ML predictions. We present a framework to seamlessly integrate the prediction of an effective electronic Hamiltonian, for both molecular and condensed-phase systems, with PySCFAD, a differentiable QM workflow. This integration facilitates training models indirectly against functions of the Hamiltonian such as electronic energy levels, dipole moments, polarizability, etc. We then use this framework to explore various possible choices within the design space of hybrid ML/QM models, examining the influence of incorporating multiple targets on model performance and learning a reduced-basis ML Hamiltonian that can
reproduce targets computed from a much larger basis. Our benchmarks evaluate the accuracy and transferability of these hybrid models, compare them against predictions of atomic properties from their surrogate models, and provide indications to guide the design of the interface between the ML and QM components of the model. For our benchmarks we have used a subset of the QM7 and QM9 datasets as well as two extrapolative datasets for long chain polyalkenes/acenes and polyenoic acid series, along with a small graphene dataset.

en: Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework

materialscloud

approval_status: approved

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

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

en: Materials Cloud

de: Materials Cloud

fr: Materials Cloud

it: Materials Cloud

political_level: other

state: active

type: organization

psi

capacity: public

type: dataset

key: guid

key: uri

value: DCAT Harvester Materials Cloud

envi

de: Umwelt

en: Environment

fr: Environnement

it: Ambiente

automatic-differentiation

display_name: automatic-differentiation

hamiltonian

display_name: hamiltonian

machine-learning

display_name: machine-learning


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: 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.

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.

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

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

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).

amorphous

display_name: amorphous

drug-formulation

display_name: drug-formulation

nmr

display_name: nmr


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

materialscloud

en: Materials Cloud

envi

en: Environment

computational-high-throughput-screening

display_name: computational-high-throughput-screening

dft

display_name: dft

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: Cobalt-based oxides have been investigated as potential alternatives to Ir/Ru-based oxides for catalyzing the oxygen evolution reaction (OER) in acidic media. Past research, however, is mainly focused on the spinel oxide structure so far. Exploring alternative crystal structures is essential for expanding the material library and developing highly efficient OER catalysts for acidic environments. As a proof of concept, we demonstrate that Co-based perovskite oxides can drive acidic OER effectively. Appling hard/soft X-ray absorption spectroscopy (hXAS/sXAS) characterizations, we show that the La and Ce doped SrCoO3 (denoted as LSC and CSC, respectively) have a bulk-average Co oxidation state close to 3+ and surface-dominant low-spin CoIII species. Electrochemical analysis reveals that they only show one Co redox pair, similar to CoOOH in acidic environments. The recorded Tafel slopes are around ~65 mV dec-1, comparable to the benchmarking Ir/Ru-based catalysts. The combination of the spectroscopic and electrochemical findings presented here highlights the important role of low-spin CoIII species in catalyzing OER in acidic environments and contributes to the rational design of non-noble metal OER catalysts.

en: Cobalt-based perovskite oxides as catalysts for acidic oxygen evolution reaction

acidic-oxygen-evolution-reaction

display_name: acidic-oxygen-evolution-reaction

cobalt-based-catalysts

display_name: cobalt-based-catalysts

cobalt-oxidation-state

display_name: cobalt-oxidation-state

experimental

display_name: experimental

perovskite-oxides

display_name: perovskite-oxides

x-ray-adsorption-spectroscopy

display_name: x-ray-adsorption-spectroscopy


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.

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

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.

actinide-materials

display_name: actinide-materials

dftu

display_name: dftu

electronic-structure

display_name: electronic-structure

orbital-resolved

display_name: orbital-resolved


en: Understanding electrical resistivity in metals remains a central challenge in quantifying charge transport at finite temperature. Current first-principles calculations based on the Boltzmann transport equation often match experiments, yet they almost always neglect the effect of thermal expansion and phonon anharmonicity. We show that both effects exert an opposite impact on electron–phonon coupling and on electrical resistivity. Thermal expansion enhances the coupling and leads to overestimation of resistivity, whereas anharmonic effects reduce it. By explicitly incorporating both effects, we establish a more complete description of resistivity in elemental metals, demonstrated here for Pb, Nb, and Al. This archive contains all our input, output files and the data.

en: Opposite impact of thermal expansion and phonon anharmonicity on the phonon-limited resistivity of elemental metals from first principles

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

transport

display_name: transport


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

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

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).

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: We present a systematic density-functional theory study of the effects of strain on the structural and electronic properties in vanadium dioxide (VO2), with particular emphasis on its effect on the relative stability of the metallic rutile and the insulating monoclinic M1 phases. We consider various strain conditions that can be related to epitaxial strain present in VO2 films grown on different lattice planes. Our calculations confirm the dominant role of c axis strain, i.e., along the direction of the V--V dimerization in the M1 phase. Our analysis suggests that this effect stems primarily from the weakening of the lattice stiffness, with the hopping amplitude along the c axis playing a minor role. We also confirm that, in strain scenarios that deform the basal plane, the c axis strain still has a dominant effect on the phase stability.

en: Effects of strain on the stability of the metallic rutile and insulating M1 phases of vanadium dioxide

materialscloud

en: Materials Cloud

envi

en: Environment

dftuv

display_name: dftuv

strain

display_name: strain


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

machine-learning

display_name: machine-learning


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).

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.

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

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: This study employed density functional theory in combination with the Materials Project database to systematically screen the unexplored phase space of Ag–In–I double salts. High-throughput screening of the Ag–In–I ternary phase space reveals several stable and metastable compounds belonging to two distinct structural families: tetrahedrally and octahedrally coordinated phases, with characteristic band gaps of approximately 3.0 eV and 2.0 eV, respectively. Although these materials are unlikely to function as efficient photovoltaic absorbers, their tunable band gaps and favorable stability make them promising candidates for charge transport and other optoelectronic applications. This dataset contains the input and output files for all DFT and hybrid functional calculations, the theoretically predicted potential of each material as absorber layer under different lighting conditions, as computed by spectroscopic limited maximum efficiency metric, as well as the structures of the uniquely identified polymorphs. All data are organized such that files are grouped according to the figures in the main text, along with selected files from the supplementary material.

en: Computational screening and discovery of Silver–Indium Halide double salts.

computational-materials-desing

display_name: computational-materials-desing

indoor-photovoltaics

display_name: indoor-photovoltaics

perovskite-inspired-materials

display_name: perovskite-inspired-materials

solar-cells

display_name: solar-cells


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

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).

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

materialscloud

en: Materials Cloud

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: Nanostructured hybrid films composed of tungsten oxide (WOx) nanoclusters and vertically aligned carbon nanotubes (CNTs) were synthesized through a combination of chemical vapor deposition and supersonic cluster beam deposition. The use of a cluster source enabled the direct fabrication of oxygen-deficient, nonstoichiometric WOx nanoclusters, which decorated the CNT sidewalls with a characteristic “beaded necklace-style” morphology. Electrical resistance measurements under ethanol exposure in ultrahigh vacuum revealed a distinct behavior consistent with n-type conduction, unlike the intrinsic p-type behavior of pristine CNTs and of WOx films. This inversion is linked to the appearance of an interfacial charge transfer from the oxygen vacancies in the defective WOx nanoclusters to the CNTs, which injects electrons into the CNT network and shifting its Fermi level, thereby inverting the conduction type. Notably, this n-type conduction response remained stable even after prolonged air exposure. These results propose a viable approach to achieving air-stable n-type doping in CNT-based nanostructures.

en: Stable n-type conduction in WOx-CNT hybrid films

envi

en: Environment

carbon-nanotubes

display_name: carbon-nanotubes

experimental

display_name: experimental

gas-sensing

display_name: gas-sensing

hybrid-nanostructures

display_name: hybrid-nanostructures

n-type-conductor

display_name: n-type-conductor

tungsten-oxide-nanoclusters

display_name: tungsten-oxide-nanoclusters


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.

en: Symmetry-protected topological polarons

diffuse-scattering

display_name: diffuse-scattering

polarons

display_name: polarons

topology

display_name: topology


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

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: 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

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).

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.

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

materialscloud

en: Materials Cloud

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.

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

envi

en: Environment

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

batteries

display_name: batteries

descriptor

display_name: descriptor

molecular-dynamics-simulation

display_name: molecular-dynamics-simulation


en: We demonstrate that non-diffusive, fluid-like heat transport, such as heat backflowing from cooler to warmer regions, can be induced, controlled, and amplified in extreme thermal conductors such as graphite and hexagonal boron nitride. We employ the viscous heat equations, i.e., the thermal counterpart of the Navier-Stokes equations in the laminar regime, to show with first-principles quantitative accuracy that a finite thermal viscosity yields steady-state heat vortices, and governs the magnitude of transient temperature waves. Finally, we devise strategies that exploit devices' boundaries and resonance to amplify and control heat hydrodynamics, paving the way for novel experiments and applications in next-generation electronic and phononic technologies.

en: Viscous heat backflow and temperature resonances in extreme thermal conductors

graphite

display_name: graphite

heat-hydrodynamics

display_name: heat-hydrodynamics

temperature-resonances

display_name: temperature-resonances

thermal-viscosity

display_name: thermal-viscosity


en: Photocatalytic water splitting has attracted considerable attention for renewable energy production. Since the first reported photocatalytic water splitting by titanium dioxide, this material remains one of the most promising photocatalysts, due to its suitable band gap and band-edge positions. However, predicting both of these properties is a challenging task for existing computational methods. Here we show how Koopmans spectral functionals can accurately predict the band structure and level alignment of rutile, anatase, and brookite TiO₂ using a computationally efficient workflow that only requires (a) a DFT calculation of the photocatalyst/vacuum interface and (b) a Koopmans spectral functional calculation of the bulk photocatalyst. The success of this approach for TiO₂ suggests that this strategy could be deployed for assessing the suitability of novel photocatalyst candidates. This record contains for each polymorph: (a) a calculation of a slab; (b) KI and pKIPZ band structure calculation of the bulk system.

en: Predicting the suitability of photocatalysts for water splitting using Koopmans spectral functionals: the case of TiO₂ polymorphs

electronic-structure

display_name: electronic-structure

koopmans-spectral--functionals

display_name: koopmans-spectral--functionals

orbital-density-dependent-functionals

display_name: orbital-density-dependent-functionals

photocatalysis

display_name: photocatalysis

water-splitting

display_name: water-splitting


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

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).

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: 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.

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

materialscloud

en: Materials Cloud

--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.

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

envi

en: Environment

beef

display_name: beef

beef-vdw

display_name: beef-vdw

benzene

display_name: benzene

ccsdt

display_name: ccsdt

cof

display_name: cof

dft

display_name: dft

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

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: 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

molecular-dynamics

display_name: molecular-dynamics

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: We study the electronic structure and dynamical correlations in antiferromagnetic BiFeO3, a prototypical room-temperature multiferroic, using a variety of static and dynamical first-principles methods. Conventional static Hubbard corrections (DFT+U, DFT+U+V) incorrectly predict a deep-valence Fe 3d peak (around -7 eV) in antiferromagnetic BiFeO3, in contradiction with hard-X-ray photoemission. We resolve this failure by using a recent generalization of DFT+U to include a frequency-dependent screening – DFT+U(ω) – or using a dynamical Hubbard functional (dynH). The screened Coulomb interaction U(ω), computed with spin-polarized RPA and projected onto maximally localized Fe 3d Wannier orbitals, is expressed as a sum-over-poles, yielding a self-energy that augments the Kohn–Sham Hamiltonian. This DFT+U(ω) approach predicts a fundamental band gap of 1.53 eV, consistent with experiments, and completely eliminates the unphysical deep-valence peak. The resulting simulated HAXPES spectrum reproduces the experimental lineshape with an accuracy comparable to state-of-the-art approaches. Our work highlights the critical nature of dynamical screening in complex oxides and of DFT+U(ω) as a predictive and computationally efficient approach to address the electronic structure of correlated materials.

en: Electronic structure and dynamical correlations in antiferromagnetic BiFeO3

density-functional-theory

display_name: density-functional-theory

dynamical-correlations

display_name: dynamical-correlations

electronic-structure

display_name: electronic-structure


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

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

materialscloud

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

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

dft

display_name: dft

phonons

display_name: phonons

vasp

display_name: vasp


en: Electron-phonon interactions in a solid are crucial for understanding many interesting material properties, such as transport properties and the temperature dependence of the electronic band gap. For harmonic materials, the linear interaction process where one electron interacts with one phonon is sufficient to quantitatively describe these properties. However, this is no longer true in anharmonic materials with significant electron-phonon interaction, such as halide perovskites. Currently, the only available models for nonlinear electron-phonon interaction are model Hamiltonians, written in terms of phenomenological parameters. Here, we provide a postprocessing Python code for the treatment of the long-range part of the 1-electron-2-phonon interaction. We show in the associated manuscripts that it is sufficient to know the phonon frequencies and eigenvectors calculated at a finite electric field, which we provide alongside this software for three selected materials: LiF, KTaO3, and CsPbI3. We calculate the 1-electron-2-phonon spectral functions for these materials, and use the one for CsPbI3 to calculate the contribution of the 1-electron-2-phonon interaction to the electron mobility. We show that the nonlinear interaction contributes significantly to the temperature scaling of the mobility in CsPbI3, due to its low phonon frequencies. For LiF, a very harmonic material with high phonon frequencies, the effect is negligible as expected. In KTaO3 the effect of nonlinear interaction is also negligible, despite the fact that it is an anharmonic material: this is due to the fact that it also has high phonon frequencies.

en: Importance of nonlinear long-range electron-phonon interaction on the carrier mobility of anharmonic halide perovskites

materialscloud

en: Materials Cloud

anharmonicity

display_name: anharmonicity

electron-phonon-coupling

display_name: electron-phonon-coupling

first-principles

display_name: first-principles

transport

display_name: transport


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.

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

envi

en: Environment

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 archive provides the AiiDA workflows, metadata, and computational datasets associated with the study of vibrational frequencies and Stark tuning rates at electrochemical interfaces within the framework of grand-canonical density functional theory (GC-DFT) combined with continuum electrochemical models.

In this work, the electrochemical potential is treated as a degree of freedom by adjusting the number of electrons in the Kohn-Sham equations, enabling simulations at fixed potential. Based on the relationship between free energy and grand potential, physical properties such as atomic forces, vibrational frequencies, and Stark tuning rates are consistently evaluated. While atomic forces are found to be equivalent to Hellmann-Feynman forces in the canonical ensemble, vibrational frequencies and Stark tuning rates exhibit distinct behavior under grand-canonical conditions.

The archive contains input parameters, workflows, and computed results obtained using finite-displacement methods for the evaluation of vibrational properties and Stark tuning rates. These data enable the reproduction of the results reported in the associated publication and provide a reference for future studies of electrochemical interfaces under constant potential conditions.

en: Vibrational frequencies and stark tuning rate with continuum electro-chemical models and grand canonical density functional theory

grand-canonical-dft

display_name: grand-canonical-dft

stark-tuning-rate

display_name: stark-tuning-rate

vibrational-frequencies

display_name: vibrational-frequencies


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.

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

dft

display_name: dft

dftuv

display_name: dftuv


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

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

deep-learning

display_name: deep-learning

edge-diffusion

display_name: edge-diffusion

generative-adversarial-network

display_name: generative-adversarial-network

kinetic-monte-carlo

display_name: kinetic-monte-carlo

neural-networks

display_name: neural-networks

roughness

display_name: roughness

stochastic-dynamics

display_name: stochastic-dynamics

surface-science

display_name: surface-science


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

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

continuum-models

display_name: continuum-models

convolutional-neural-network

display_name: convolutional-neural-network

elastic-energy

display_name: elastic-energy

finite-element-method

display_name: finite-element-method

germanium

display_name: germanium

machine-learning

display_name: machine-learning

strained-films

display_name: strained-films

surface-evolution

display_name: surface-evolution

thin-films

display_name: thin-films


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

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

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).

active-learning

display_name: active-learning

aiida

display_name: aiida

aiida-trainspot

display_name: aiida-trainspot

carbon-allotropes

display_name: carbon-allotropes

mace

display_name: mace

neural-network-interatomic-potentials

display_name: neural-network-interatomic-potentials

uncertainty-quantification

display_name: uncertainty-quantification