arXiv subjects

Aldo H. Romero

Publications and source records attributed to Aldo H. Romero.

At least 19 recordsLinked to original sources

arXiv2026

AtomBench: A Benchmarking Framework for Generative Crystal Reconstruction Models in Conventional Superconductors

A key question in benchmarking generative crystal reconstruction models is how the amount and type of crystallographic information provided to a generative model affects its ability to reconstruct atomic structures. Yet such comparisons often overlook the fact that models receive unequal information about the target during reconstruction, thereby confounding architectural conclusions. We present AtomBench, an extensible, model-agnostic framework for comparing generative models on a well-defined crystal reconstruction task (rather than \textit{de novo} generation), which we here apply to conventional superconductors. We train and evaluate four models, AtomGPT, CDVAE, FlowMM, and MatterGen, on the JARVIS Supercon-3D and Alexandria DS-A/B datasets, grouping them by the information each accesses at inference. Reconstruction fidelity is measured by the Kullback-Leibler divergence (KLD) and mean absolute error (MAE) of lattice parameters and the root-mean-squared displacement (RMSD) of atomic coordinates. We further introduce the continuous corrected RMSD (ccRMSD), a continuous measure of local geometric fidelity defined for every structure in the test set. MatterGen achieves the best atomic-coordinate reconstruction, followed by AtomGPT, while CDVAE reconstructs lattices most accurately, and FlowMM is the least accurate but fastest overall. We find that conditioning on critical temperature T$_c$ does not consistently improve fidelity. We also release AtomBench as an open-source Python package that reproduces all reported reconstruction metrics, figures, and tables from one or more benchmark files and supports direct submission to the JARVIS-Leaderboard. Any inverse model emitting crystal reconstructions can be benchmarked with \texttt{atombench}, and we encourage community use. https://github.com/atomgptlab/atombench

cs.LG↗

arXiv2026

Hallucination Detector: A hybrid LLM and Semantic Scholar tool calling for detecting hallucination in scientific literature on AtomGPT.org

Large language models are now commonly used as partners in scientific writing, and this shift has brought a subtler type of failure: made-up references. Fabricated authors, bogus DOIs, wrongly assigned identifiers, and citations that merge elements from multiple genuine articles are now being inserted into manuscripts at a volume that traditional peer review was never meant to handle. Recent audits reveal that such references have already slipped through the review process and made their way into the published literature, including leading journals and conferences. Automated verification that operates at the speed and scale of modern content production has therefore become a necessary safeguard rather than a convenience. This work presents and evaluates the AtomGPT reference checker (https://atomgpt.org/hallucination_detector), an open, web-accessible tool that verifies citations against the scholarly literature by combining large-language-model field extraction with structured retrieval from Semantic Scholar. For each reference, the tool extracts the bibliographic fields, retrieves the closest matching real papers, and scores the agreement across title, authorship, and venue to produce a graded judgment of whether a citation is trustworthy, partially supported, or likely fabricated. We benchmark the tool against an externally curated set of confirmed hallucinated citations from accepted NeurIPS 2025 papers and find that it reliably flags the great majority of them.

cs.DL↗

arXiv2026

AI-Driven Expansion and Application of the Alexandria Database

We present a novel multi-stage workflow for computational materials discovery that achieves a 99% success rate in identifying compounds within 100 meV/atom of thermodynamic stability, with a threefold improvement over previous approaches. By combining the Matra-Genoa generative model, Orb-v2 universal machine learning interatomic potential, and ALIGNN graph neural network for energy prediction, we generated 119 million candidate structures and added 1.3 million DFT-validated compounds to the ALEXANDRIA database, including 74 thousand new stable materials. The expanded ALEXANDRIA database now contains 5.8 million structures with 175 thousand compounds on the convex hull. Predicted structural disorder rates (37-43%) match experimental databases, unlike other recent AI-generated datasets. Analysis reveals fundamental patterns in space group distributions, coordination environments, and phase stability networks, including sub-linear scaling of convex hull connectivity. We release the complete dataset, including sAlex25 with 14 million out-of-equilibrium structures containing forces and stresses for training universal force fields. We demonstrate that fine-tuning a GRACE model on this data improves benchmark accuracy. All data, models, and workflows are freely available under Creative Commons licenses.

cond-mat.mtrl-sci↗

arXiv2026

Structural chirality measurements and computation of handedness in periodic solids

We compare the various chirality measures most widely used in the literature to quantify chiral symmetry in extended solids, i.e., the continuous chirality measure, the Hausdorff distance, and the angular momentum. By studying these functions in an algebraically tractable case, we can evaluate their strengths and weaknesses when applied to more complex crystals. Going beyond those classical calculations, we propose a new method to quantify the handedness of a crystal based on a pseudoscalar function, i.e., the helicity. This quantity, borrowed from hydrodynamics, can be computed from the eigenvector carrying the system from the high-symmetry non-chiral phase to the low-symmetry chiral phase. Different model systems like K$_3$NiO$_2$, CsCuCl$_3$ and MgTi$_2$O$_4$ are used as test cases where we show the superior interest of using helicity to quantify chirality together with the handedness distinction.

cond-mat.mtrl-sci↗

arXiv2025

Abinit 2025: New Capabilities for the Predictive Modeling of Solids and Nanomaterials

Abinit is a widely used scientific software package implementing density functional theory and many related functionalities for excited states and response properties. This paper presents the novel features and capabilities, both technical and scientific, which have been implemented over the past 5 years. This evolution occurred in the context of evolving hardware platforms, high-throughput calculation campaigns, and the growing use of machine learning to predict properties based on databases of first principles results. We present new methodologies for ground states with constrained charge, spin or temperature; for density functional perturbation theory extensions to flexoelectricity and polarons; and for excited states in many-body frameworks including GW, dynamical mean field theory, and coupled cluster. Technical advances have extended abinit high-performance execution to graphical processing units and intensive parallelism. Second principles methods build effective models on top of first principles results to scale up in length and time scales. Finally, workflows have been developed in different community frameworks to automate \abinit calculations and enable users to simulate hundreds or thousands of materials in controlled and reproducible conditions.

cond-mat.mtrl-sci↗

arXiv2025

Pathways to crystal chirality An algorithm to identify new displacive chiral phase transitions

We present an algorithm that integrates pseudosymmetry search with first-principles calculations to systematically identify achiral parent structures and establish potential chiral displacive transitions linking them to their corresponding chiral phases within the 22 enantiomorphic space groups. This approach enables a robust exploration of structural relationships, offering new insights into symmetry-driven properties. Our workflow streamlines the discovery of displacive chiral phase transitions driven by soft phonon modes, providing insights into the mechanisms of structural chirality in inorganic materials. We apply this methodology on the chiral phases of TeO$_2$, Na$_2$SeO$_9$, Sr$_2$As$_2$O$_7$, As$_2$O$_5$, Rb$_2$Be$_2$O$_3$, and CaTe$_2$O$_3$. Demonstrating that some do not have a minimal supergroup that allows for an achiral phase; some can have a minimal supergroup, still, no unstable phonon mode exists in the achiral phase; and somewhere the minimal supergroup exists with a soft phonon mode connecting the identified achiral phase and the chiral phase through small continuous displacements.

cond-mat.mtrl-sci↗

arXiv2025

Supervised Pretraining for Material Property Prediction

Accurate prediction of material properties facilitates the discovery of novel materials with tailored functionalities. Deep learning models have recently shown superior accuracy and flexibility in capturing structure-property relationships. However, these models often rely on supervised learning, which requires large, well-annotated datasets an expensive and time-consuming process. Self-supervised learning (SSL) offers a promising alternative by pretraining on large, unlabeled datasets to develop foundation models that can be fine-tuned for material property prediction. In this work, we propose supervised pretraining, where available class information serves as surrogate labels to guide learning, even when downstream tasks involve unrelated material properties. We evaluate this strategy on two state-of-the-art SSL models and introduce a novel framework for supervised pretraining. To further enhance representation learning, we propose a graph-based augmentation technique that injects noise to improve robustness without structurally deforming material graphs. The resulting foundation models are fine-tuned for six challenging material property predictions, achieving significant performance gains over baselines, ranging from 2% to 6.67% improvement in mean absolute error (MAE) and establishing a new benchmark in material property prediction. This study represents the first exploration of supervised pertaining with surrogate labels in material property prediction, advancing methodology and application in the field.

cs.LG↗

arXiv2025

Light-driven lattice metastability for enhanced superconductivity in FeSe/SrTiO3

Driven quantum materials with on demand properties controlled by external stimuli are critical for emergent quantum technology. In optically tunable superconducting heterostructures, the lattice responses at the buried interface may hold the key to the light susceptibility but is very challenging to detect. In this work, a nondestructive synchrotron-based X-ray scattering phase-retrieval technique is implemented in monolayer-FeSe/SrTiO3 heterostructures to capture the three-dimensional interfacial atomic displacements in-situ as the interface superconductivity is actively manipulated by light. It is found that the interlayer sliding between FeSe and SrTiO3 can drastically alter how the lattice responds to the light. In domains with selected stacking configurations, the interface transforms the very weak photoexcitation in SrTiO3 into significant Fe-atom displacements in FeSe and generate metastable interfacial structures that can lead to a persistent superconductivity enhancement. These findings demonstrate an effective strategy for achieving greatly amplified light-lattice coupling for efficient quantum phase manipulations at designed interfaces.

cond-mat.mtrl-sci↗

arXiv2025

ParquetDB: A Lightweight Python Parquet-Based Database

Traditional data storage formats and databases often introduce complexities and inefficiencies that hinder rapid iteration and adaptability. To address these challenges, we introduce ParquetDB, a Python-based database framework that leverages the Parquet file format's optimized columnar storage. ParquetDB offers efficient serialization and deserialization, native support for complex and nested data types, reduced dependency on indexing through predicate pushdown filtering, and enhanced portability due to its file-based storage system. Benchmarks show that ParquetDB outperforms traditional databases like SQLite and MongoDB in managing large volumes of data, especially when using data formats compatible with PyArrow. We validate ParquetDB's practical utility by applying it to the Alexandria 3D Materials Database, efficiently handling approximately 4.8 million complex and nested records. By addressing the inherent limitations of existing data storage systems and continuously evolving to meet future demands, ParquetDB has the potential to significantly streamline data management processes and accelerate research development in data-driven fields.

cs.DB↗

arXiv2025

Handedness selection and hysteresis of chiral orders in crystals

A phase transition can drive the spontaneous emergence of chiral orders in crystals below a critical temperature. However, selecting either a right- or a left-handed phase with the aid of electromagnetic fields is challenging, particularly when intrinsic polar and axial moments are lacking. In this work we show that \textit{purely} chiral phases with opposite handedness, when both deriving from one degenerate instability, are linked by accessible transition states. While these states compete with the chirality under an electromagnetic field, a circularly polarized source can select the handedness of the system. This selection is mediated by a chiral monopole and may further result in a hysteresis process of the gyrotropic properties, namely the optical activity, below the critical temperature. We suggest several materials, among which K$_3$NiO$_2$, as candidates for possible experimental observation.

cond-mat.mtrl-sci↗

arXiv2024

A phonon-driven mechanism for an emergent and reversible chirality in crystals

We demonstrate through first-principles calculations applied to the K$_{3}$NiO$_{2}$ crystal that a structural phase transition from an achiral to a chiral phase can be mediated by a degenerate soft phonon mode and controlled by pressure and epitaxial strain. Breaking such degeneracy with an electric field generates a competition between enantiomorphic, polar, and orthorhombic displacements. Originated by the interaction between spontaneous chiral and induced polar and axial modes, an optimal parameter window for converting the handedness of the system into its opposite kind is observed.

cond-mat.mtrl-sci↗

arXiv2024

Enhancing material property prediction with ensemble deep graph convolutional networks

Machine learning (ML) models have emerged as powerful tools for accelerating materials discovery and design by enabling accurate predictions of properties from compositional and structural data. These capabilities are vital for developing advanced technologies across fields such as energy, electronics, and biomedicine, potentially reducing the time and resources needed for new material exploration and promoting rapid innovation cycles. Recent efforts have focused on employing advanced ML algorithms, including deep learning - based graph neural network, for property prediction. Additionally, ensemble models have proven to enhance the generalizability and robustness of ML and DL. However, the use of such ensemble strategies in deep graph networks for material property prediction remains underexplored. Our research provides an in-depth evaluation of ensemble strategies in deep learning - based graph neural network, specifically targeting material property prediction tasks. By testing the Crystal Graph Convolutional Neural Network (CGCNN) and its multitask version, MT-CGCNN, we demonstrated that ensemble techniques, especially prediction averaging, substantially improve precision beyond traditional metrics for key properties like formation energy per atom ($ΔE^{f}$), band gap ($E_{g}$) and density ($ρ$) in 33,990 stable inorganic materials. These findings support the broader application of ensemble methods to enhance predictive accuracy in the field.

cs.LG↗

arXiv2024

Polar phonons and magnetic excitations in the antiferromagnet CoF$_2$

The coupling between antiferromagnetic spins and infrared-active phonons in solids is responsible for many intriguing phenomena and is a field of intense research with extensive potential applications in the modern devices based on antiferromagnetic spintronics and phononics. Insulating rutile antiferromagnetic crystal CoF$_2$ is one of the model materials for studying nonlinear magnetophononics due to the strong spin-lattice coupling as a result of the orbitally degenerate ground state of Co$^{2+}$ ions manifested in the plethora of static and induced piezomagnetic effects. Here we report results on the complete infrared spectroscopy study of lattice and magnetic dynamics in CoF$_2$ in a wide temperature range and their careful analysis. We observed that infrared-active phonons demonstrate frequency shifts at the antiferromagnetic ordering. Furthermore, using first-principles calculations, we examined the lattice dynamics and disclosed that these frequency shifts are rather due to the spin-phonon coupling than geometrical lattice effects. Next we found that the low-frequency dielectric permittivity demonstrates distinct changes at the antiferromagnetic ordering due to the spontaneous magnetodielectric effect caused by the behavior of infrared-active phonons. In addition, we have observed magnetic excitations in the infrared spectra and identified their magnetodipole origin. To strengthen our conclusions, we analyze the theoretical phonon-magnon coupling overall phonons at the $Γ$ point. We conclude that the largest effect comes from the $A_{1g}$ and $B_{2g}$ Raman-active modes. As such, our results establish a solid basis for further investigations and more deeper understanding of the coupling of phonons with spins and magnetic excitations in antiferromagnets.

cond-mat.mtrl-sci↗

arXiv2024

Structural chirality and related properties in the periodic inorganic solids: Review and perspectives

Chirality refers to the asymmetry of objects that cannot be superimposed on their mirror image. It is a concept that exists in various scientific fields and has profound consequences. Although these are perhaps most widely recognized within biology, chemistry, and pharmacology, recent advances in chiral phonons, topological systems, crystal enantiomorphic materials, and magneto-chiral materials have brought this topic to the forefront of condensed matter physics research. Our review discusses the symmetry requirements and the features associated with structural chirality in inorganic materials. This allows us to explore the nature of phase transitions in these systems, the coupling between order parameters, and their impact on the material's physical properties. We highlight essential contributions to the field, particularly recent progress in the study of chiral phonons, altermagnetism, magnetochirality between others. Despite the rarity of naturally occurring inorganic chiral crystals, this review also highlights a significant knowledge gap, presenting challenges and opportunities for structural chirality mostly at the fundamental level, e.g., chiral displacive phase transitions and ferrochirality, possibilities of tuning and switching structural chirality by external means (electric, magnetic, or strain fields), whether chirality could be an independent order parameter, and whether structural chirality could be quantified, etc. Beyond simply summarising this field of research, this review aims to inspire further research in materials science by addressing future challenges, encouraging the exploration of chirality beyond traditional boundaries, and seeking the development of innovative materials with superior or new properties.

cond-mat.mtrl-sci↗

arXiv2024

Promises and technological prospects of two-dimensional Rashba materials

The Rashba spin-orbit coupling effect, primarily arising from structural-inversion asymmetry in periodic crystals, has garnered considerable attention due to its tunability and potential applications in spintronics. Its capability to manipulate electron spin without an external magnetic field opens new avenues for spintronic device design, particularly in semiconductor technology. Within this framework, 2D Rashba materials hold special interest due to their inherent characteristics, which facilitate miniaturization and engineering capabilities. In this Perspective article, we provide an overview of recent advancements in the research of 2D Rashba materials, aiming to offer a comprehensive understanding of the diverse manifestations and multifaceted implications of the Rashba effect in material science. Rather than merely presenting a list of materials, our approach involves synthesizing various viewpoints, assessing current trends, and addressing challenges within the field. Our objective is to bridge the gap between fundamental research and practical applications by correlating each material with the necessary advancements required to translate theoretical concepts into tangible technologies. Furthermore, we highlight promising avenues for future research and development, drawing from insights gleaned from the current state of the field.

cond-mat.mtrl-sci↗

arXiv2024

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard

cond-mat.mtrl-sci↗

arXiv2024

Ferroelectricity and chirality in the Pb$_5$Ge$_3$O$_{11}$ crystal

We study from first-principles calculations the ferroelectric structural phase transition of Pb$_5$Ge$_3$O$_{11}$ crystal. The calculations of phonons and Born effective charges of the paraelectric phase allow us to identify a polar instability that is unstable in both transverse-optic and longitudinal-optic versions, giving rise to an entire branch of instability along a propagation vector parallel to the mode polarization (the hexagonal axe). This is the hint of hyperferroelectricity and the stable head-to-head and tail-to-tail domain, as recently reported from both experiments and theory. Then, our analysis of the ferroelectric phase shows that the polarization of Pb$_5$Ge$_3$O$_{11}$ is uniaxial along the hexagonal axes and with small in-plane components due to a piezoelectric effect. The symmetry-adapted mode analysis shows that the total ferroelectric ground state distortion comes mainly from polar distortions of the unstable polar phonon mode but also from an invariant, cooperative mode that amplifies the polar deformation. We also build a phenomenological model that highlights how the coupling between these modes is at play and helps us understand how to reproduce the second-order phase transition. At last, we also quantify the structural chirality through the continuous symmetry measure method and trace its origin to the polar unstable mode itself. By extending our approach to the phonon states we further show that the chirality is poorly affected by the relaxation but could also be enhanced by activating high frequency modes with polar symmetry. Finally we study the phonon angular momentum (AM) distribution in both phases and identify trends in the AM behaviour across the Brillouin zone.

cond-mat.mtrl-sci↗

arXiv2023

Two-Dimensional Noble Metal Chalcogenides in the Frustrated Snub-Square Lattice

We study two-dimensional noble metal chalcogenides, with composition {Cu, Ag, Au}2{S, Se, Te}, crystallizing in a snub-square lattice. This is a semi-regular two-dimensional tesselation formed by triangles and squares that exhibits geometrical frustration. We use for comparison a square lattice, from which the snub-square tiling can be derived by a simple rotation of the squares. The mono-layer snub-square chalcogenides are very close to thermodynamic stability, with the most stable system (Ag2Se) a mere 7 meV/atom above the convex hull of stability. All compounds studied in the square and snub-square lattice are semiconductors, with band gaps ranging from 0.1 to more than 2.5 eV. Excitonic effects are strong, with an exciton binding energy of around 0.3 eV. We propose the Cu (001) surface as a possible substrate to synthesize Cu2Se, although many other metal and semiconducting surfaces can be found with very good lattice matching.

cond-mat.mtrl-sci↗