arXiv subjects

Filippo Federici Canova

Publications and source records attributed to Filippo Federici Canova.

6 recordsLinked to original sources

arXiv2026

Overcoming Labelled Data Scarcity for Defect Classification in Scanning Tunneling Microscopy

Scanning tunnelling microscopy (STM) is a powerful technique for imaging surfaces with atomic resolution, providing insight into physical and chemical processes at the level of single atoms and molecules. A regular task of STM image analysis is the identification and labelling of features of interest against a uniform background. Performing this manually is a labour-intensive task, requiring significant human effort. To reduce this burden, we propose an automated approach to the segmentation of STM images that uses both few-shot learning and unsupervised learning. Our technique offers greater flexibility compared to previous supervised methods; it removes the requirement for large manually annotated datasets and is thus easier to adapt to an unseen surface while still maintaining a high accuracy. We demonstrate the effectiveness of our approach by using it to recognise atomic features on three distinct surfaces: Si(001), Ge(001), and TiO$_2$(110), including adsorbed AsH$_3$ molecules on the silicon and germanium surfaces. Our model exhibits strong generalisation capabilities, and following initial training, can be adapted to unseen surfaces with as few as one additional labelled data point. This work is a significant step towards efficient and material-agnostic, automatic segmentation of STM images.

cond-mat.mtrl-sci↗

arXiv2026

Mesoscopic Modeling of Structure-Transport Relationships in Dense CNT Films Containing Amorphous Carbon

Carbon nanotube (CNT) films are widely considered as prospective building blocks for advanced electronic and nanostructured materials. In particular, electrical transport in high-density CNT films results from a complex interplay between network morphology and CNT connectivity, which remains challenging to characterize quantitatively. To identify the structural parameters that govern the electrical current in CNT films, we employed coarse-grained molecular dynamics to construct dense mesoscale CNT film models that include CNTs with different chiralities and lengths. The effects of CNT geometrical features on the film morphologies were quantified by devising a set of structural descriptors and analyzing their mutual correlations. The impact of varying the concentration of amorphous carbon (aC) inclusions on the film structure was assessed. Finally, we employed a nodal analysis framework to compute the electrical current across the networks and correlate the charge transport characteristics to the underlying structural descriptors. The current is found to be enhanced in films that exhibit high curvature and buckling, low bundling, and strong connectivity. We discuss how the presence of aC inclusions modifies these morphological and current characteristics. This work provides a mesoscale modeling framework for modeling structure-transport relationships in dense CNT films and highlights the role of morphological descriptors in guiding the interpretation of electrical transport in complex nanostructured networks.

physics.comp-ph↗

arXiv2025

Improving atomic force microscopy structure discovery via style-translation

Atomic force microscopy (AFM) is a key tool for characterising nanoscale structures, with functionalised tips now offering detailed images of the atomic structure. In parallel, AFM simulations using the particle probe model provide a cost-effective approach for rapid AFM image generation. Using state-of-the-art machine learning models and substantial simulated datasets, properties such as molecular structure, electrostatic potential, and molecular graph can be predicted from AFM images. However, transferring model performance from simulated to experimental AFM images poses challenges due to the subtle variations in real experimental data compared to the seemingly flawless simulations. In this study, we explore style translation to augment simulated images and improve the predictive performance of machine learning models in surface property analysis. We reduce the style gap between simulated and experimental AFM images and demonstrate the method's effectiveness in enhancing structure discovery models through local structural property distribution comparisons. This research presents a novel approach to improving the efficiency of machine learning models in the absence of labelled experimental data.

cond-mat.mtrl-sci↗

arXiv2021

Electrostatic Discovery Atomic Force Microscopy

While offering unprecedented resolution of atomic and electronic structure, Scanning Probe Microscopy techniques have found greater challenges in providing reliable electrostatic characterization at the same scale. In this work, we introduce Electrostatic Discovery Atomic Force Microscopy, a machine learning based method which provides immediate quantitative maps of the electrostatic potential directly from Atomic Force Microscopy images with functionalized tips. We apply this to characterize the electrostatic properties of a variety of molecular systems and compare directly to reference simulations, demonstrating good agreement. This approach opens the door to reliable atomic scale electrostatic maps on any system with minimal computational overhead.

cond-mat.mes-hall↗

arXiv2019

Automated Structure Discovery in Atomic Force Microscopy

Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules, due to difficulties with interpretation of highly distorted AFM images originating from non-planar molecules. Here we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular structure directly. We apply this methodology to resolve several distinct adsorption configurations of 1S-camphor on Cu(111) based on low-temperature AFM measurements. This approach will open the door to apply high-resolution AFM to a large variety of systems for which routine atomic and chemical structural resolution on the level of individual objects/molecules would be a major breakthrough.

physics.comp-ph↗

arXiv2019

DScribe: Library of Descriptors for Machine Learning in Materials Science

DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations. DScribe accelerates the application of machine learning for atomistic property prediction by providing user-friendly, off-the-shelf descriptor implementations. The package currently contains implementations for Coulomb matrix, Ewald sum matrix, sine matrix, Many-body Tensor Representation (MBTR), Atom-centered Symmetry Function (ACSF) and Smooth Overlap of Atomic Positions (SOAP). Usage of the package is illustrated for two different applications: formation energy prediction for solids and ionic charge prediction for atoms in organic molecules. The package is freely available under the open-source Apache License 2.0.

cond-mat.mtrl-sci↗