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Gian-Marco Rignanese

Publications and source records attributed to Gian-Marco Rignanese.

At least 19 recordsLinked to original sources
arXiv2026

Magnetic-configuration design for reliable Heisenberg exchange parameters

The determination of magnetic exchange interactions is essential for the quantitative description and predictive modeling of magnetic materials. In this work, we present a neighbor-shell-based screening method for selecting magnetic configurations suitable for extracting Heisenberg exchange parameters beyond nearest-neighbor from density functional theory (DFT) calculations. Using only the structure, the proposed approach identifies the linear independence of neighbor-shell contributions before any first-principles calculations instead of relying on trial-and-error generation of magnetic configurations. We apply the proposed approach to two representative classes of magnetic configurations: random spin states and spin spirals, and validate its predictions against direct DFT fitting for Fe and MnF$_2$. We show that only parameters obtained when all relevant neighbor-shell contributions are linearly independent remain transferable to other magnetic configurations. The proposed method provides practical guidance for selecting magnetic configurations for reliable exchange-parameter extraction and may also benefit other neighbor-shell-based models.

cond-mat.mtrl-sci↗
arXiv2026

First-Principles Spin-Lattice Coupling from Downfolded Electron-Phonon Interaction

We present a method to calculate spin-phonon coupling parameters from first-principles perturbation theory by downfolding the electron-phonon coupling (EPC). We exploit the localized nature of magnetic moments and atomic displacements by working in the Wannier representation of the electronic Hamiltonian and the EPC matrix. The spin system is mapped to a classical Heisenberg Hamiltonian, whose parameters are obtained by treating local spin rotations as a perturbation within a Green's-function formalism. The spin and phonon perturbations are connected through the EPC parameters, which enter as lattice-induced perturbations to the tight-binding Hamiltonian. By combining these lattice perturbations with local spin rotations, we obtain real-space derivatives of magnetic exchange parameters without performing displaced magnetic supercell calculations. We illustrate the method on SrMnO$_3$ and show that it can be integrated directly into standard workflows.

cond-mat.mtrl-sci↗
arXiv2026

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design

Inverse materials design is shifting materials discovery from forward prediction toward targeted proposal of candidates that satisfy objectives under physical constraints. Here, we review advances in generative crystal structure modeling, multimodal learning, and closed-loop design pipelines for crystalline solids. We survey how generators learn chemical-structural priors from databases to enable controllable sampling of periodic structures, comparing variational autoencoders, normalizing flows, autoregressive models, and diffusion models. Across these families, we examine where feasibility constraints and physical priors enter, from representations and training objectives to sampling-time guidance, screening, and relaxation. We also discuss multimodal learning combining crystal structures, thermodynamic and electronic information, microscopy, spectroscopy, processing context, and scientific text to construct materials representations. Inverse-design strategies integrating conditional generation with latent optimization, Bayesian optimization, reinforcement learning, and active learning are also examined. We highlight recurring failure modes, including surrogate exploitation, diversity collapse, distribution shift, and the stability-synthesizability gap, and outline evaluation based on validity, novelty, uniqueness, stability, and cost. To support credible claims, we define a nine-rung discovery-credibility ladder and propose a minimum reporting standard: declared matching tolerances and database snapshots; separate reporting of uniqueness, training-set memorization, and external rediscovery; novelty as a continuous distance distribution; energy-above-hull distributions with functional and hull version; relaxation-survival and dynamical stability rates; and validation cost per credible hit. Headline validity or S.U.N. rates without these disclosures should be treated as uninformative.

cond-mat.mtrl-sci↗
arXiv2026

Optical decoherence in Er$^{3+}$-doped CeO$_2$ spin qubit platforms

Erbium ions (Er$^{3+}$) in cerium dioxide (CeO$_2$) represent a promising spin-photon interface for quantum communication, but the mechanisms limiting their optical coherence remain poorly understood. Using periodic hybrid density functional theory calculations with finite-size corrections, we identify Ce$^{3+}$ polarons and their complexes with oxygen vacancies and Er$^{3+}$ dopants as likely sources of optical decoherence. These defects exhibit finite photoionization cross-sections at 0.8 eV, coinciding with both the laser excitation energy used experimentally and the emission energy of Er$^{3+}$. This resonance enables photoionization of the polarons and photoluminescence quenching of Er$^{3+}$, leading to the broadening of optical linewidths, shortening of excited-state lifetimes, and introduction of charge noise. Our concentration-dependent photocurrent measurements in Er$^{3+}$-doped CeO$_2$ films under 0.8 eV illumination validate the predicted decoherence pathway. Our combined computational and experimental results identify a concrete defect-engineering target for improving the Er$^{3+}$-doped CeO$_2$ platform, and point to a decoherence mechanism likely relevant to other Er$^{3+}$-doped multivalent-oxide quantum platforms.

cond-mat.mtrl-sci↗
arXiv2026

Machine Learning for Electrode Materials: Property Prediction via Composition

In this work, we benchmark three leading composition based Machine Learning (ML) frameworks, MODNet, CrabNet, and a random forest model based on Magpie features, predicting the properties of battery electrode materials using the Materials Project Battery Explorer dataset. We evaluate these models based on predictive accuracy, visualize numerical features using two-dimensional embeddings, and quantify performance using standard metrics. Our results demonstrate that CrabNet consistently outperforms the other models across all tests. To validate these findings, we employ bootstrap resampling and two cross-validation (CV) strategies (leave-one-cluster-out and stratified 5-fold CV), comparing each model against a control baseline, using unseen experimental data as a hold-out test. We also apply unsupervised clustering using t-SNE and DBSCAN on physically observed features extracted from matminer, revealing coherent material groupings without prior labels. The final selected model consistently improves over controls, and we believe can be used as a early stage oracle for electrode materials composition screening. Our study aims to identify the error distributions and limitations of the approach, discussing the challenges with developing robust ML models. Despite these constraints, our findings suggest the final selected model is effective for early-stage compositional screening.

cond-mat.mtrl-sci↗
arXiv2026

Fine-Tuned Machine-Learned Interatomic Potentials for Structural and Vibrational Properties of Twisted 2D Materials

Twisted van der Waals bilayers form moiré superlattices whose structural and vibrational properties are highly sensitive to variations in local stacking registry and the degree of atomic reconstruction, yet accurate atomistic modeling of these systems at the DFT level remains computationally prohibitive at small twist angles. We investigate machine-learned interatomic potentials for moiré systems, using twisted bilayer graphene, \textit{h}-BN, and MoS$_2$ as representative materials spanning a broad spectrum of mechanical compliance and atomic reconstruction behavior. We show that fine-tuning universal atomistic foundation models is essential to achieve DFT accuracy for layered materials, as broadly trained foundation models prove insufficient for resolving the subtle interlayer energetics that govern atomic reconstruction. Through local strain tensor analysis and the phonon band unfolding technique, our fine-tuned MACE model reveals a consistent reconstruction-induced strain landscape in all three materials, with extended low-energy stacking domains separated by narrow soliton lines where deformation concentrates. The system progressively optimizes the local stacking registry within each domain, giving rise to a spatially structured deformation field whose amplitude scales with the mechanical compliance of the material and can be further tuned by external perturbation. The obtained results of both atomic reconstructed structures and moiré phonon spectra present a good agreement with the reported experiments, thereby demonstrating the accuracy and efficiency of our methodology in modeling of these large scale nanomaterials.

cond-mat.mtrl-sci↗
arXiv2026

From Symmetry to Stability: Structural and Electronic Transformation in Cs$_2$KInI$_6$

Cs$_2$KInI$_6$ is a promising lead-free halide double perovskite with a calculated direct band gap of 1.94 eV, ideal for solar cell applications. Our first-principles calculations reveal that its cubic phase (Fm$\bar{3}$m) is dynamically unstable. Using an accelerated machine learning approach, we identify 42 dynamically stable structures and further validate these findings using first-principles calculations on 11 of these. The most stable phase has Cmc$2_1$ symmetry with 20 atoms/unit cell. It lies 13 meV/atom above the convex hull but lacks octahedral cation coordination. The most stable perovskite-like structure has P$\bar{3}$ symmetry with 10 atoms/unit cell and low octahedral connectivity. Structure-property trade-offs are highlighted, with calculated distortions generally widening the band gap, shifting it from direct to indirect, and flattening the band edges. This work showcases the synergy of genetic algorithms, machine-learned potentials, and first-principles validation for discovering stable, complex materials.

cond-mat.mtrl-sci↗
arXiv2026

optimade-maker: Automated generation of interoperable materials APIs from static datasets

Atomistic structural data are central to materials science, condensed matter physics, and chemistry, and are increasingly digitised across diverse repositories and databases. Interoperable access to these heterogeneous data sources enables reusable clients and tools, and is essential for cross-database analyses and data-driven materials discovery. Toward this aim, the OPTIMADE (Open Databases Integration for Materials Design) specification defines a standard REST API for atomistic structures and related properties. However, deploying and maintaining compliant services remains technically demanding and poses a significant barrier for many data providers. Here, we present optimade-maker, a lightweight toolkit for the automated generation of OPTIMADE-compliant APIs directly from raw atomistic structure and property data. The toolkit supports a wide range of raw datasets, enables conversion to a standardised OPTIMADE data representation, and allows for rapid deployment of APIs in both local and production environments. We further demonstrate it through an automated service on the Materials Cloud Archive, which automatically creates and publishes OPTIMADE APIs for contributed datasets, enabling immediate discoverability and interoperability. In addition, we implement data transformation pipelines for the Cambridge Structural Database (CSD) and the Inorganic Crystal Structure Database (ICSD), enabling unified access to these curated resources through the OPTIMADE framework. By lowering the technical barriers to interoperable data publication, optimade-maker represents an important step toward a scalable, FAIR materials data ecosystem integrating both community-contributed and curated databases.

cs.DB↗
arXiv2026

Short-range order in the CoCrFeMnNi high-entropy alloy from cluster expansion

We investigate the short-range order (SRO) and phase stability of the equiatomic CoCrFeMnNi high-entropy alloy using cluster expansion supplemented by an eigen-decomposition analysis of the SRO parameters. Our results reveal that the primary ordering behavior is determined by strong Cr-Cr repulsive interactions, complemented by attractive heteroatomic Cr-$X$ pairs in the first nearest-neighbor shell. This chemical affinity is consistent with the emergence of ordered local environments and appears to be a major contributor to the primary order-disorder transition. At lower temperatures, the spectral SRO analysis suggests two additional lower-temperature instabilities: a collective ordering instability and an Fe-rich local clustering instability.

cond-mat.mtrl-sci↗
arXiv2026

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials

While machine-learned interatomic potentials (MLIPs) accelerate phonon dispersion calculations, merely identifying dynamical instabilities in computationally predicted materials is insufficient; automated pathways to resolve them are required. We introduce VibroML, an open-source Python toolkit driven by foundational MLIPs that shifts the paradigm from stability verification to automated structural remediation. VibroML employs an energy-guided genetic algorithm that vastly outperforms traditional soft-mode following, efficiently navigating the potential energy surface to uncover diverse, dynamically stable polymorphs. As 0 K harmonic stability does not guarantee macroscopic viability, an automated molecular dynamics workflow evaluates finite-temperature structural retention. VibroML also couples with ProtoCSP, our combinatorial structure prediction engine, to stabilize frustrated crystal topologies via targeted alloying, successfully rescuing functional perovskite networks like Cs$_2$KInI$_6$ and KTaSe$_3$. Demonstrating broader applicability, we mined the Alexandria database -- where ~50% of quaternary and 99.5% of quinary elemental combinations lack any structural entries -- to identify thousands of abandoned, high-symmetry stoichiometries. Deploying ProtoCSP's "cold start" retrieval and VibroML's evolutionary search on a sample, we successfully identified dynamically stable low-symmetry candidates. Through integrated structural remediation, thermal validation, and systematic compositional exploration, VibroML enables a comprehensive deep-screening approach, yielding physically sound structural propositions that far surpass standard high-throughput workflows.

cond-mat.mtrl-sci↗
arXiv2026

A critical assessment of bonding descriptors for predicting materials properties

Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuable concept for predicting materials properties. Over the years, various theoretical frameworks have been developed to characterize bonding in solid-state materials. However, integrating bonding information from these frameworks into machine learning pipelines at scale has been limited by the lack of a systematically generated and validated database. Recent advances in high-throughput bonding analysis workflows have addressed this issue, and our previously computed Quantum-Chemical Bonding Database for Solid-State Materials was extended to include approximately 13,000 materials. This database is then used to derive a new set of quantum-chemical bonding descriptors. A systematic assessment is performed using statistical significance tests to evaluate how the inclusion of these descriptors influences the performance of machine-learning models that otherwise rely solely on structure- and composition-derived features. Models are built to predict elastic, vibrational, and thermodynamic properties typically associated with chemical bonding in materials. The results demonstrate that incorporating quantum-chemical bonding descriptors not only improves predictive performance but also helps identify intuitive expressions for properties such as the projected force constant and lattice thermal conductivity via symbolic regression.

cond-mat.mtrl-sci↗
arXiv2026

Crystal-GFN: sampling crystals with desirable properties and constraints

The discovery of novel solid-state materials, such as electrocatalysts, super-ionic conductors, or photovoltaic materials, plays a critical role in addressing various global challenges. It has, for instance, the potential to significantly improve the efficiency of renewable energy production and storage, thereby making substantial contributions to climate crisis mitigation strategies. In this paper, we introduce Crystal-GFN, a generative model of crystal structures possessing desirable properties and constraints. Operating as a multi-environment, continuous-discrete GFlowNet, it sequentially samples structural attributes of crystalline materials, namely space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physicochemical and geometric hard constraints. We demonstrate the capabilities of Crystal-GFN to efficiently discover diverse and valid crystals with various properties: low predicted formation energy (median -3.2 eV/atom), band gap close to a target value and high density. Overall, Crystal-GFN is a crystal generation method that addresses several existing challenges in the literature and opens promising paths for accelerating materials discovery with machine learning.

cs.LG↗
arXiv2026

MEIDNet: Multimodal generative AI framework for inverse materials design

In this work, we present Multimodal Equivariant Inverse Design Network (MEIDNet), a framework that jointly learns structural information and materials properties through contrastive learning, while encoding structures via an equivariant graph neural network (EGNN). By combining generative inverse design with multimodal learning, our approach accelerates the exploration of chemical-structural space and facilitates the discovery of materials that satisfy predefined property targets. MEIDNet exhibits strong latent-space alignment with cosine similarity 0.96 by fusion of three modalities through cross-modal learning. Through implementation of curriculum learning strategies, MEIDNet achieves ~60 times higher learning efficiency than conventional training techniques. The potential of our multimodal approach is demonstrated by generating low-bandgap perovskite structures at a stable, unique, and novel (SUN) rate of 13.6 %, which are further validated by ab initio methods. Our inverse design framework demonstrates both scalability and adaptability, paving the way for the universal learning of chemical space across diverse modalities.

cond-mat.mtrl-sci↗
arXiv2025

On the Role of Interlayer Electrons on the Frictional Behavior of Two-Dimensional Electrides

Friction accounts for up to 30% of global energy consumption, underscoring the urgent need for superlubricity in advanced materials. Two-dimensional (2D) electrides are layered materials with cationic layers separated by 2D confined electrons that act as anions. This study reveals the unique frictional properties of these compounds and the underlying mechanisms. We establish that interlayer friction correlates with the cationic charges and sliding-induced charge redistribution. Remarkably, the 2D electride Ba2N stands out for its lower interlayer friction than graphene, despite its stronger interlayer adhesion, defying conventional tribological understanding. This anomalous behavior arises from electron redistribution as the dominant energy dissipation pathway. Combining ab initio calculations and deep potential molecular dynamics (DPMD) simulations, we show that incommensurate twisted interfaces (2° < θ < 58°) in Ba2N achieve structural superlubricity by suppressing out-of-plane buckling and energy corrugation. Notably, a critical normal load of 2.3 GPa enables barrier-free sliding in commensurate Ba2N (θ = 0°), with an ultralow shear-to-load ratio of 0.001, suggesting the potential for superlubricity. Moreover, electron doping effectively reduces interlayer friction by controllably modulating stacking energies in 2D electrides. These findings establish 2D electrides as a transformative platform for energy-efficient tribology, enabling scalable superlubricity through twist engineering, load adaptation, or electrostatic gating. Our work advances the fundamental understanding of electron-mediated friction, with Ba2N serving a model system for cost-effective, high-performance material design.

cond-mat.mtrl-sci↗
arXiv2025

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability

This study introduces MatterVial, an innovative hybrid framework for feature-based machine learning in materials science. MatterVial expands the feature space by integrating latent representations from a diverse suite of pretrained graph neural network (GNN) models including: structure-based (MEGNet), composition-based (ROOST), and equivariant (ORB) graph networks, with computationally efficient, GNN-approximated descriptors and novel features from symbolic regression. Our approach combines the chemical transparency of traditional feature-based models with the predictive power of deep learning architectures. When augmenting the feature-based model MODNet on Matbench tasks, this method yields significant error reductions and elevates its performance to be competitive with, and in several cases superior to, state-of-the-art end-to-end GNNs, with accuracy increases exceeding 40% for multiple tasks. An integrated interpretability module, employing surrogate models and symbolic regression, decodes the latent GNN-derived descriptors into explicit, physically meaningful formulas. This unified framework advances materials informatics by providing a high-performance, transparent tool that aligns with the principles of explainable AI, paving the way for more targeted and autonomous materials discovery.

cond-mat.mtrl-sci↗
arXiv2025

Generative AI for Crystal Structures: A Review

As in many other fields, the rapid rise of generative artificial intelligence is reshaping materials discovery by offering new ways to propose crystal structures and, in some cases, even predict desired properties. This review provides a comprehensive survey of recent advancements in generative models specifically for inorganic crystalline materials. We begin by introducing the fundamentals of generative modeling and invertible material descriptors. We then propose a taxonomy based on architecture, representation, conditioning, and materials domain to categorize the diverse range of current generative AI models. We discuss data sources and address challenges related to performance metrics, emphasizing the need for standardized benchmarks. Specific examples and applications of novel generated structures are presented. Finally, we examine current limitations and future directions in this rapidly evolving field, highlighting its potential to accelerate the discovery of new inorganic materials.

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

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.

cond-mat.mtrl-sci↗