arXiv subjects

Gian-Marco Rignanese

Publications and source records attributed to Gian-Marco Rignanese.

At least 37 records · Page 2Linked to original sources

arXiv2025

Lithium Intercalation in the Anisotropic van der Waals Magnetic Semiconductor CrSBr

Alkali metal intercalation is an important strategy for doping van der Waals materials. Lithium, in particular, was shown to achieve exceptional charge carrier densities, reaching levels at which fundamental electrical, optical, and magnetic material properties begin to be strongly modified. While lithium is known to be highly volatile, its migration dynamics in anisotropic layered crystals remain poorly understood. In this work, we investigate the intercalation of lithium in-between layers of the anisotropic magnetic semiconductor CrSBr. Using exfoliated crystals, we are able to monitor the dynamics of the intercalation process in real time through optical and electrical characterization methods. Our measurements reveal highly anisotropic migration of Lithium characterized by diffusion coefficients that differ by more than one order of magnitude along a- and b-directions. This finding is in good agreement with our molecular dynamics simulations which show trajectories of lithium atoms primarily follow the Br-chains in the a-direction. Beyond that, we find that partially covering CrSBr crystals by thin hexagonal boron nitride (hBN) flakes has a significant impact on the intercalation process, and that lithium strongly enhances the electrical conductivity along the a-axis. Our method offers a new platform for lithium diffusion studies and encourages further research to pursue the fabrication of lithium-doped devices.

cond-mat.mtrl-sci↗

arXiv2025

Datatractor: Metadata, automation, and registries for extractor interoperability in the chemical and materials sciences

Two key issues hindering the transition towards FAIR data science are the poor discoverability and inconsistent instructions for the use of data extractor tools, i.e., how we go from raw data files created by instruments, to accessible metadata and scientific insight. If the existing format conversion tools are hard to find, install, and use, their reimplementation will lead to a duplication of effort, and an increase in the associated maintenance burden is inevitable. The Datatractor framework presented in this work addresses these issues. First, by providing a curated registry of such extractor tools their discoverability will increase. Second, by describing them using a standardised but lightweight schema, their installation and use is machine-actionable. Finally, we provide a reference implementation for such data extraction. The Datatractor framework can be used to provide a public-facing data extraction service, or be incorporated into other research data management tools providing added value.

physics.data-an↗

arXiv2025

Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening

Due to their abundant use in all-solid-state lasers, nonlinear optical (NLO) crystals are needed for many applications across diverse fields such as medicine and communication. However, because of conflicting requirements, the design of suitable inorganic crystals with strong second-harmonic generation (SHG) has proven to be challenging to both experimentalists and computational scientists. In this work, we leverage a data-driven approach to accelerate the search for high-performance NLO materials. We construct an extensive pool of candidates using databases within the OPTIMADE federation and employ an active learning strategy to gather optimal data while iteratively improving a machine learning model. The result is a publicly accessible dataset of $\sim$2,200 computed SHG tensors using density-functional perturbation theory. We further assess the performance of machine learning models on SHG prediction and introduce a multi-fidelity correction-learning scheme to refine data accuracy. This study represents a significant step towards data-driven materials discovery in the NLO field and demonstrates how new materials can be screened in an automated fashion.

cond-mat.mtrl-sci↗

arXiv2025

Machine Learning on Multiple Topological Materials Datasets

A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are developed to categorize materials into five distinct topological types, with the XGBoost model achieving an impressive 85.2% classification accuracy. By conducting generalization tests on different sub-datasets, differences are identified between the original datasets in terms of topological types, chemical elements, unknown magnetic compounds, and feature space coverage. Their impact on model performance is analyzed. Turning to the simpler binary classification between trivial insulators and nontrivial topological materials, three different approaches are also tested. Key characteristics influencing material topology are identified, with the maximum packing efficiency and the fraction of $\textit{p}$ valence electrons being highlighted as critical features.

cond-mat.mtrl-sci↗

arXiv2025

Towards MatCore: A Unified Metadata Standard for Materials Science

The materials science community seeks to support the FAIR principles for computational simulation research. The MatCore Project was recently launched to address this need, with the goal of developing an overall metadata framework and accompanying guidelines. This paper reports on the MatCore goals and overall progress. Historical background context is provided, including a review of the principles underlying successful core metadata standards. The paper also presents selected MatCore examples and discusses future plans.

cond-mat.mtrl-sci↗

arXiv2025

A generative material transformer using Wyckoff representation

Materials play a critical role in various technological applications. Identifying and enumerating stable compounds, those near the convex hull, is therefore essential. Despite recent progress, generative models either have a relatively low rate of stable compounds, are computationally expensive, or lack symmetry. In this work we present Matra-Genoa, an autoregressive transformer model built on invertible tokenized representations of symmetrized crystals, including free coordinates. This approach enables sampling from a hybrid action space. The model is trained across the periodic table and space groups and can be conditioned on specific properties. We demonstrate its ability to generate stable, novel, and unique crystal structures by conditioning on the distance to the convex hull. Resulting structures are 8 times more likely to be stable than baselines using PyXtal with charge compensation, while maintaining high computational efficiency. We also release a dataset of 3 million unique crystals generated by our method, including 4,000 compounds verified by density-functional theory to be within 0.001 eV/atom of the convex hull.

cond-mat.mtrl-sci↗

arXiv2024

Surface-plasmon polaritons in multilayer jellium systems: dispersion and spatial description

Surface-plasmon polaritons (SPPs) are electromagnetic waves that propagate along metal-dielectric interfaces, with important applications in sensing, energy, and nanotechnology. While the behavior of SPPs in single metal slabs is well understood, the coupling between plasmon modes in multilayer systems has received less attention. In this paper, we explore the response functions of SPPs in single-slab, double-slab, and two-different-slab systems using the jellium model. Thanks to a comparison with classical models, our study reveals how quantum effects influence the resonance frequencies of these modes. It also details the spatial description of the different SPP modes and unveils how their coupling occurs in two-different-slab systems. These findings provide new insights into the behavior of SPPs, especially in complex nanostructures.

physics.optics↗

arXiv2024

A substitutional quantum defect in WS$_2$ discovered by high-throughput computational screening and fabricated by site-selective STM manipulation

Point defects in two-dimensional materials are of key interest for quantum information science. However, the space of possible defects is immense, making the identification of high-performance quantum defects extremely challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS$_2$, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co$_{\rm S}^{0}$) as very promising and fabricate it with scanning tunneling microscopy (STM). The Co$_{\rm S}^{0}$ electronic structure measured by STM agrees with first principles and showcases an attractive new quantum defect. Our work shows how HT computational screening and novel defect synthesis routes can be combined to design new quantum defects.

cond-mat.mtrl-sci↗

arXiv2024

Assessing carrier mobility, dopability, and defect tolerance in the chalcogenide perovskite BaZrS$_3$

The chalcogenide perovskite BaZrS$_3$ has attracted much attention as a promising solar absorber for thin-film photovoltaics. Here, we use first-principles calculations to evaluate its carrier transport and defect properties. We find that BaZrS$_3$ has a phonon-limited electron mobility of 37 cm$^2$/Vs comparable to that in halide perovskites but lower hole mobility of 11 cm$^2$/Vs. The defect computations indicate that BaZrS$_3$ is intrinsically n-type due to shallow sulfur vacancies, but that strong compensation by sulfur vacancies will prevent attempts to make it p-type. We also establish that BaZrS$_3$ shows some degree of defect tolerance, presenting only few low formation energy, deep intrinsic defects. Among the deep defects, sulfur interstitials are the dominant nonradiative recombination centers but exhibit a moderate capture coefficient. Our work highlights the material's intrinsic limitations in carrier mobility and p-type doping and suggests focusing on suppressing the formation of sulfur interstitials to reach longer carrier lifetime.

cond-mat.mtrl-sci↗

arXiv2024

Systematic assessment of various universal machine-learning interatomic potentials

Machine-learning interatomic potentials have revolutionized materials modeling at the atomic scale. Thanks to these, it is now indeed possible to perform simulations of \abinitio quality over very large time and length scales. More recently, various universal machine-learning models have been proposed as an out-of-box approach avoiding the need to train and validate specific potentials for each particular material of interest. In this paper, we review and evaluate five different universal machine-learning interatomic potentials (uMLIPs), all based on graph neural network architectures which have demonstrated transferability from one chemical system to another. The evaluation procedure relies on data both from a recent verification study of density-functional-theory implementations and from the Materials Project. Through this comprehensive evaluation, we aim to provide guidance to materials scientists in selecting suitable models for their specific research problems, offer recommendations for model selection and optimization, and stimulate discussion on potential areas for improvement in current machine-learning methodologies in materials science.

cond-mat.mtrl-sci↗

arXiv2024

Facilities and practices for linear response Hubbard parameters U and J in Abinit

Members of the DFT+U family of functionals are increasingly prevalent methods of addressing errors intrinsic to (semi-) local exchange-correlation functionals at minimum computational cost, but require their parameters U and J to be calculated in situ for a given system of interest, simulation scheme, and runtime parameters. The SCF linear response approach offers ab initio acquisition of the U and has recently been extended to compute the J analogously, which measures localized errors related to exchange-like effects. We introduce a renovated post-processor, the lrUJ utility, together with this detailed best-practices guide, to enable users of the popular, open-source Abinit first-principles simulation suite to engage easily with in situ Hubbard parameters and streamline their incorporation into material simulations of interest. Features of this utility, which may also interest users and developers of other DFT codes, include $n$-degree polynomial regression, error analysis, Python plotting facilities, didactic documentation, and avenues for further developments. In this technical introduction and guide, we place particular emphasis on the intricacies and potential pitfalls introduced by the projector augmented wave (PAW) method, SCF mixing schemes, and non-linear response, several of which are translatable to DFT+U(+J) implementations in other packages.

physics.comp-ph↗

arXiv2024

Optical materials discovery and design with federated databases and machine learning

Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.

cond-mat.mtrl-sci↗

arXiv2024

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the upcoming v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development.

cond-mat.mtrl-sci↗

arXiv2024

Native point defects in HgCdTe infrared detector material: Identifying deep centers from first principles

We investigate the native point defects in the long-wavelength infrared (LWIR) detector material Hg$_{0.75}$Cd$_{0.25}$Te using a dielectric-dependent hybrid density functional combined with spin-orbit coupling. Characterizing these point defects is essential as they are responsible for intrinsic doping and nonradiative recombination centers in the detector material. The dielectric-dependent hybrid functional allows for an accurate description of the band gap ($E_g$) for Hg$_{1-x}$Cd$_{x}$Te (MCT) over the entire compositional range, a level of accuracy challenging with standard hybrid functionals. Our comprehensive examination of the native point defects confirms that cation vacancies $V_\text{Hg(Cd)}$ are the primary sources of $p$-type conductivity in the LWIR material given their low defect formation energies and the presence of a shallow acceptor level ($-$/0) near the valence-band maximum (VBM). In addition to the shallow acceptor level, the cation vacancies exhibit a deep charge transition level (2$-$/$-$) situated near the midgap, characteristic of nonradiative recombination centers. Our results indicate that Hg interstitial could also be a deep center in the LWIR MCT through a metastable configuration under the Hg-rich growth conditions. While an isolated Te antisite does not show deep levels, the formation of $V_\text{Hg}$-Te$_\text{Hg}$ defect complex introduces a deep acceptor level within the band gap.

cond-mat.mtrl-sci↗

arXiv2023

Surface Enhanced Infrared Absorption mechanism and modification of the plasmonic response

Surface Enhanced Infrared Absorption (SEIRA) is an experimental method where trace amount of a compound can be detected with high sensibility. This high detection sensibility is the result of the interaction of the molecules with a localized plasmon, usually from a metallic nano-particle. In this study we numerically investigate by discrete dipole approximation the origin of the Fano-like response of the system, including the induced transparency when the plasmon resonance and the molecular vibrational mode coincide. The detailed analysis of the localization of the absorption show that the modification of the absorption cross-section when the molecule is present comes from a change of the plasmonic resonance, not from the direct molecular response which is negligible. This sheds a new light on the SEIRA mechanism. In particular, it demonstrates that the sensibility is associated with the influence of the molecule on the plasmon resonance rather than with the local field enhancement itself.

physics.optics↗

arXiv2023

Discovery of the Zintl-phosphide BaCd$_{2}$P$_{2}$ as a long carrier lifetime and stable solar absorber

Thin-film photovoltaics offers a path to significantly decarbonize our energy production. Unfortunately, current materials commercialized or under development as thin-film solar cell absorbers are far from optimal as they show either low power conversion efficiency or issues with earth-abundance and stability. Entirely new and disruptive materials platforms are rarely discovered as the search for new solar absorbers is traditionally slow and serendipitous. Here, we use first principles high-throughput screening to accelerate this process. We identify new solar absorbers among known inorganic compounds using considerations on band gap, carrier transport, optical absorption but also on intrinsic defects which can strongly limit the carrier lifetime and ultimately the solar cell efficiency. Screening about 40,000 materials, we discover the Zintl-phosphide BaCd$_{2}$P$_{2}$ as a potential high-efficiency solar absorber. Follow-up experimental work confirms the predicted promises of BaCd$_{2}$P$_{2}$ highlighting an optimal band gap for visible absorption, bright photoluminescence, and long carrier lifetime of up to 30 ns even for unoptimized powder samples. Importantly, BaCd$_{2}$P$_{2}$ does not contain any critical elements and is highly stable in air and water. Our work opens an avenue for a new family of stable, earth-abundant, high-performance Zintl-based solar absorbers. It also demonstrates how recent advances in first principles computation can accelerate the search of photovoltaic materials by combining high-throughput screening with experiment.

cond-mat.mtrl-sci↗

arXiv2023

Shared Metadata for Data-Centric Materials Science

The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on "Shared Metadata and Data Formats for Big-Data Driven Materials Science". We start from an operative definition of metadata, and what features a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them.

cond-mat.mtrl-sci↗

arXiv2023

How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies).

cond-mat.mtrl-sci↗