AndreaBozzo/ceres-open-data-index huggingface dataset:
AiiDA-TrainsPot: Towards automated training of neural-network interatomic potentials

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.

Tags: active-learning, aiida, aiida-trainspot, carbon-allotropes, mace, machine-learning, neural-network-interatomic-potentials, uncertainty-quantification
Zigzag graphene nanoribbons with periodic porphyrin edge extensions

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.

Tags: cscs, dftu, feii-porphyrin, graphene-nanoribbons, marvelp4, on-surface-synthesis, pbe0-calculations, snsf
Stable n-type conduction in WOx-CNT hybrid films

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.

Tags: carbon-nanotubes, experimental, gas-sensing, hybrid-nanostructures, n-type-conductor, tungsten-oxide-nanoclusters
Determining the optimal structural resolution of proteins through an information-theoretic analysis of their conformational ensemble

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

Tags: coarse-graining, model-resolution, molecular-dynamics
A universal machine learning model for the electronic density of states

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.

Tags: bandgap, density-of-states, electronic-bands, electronic-structure, machine-learning, universal
Benchmarking physics-inspired machine learning models for transition metal complexes with diverse charge and spin states

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.

Tags: benchmark, geometric-deep-learning, machine-learning, marvelp2, representations, transition-metal-complex
Importance of non-adiabatic effects on Kohn anomalies in 1D metals

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

Tags: electron-phonon-coupling, electronic-structure, kohn-anomalies, phonon-self-energy, phonons
Computational screening and discovery of Silver–Indium Halide double salts.

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.

Tags: computational-materials-desing, high-throughput, indoor-photovoltaics, perovskite-inspired-materials, solar-cells
Monolayer Sc2NF2 and Sc2NO2 electrodes for bilayer MoS2: Achieving symmetric and excellent performances

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.

Tags: ab-initio-transport-calculation, bl-mos2-sbfet, ml-sc2nf2-and-sc2no2-electrode, symmetric-performance
Origin of the machine learning forces field errors across metal elements

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-precision 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.

Tags: dataset, density-functional-theory, machine-learning-force-field
Origin of the machine learning forces field errors across metal elements

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.

Tags: dataset, density-functional-theory, machine-learning-force-field
Benchmarking the plasmon-pole and multipole approximations in the Yambo Code using the GW100 dataset

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).

Tags: aiida, aiida-yambo, first-principles, g0w0, godby-need-plasmon-pole-approximation, gw, gw100, high-throughput, mpa, multipole-approximation, yambo, yambo-code
Modeling the equilibrium vacancy concentration in multi-principal element alloys from first-principles

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.

Tags: embedded-cluster-expansion, equilbrium-vacancy-concentration, machine-learning
Two-dimensional RMSD projections for reaction path visualization and validation

This record contains the complete optimization trajectories, potential model, and analysis data for the Nudged Elastic Band (NEB) calculation of the ethylene + N$_2$O cycloaddition, a Grignard rearrangement, and a bicyclobutane  reaction. This dataset supports the accompanying manuscript "Two-dimensional RMSD projections for reaction path visualization and validation" and enables the full reproduction of the reported 2D RMSD landscapes and 1D energy profiles.

 

Transition state or minimum energy path finding methods constitute a routine component of the computational chemistry toolkit. Standard analysis involvestrajectories conventionally plotted in terms of the relative energy to the initial state against a cumulative displacement variable, or the image number.These dimensional reductions obscure structural rearrangements in high dimensions and are often history dependent. This precludes the ability tocompare optimization histories of different methods beyond the number of calculations, time taken, and final saddle geometry. We present a method mappingtrajectories onto a two-dimensional projection defined by a permutation corrected root mean square deviation from the reactant and product configurations. Energyis represented as an interpolated color-mapped surface constructed from all optimization steps using a gradient-enhanced Gaussian Process with the inversemultiquadric kernel, whose posterior variance contours delineate data-supported regions from extrapolated ones. A rotated coordinate frame decomposes the RMSDplane into reaction progress and orthogonal distance. We show the utility of the framework on a cycloaddition reaction, where a machine-learned potentialsaddle and density functional theory reference lie on comparable energy contours despite geometric displacements, along with the ratification of thevisualization for more complex reactions, a Grignard rearrangement, and a conrotatory bicyclobutane ring opening.

 

The provided data encompasses the entire computational workflow: from the pre-trained PET-OMAT machine-learned potential and initial endpoint geometries to the intermediate NEB optimization steps and the final converged reaction path.

Tags: nudged-elastic-band, saddle-search-methods, transition-states, visualization
Comparing the latent features of universal machine-learning interatomic potentials

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





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

Tags: atomistic-machine-learning, feature-comparison, interatomic-potentials, machine-learning
Self-induced buckling in hollow microgels

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

Tags: buckling-instability, microgels, monomer-resolved-simulations