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Francesco Mercuri

Publications and source records attributed to Francesco Mercuri.

16 recordsLinked to original sources

Atomic Scale Ordering of Sulfur Vacancies Enhances Charge Transport in Monolayer MoS$_2$

Defect engineering in two-dimensional semiconductors has primarily focused on controlling the nature and concentration of atomic defects. Here, we show that the spatial arrangement of defects can be equally decisive in determining electronic transport. Using sulfur vacancies in monolayer MoS$_2$ as a model system, we investigate the impact of vacancy ordering through density functional theory, density functional tight-binding calculations, and quantum transport simulations. We demonstrate that a periodic vacancy arrangement at a concentration of 11.1% transforms isolated defect states into a narrow dispersive in-gap miniband, whereas randomly distributed vacancies generate only localized electronic states. This electronic transition fundamentally alters charge transport, enabling band-like propagation through the defect network rather than transport limited by disconnected localized states. A systematic analysis of the complete symmetry-reduced ensemble of 94 non-adjacent four-vacancy configurations shows that the ordered pattern lies within a broad low-energy manifold and is not energetically anomalous, although it is not the thermodynamic ground state. Device-level simulations of Au/MoS$_2$/Au junctions reveal efficient alignment of the metal Fermi level with vacancy-derived states, promoting charge injection into the defect miniband. As a result, ordered vacancy arrays exhibit electrical currents up to five orders of magnitude higher than statistically equivalent random distributions and can approach, or locally exceed, the transport performance of pristine MoS$_2$. These findings establish atomic-scale defect ordering as a powerful design principle for two-dimensional materials, demonstrating that the organization of defects, beyond their concentration alone, provides a route to simultaneously preserve functionality and high electrical conductivity in highly defective semiconductors.

cond-mat.mtrl-sci

Materials Informatics Across the Length Scales

Materials informatics is increasingly used to support modelling, analysis and design across the length scales of materials science, from atomistic simulations to microstructural characterisation and continuum descriptions. Despite rapid progress, the reliability and transferability of these approaches vary strongly with scale. Here we survey data-driven methods at the nanoscale, mesoscale, and micro-to-continuum levels, highlighting established capabilities as well as unresolved challenges. Machine-learning interatomic potentials, mesoscale surrogate and operator-learning models, and learning-based analysis of experimental microstructures are discussed, with emphasis on data quality, uncertainty, interpretability, and cross-scale consistency. We further examine the role of data standards, ontologies, and emerging tools, such as autonomous laboratories, where they directly affect multiscale workflows. This perspective clarifies what can be considered reliable today and identifies key obstacles to the broader integration of materials informatics across scales.

cond-mat.mtrl-sci

Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie

Artificial Intelligence is rapidly transforming materials science and engineering, offering powerful tools to navigate complexity, accelerate discovery, and optimize material design in ways previously unattainable. Driven by the accelerating pace of algorithmic advancements and increasing data availability, AI is becoming an essential competency for materials researchers. This review provides a comprehensive and structured overview of the current landscape, synthesizing recent advancements and methodologies for materials scientists seeking to effectively leverage these data-driven techniques. We survey the spectrum of machine learning approaches, from traditional algorithms to advanced deep learning architectures, including CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models such as Gaussian Processes for uncertainty quantification. The review also examines the pivotal role of data in this field, emphasizing how effective representation and featurization strategies, spanning compositional, structural, image-based, and language-inspired approaches, combined with appropriate preprocessing, fundamentally underpin the performance of machine learning models in materials research. Persistent challenges related to data quality, quantity, and standardization, which critically impact model development and application in materials science and engineering, are also addressed.

cond-mat.mtrl-sci

MAMBO: a lightweight ontology for multiscale materials and applications

Advancements of both computational and experimental tools have recently led to significant progress in the development of new advanced and functional materials, paralleled by a quick growth of the overall amount of data and information on materials. However, an effective unfolding of the potential of advanced and data-intensive methodologies requires systematic and efficient methods for the organization of knowledge in the context of materials research and development. Semantic technologies can support the structured and formal organization of knowledge, providing a platform for the integration and interoperability of data. In this work, we introduce the Materials and Molecules Basic Ontology (MAMBO), which aims at organizing knowledge in the field of computational and experimental workflows on molecular materials and related systems (nanomaterials, supramolecular systems, molecular aggregates, etc.). Linking recent efforts on ontologies for materials sciences in neighboring domains, MAMBO aims at filling gaps in current state-of-the-art knowledge modelling approaches for materials development and design targeting the intersection between the molecular scale and higher scale domains. With a focus on operational processes, lightweight, and modularity, MAMBO enables extensions to broader knowledge domains and integration of methodologies and workflows related to both computational and experimental tools. MAMBO is expected to advance the application of data-driven technologies to molecular materials, including predictive machine learning frameworks for materials design and discovery and automated platforms.

cond-mat.mtrl-sci

Molecular and Materials Basic Ontology: development and first steps

Advanced materials and their applications have become a key field of research, and it looks like this trend is not going to change soon. For that reason, the need for systematic and efficient methods for organizing knowledge in the field and conduct computational or experimental investigations is stronger than ever. In this work, we present a basic implementation of MAMBO - an ontology for molecular materials and their applications in real-life scenarios. The development of MAMBO has been guided by the needs of the research community involved in the development of novel materials with functional properties, with particular attention to the nanoscale. MAMBO aims at extending the current work in the field, while retaining a modular nature in order to allow straightforward extension of concepts and relations to neighboring domains. Our work is expected to enable the systematic integration of computational and experimental data in specific domains of interest (nanomaterials, molecular materials, organic an polymeric materials, supramolecular and bio-organic systems, etc.). Moreover, MAMBO is developed with a strong focus on the applications of data-driven frameworks for the design of novel materials with tailored characteristics.

cond-mat.mtrl-sci

Introducing MAMBO: Materials And Molecules Basic Ontology

Recent advances in computational and experimental technologies applied to the design and development of novel materials have brought out the need for systematic, rational and efficient methods for the organization of knowledge in the field. In this work, we present the initial steps carried out in the development of MAMBO - an ontology focused on the organization of concepts and knowledge in the field of materials based on molecules and targeted to applications. Our approach is guided by the needs of the communities involved in the development of novel molecular materials with functional properties at the nanoscale. As such, MAMBO aims at bridging the gaps of ongoing efforts in the development of ontologies in the materials science domain. By extending current work in the field, the modular nature of MAMBO also allows straightforward extension of concepts and relations to neighboring domains. Our work is expected to enable the systematic integration of computational and experimental data in specific domains of interest (nanomaterials, molecular materials, organic an polymeric materials, supramolecular and bio-organic systems, etc.). Moreover, MAMBO can be applied to the development of data-driven integrated predictive frameworks for the design of novel materials with tailored functional properties.

cond-mat.mtrl-sci

Predicting the properties of molecular materials: multiscale simulation workflows meet machine learning

Machine Learning tools are nowadays widely applied extensively to the prediction of the properties of molecular materials, using datasets extracted from high-throughput computational models. In several cases of scientific and technological relevance, the properties of molecular materials are related to the link between molecular structure and phenomena occurring across a wide set of spatial scales, from the nanoscale to the macroscale. Here, we describe an approach for predicting the properties of molecular aggregates based on multiscale simulations and machine learning.

cond-mat.mtrl-sci

Novel two-dimensional materials for electronics: computational modelling as a tool to enable the use of phosphorene in real applications

Two-dimensional and layered materials, such as graphene, have emerged in recent years for their potential use in several applications in technology, for example in electronics, bioelectronics, optoelectronics and related fields. Phosphorene, which can be considered as an analogous of graphene made of phosphorus atoms, is a relatively new two-dimensional system with peculiar structural, electronic, and mechanical properties. Despite the remarkable potential for applications, phosphorene suffers from severe limitations, hampering its use in practical uses. For example, phosphorene undergoes severe degradation phenomena in ordinary environments, leading to a very limited stability. Phosphorene is therefore very difficult to handle in real applications. In this paper, we briefly outline the concepts behind the application of predictive computational approaches to complex systems based on phosphorene, leading to the definition of practical strategies that can be applied to the development of stable, high-performance devices for electronics.

cond-mat.mtrl-sci

Early steps in the formation of the interface between organic molecular semiconductors and metals: a computational approach

A computational approach for predictive simulations of the nanoscale morphology in the early steps of the formation of the interface between metals and organic molecular semiconductors is presented. Despite the relevance of the metal-molecule junction for the development of electronic applications, structural details at the interface are often difficult to assess. Our approach, based on the integration of density functional theory with methods for the simulation of growth dynamics, allows to unravel the structural details of the formation of gold aggregates onto molecular materials. Simulations are applied to investigations of the initial steps in the formation of gold clusters at the interface with prototypical p-type and n-type organic molecular semiconductors. Results show a striking correlation between the morphology of the metal-organic interface, the details of the molecular structure and the peculiar metal-molecule interaction, also highlighting the role of fabrication conditions.

cond-mat.mtrl-sci

The Gauss map of a complete minimal surface with finite total curvature

In [15] Robert Osserman proved that the image of the Gauss map of a complete, non flat minimal surface in R^3 with finite total curvature miss at most 3 points. In this paper we prove that the Gauss map of such a minimal immersions omit at most 2 points. This is a sharp result since the Gauss map of the catenoid omits exactly two points. In fact we prove this result for a wider class of isometric immersions, that share the basic differential topological properties of the complete minimal surfaces of finite total curvature.

math.DG

Riemannian Hilbert manifolds

In this article we collect results obtained by the authors jointly with other authors and we discuss old and new ideas. In particular we discuss singularities of the exponential map, completeness and homogeneity for Riemannian Hilbert quotient manifolds. We also extend a Theorem due to Nomizu and Ozeki to infinite dimensional Riemannian Hilbert manifolds.

math.DG

Stability of the Focal and Geometric Index in semi-Riemannian Geometry via the Maslov Index

We investigate the problem of the stability of the number of conjugate or focal points (counted with multiplicity) along a semi-Riemannian geodesic $γ$. For a Riemannian or a non spacelike Lorentzian geodesic, such number is equal to the intersection number (Maslov index) of a continuous curve with a subvariety of codimension one of the Lagrangian Grassmannian of a symplectic space. Such intersection number is proven to be stable in a large variety of circumstances. In the general semi-Riemannian case, under suitable hypotheses this number is equal to an algebraic count of the multiplicities of the conjugate points, and it is related to the spectral properties of a non self-adjoint differential operator. This last relation gives a weak extension of the classical Morse Index Theorem in Riemannian and Lorentzian geometry. In this paper we reprove some results that were incorrectly stated by Helfer in a previous reference; in particular, a counterexample to one of Helfer's results, which is essential for the theory, is given. In the last part of the paper we discuss a general technique for the construction of examples and counterexamples in the index theory for semi-Riemannian metrics, in which some new phenomena appear.

math.DG