AndreaBozzo/ceres-open-data-index huggingface dataset:
Cobalt-based perovskite oxides as catalysts for acidic oxygen evolution reaction

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.

Tags: acidic-oxygen-evolution-reaction, cobalt-based-catalysts, cobalt-oxidation-state, experimental, perovskite-oxides, x-ray-adsorption-spectroscopy
Quantifying electronic and geometric effects on the activity of platinum catalysts for water-gas shift

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

Tags: dft, nanoparticle, ptceo2
Strongly correlated physics in organic open-shell quantum systems

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

Tags: dft, dmft, exact-diagonalization, transport
Determination of key functional structures of an amorphous VHL-based SMARCA2 PROTAC

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.

Tags: amorphous, drug-formulation, nmr
Mass-zero constrained molecular dynamics for electrostatic interactions

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.

Tags: cecam, electrostatic-interactions, marvelp3, molecular-dynamics-simulation, molten-salts
Controlled magnetic bistability of a helical non-Kekulé hydrocarbon on a Au(111) surface

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.

Tags: ab-initio-simulations, hydrocarbons, magnetic-nanocarbons, marvelp4, scanning-probe-microscopy, snsf
Two-dimensional diboron trioxide crystal composed by boroxol groups

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.

Tags: 2d-materials, b2o3, boroxol-groups
NaviDiv: a web app for monitoring chemical diversity in generative molecular design

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.

Tags: chemical-diversity, generative-models, singlet-fission
Impact of electronic correlations on the superconductivity of high-pressure CeH9

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

Tags: electronic-correlations, high-pressure, superconductivity
Exploring different search approaches to discover donor molecules for organic solar cells

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.

Tags: bayesian-optimisation, building-block-approach, genetic-algorithm, organic-solar-cells, representation-learning
Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds

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.

Tags: electron-phonon-coupling, first-principles, high-throughput, migdal-eliashberg, superconductivity
Heat transport in superionic materials via machine-learned molecular dynamics

Precise modeling and understanding of heat transport in the superionic phase are of great interest. Although simulations combining Green-Kubo (GK) molecular dynamics with machine-learned potentials (MLPs) stand as a promising approach, substantial challenges remain due to the crucial impact of atomic diffusion. Here, we first show that the thermal conductivity (κ) of superionic materials calculated via conventional GK integral of the energy flux varies notably with the MLP model. Subsequently, we highlight that reliable, model-independent κ values can be obtained by applying Onsager’s reciprocal relations to correctly capture the coupled heat and mass transport. Remarkably, an anomalously invariant κ is observed over a wide temperature range, distinct from the characteristic trends in traditional crystals and glasses. Finally, we illustrate that conventional κ decompositions into kinetic, potential, and cross terms suffer from ambiguities in the physical interpretation, despite their mathematical rigor.

Tags: superionic-materials-heat-transport-green-kubo-formalism-onsager-reciprocal-relations-machine-learne
Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework

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.

Tags: automatic-differentiation, hamiltonian, machine-learning
Magnons in chromium trihalides calculated with the ab initio Bethe-Salpeter equation

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.

Tags: bethe-salpeter-equation, dft, magnons
Magnetoresistance in ZrSiX (X = S, Se, Te) nodal-line semimetals

We present a comprehensive first-principles study of the magnetoresistance in ZrSiX (X = S, Se, Te) topological nodal-line semimetals. Our study demonstrates that all primary features of the experimentally measured magnetoresistance in these materials are captured by our calculations, including the unusual butterfly-shaped anisotropic magnetoresistance. This anisotropic magnetoresistance can be accurately reproduced using the semiclassical Boltzmann transport theory without introducing any information on the topological nature of bands or the concepts of topological phase transition. Considering the complex structure of the Fermi surface in these topological materials, we develop a theoretical description explaining the features observed in magnetoresistance measurements. Additionally, the atypical Hall resistance can be interpreted by the same semiclassical approach. Our findings establish magnetotransport as a powerful tool for analyzing the geometry of the Fermi surface, complementing angle-resolved photoemission spectroscopy and quantum oscillation measurements. This approach is demonstrated to be particularly useful for determining the role of non-trivial topology in transport properties.

Tags: boltzmann-theory, density-functional-calculations, fermi-surface, first-principles-calculations, hall-effect, magnetoresistance, node-line-semimetals, topological-materials
Mobility calculation in disordered WS2-Al2O3 stacks from first principles

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

Tags: 2d-materials, 2d-oxide-interface, mobility