arXiv subjects

Pablo Pou

Publications and source records attributed to Pablo Pou.

9 recordsLinked to original sources

arXiv2026

Reconstruction-Dependent Imaging, Reactivity and Local Reduction of the CeO$_2$(100) surface

The possibility of mapping the local reactivity and reduction state to the atomic structure of chemically active oxide surfaces opens new avenues for further understanding of catalysis. Here, we combine scanning tunnelling (STM) and atomic force microscopy (AFM) with first-principles modelling to explore this possibility on the CeO2(100) surface. While STM reveals the periodicity of cerium-terminated and oxygen-terminated CeO$_2$(100) reconstructions coexisting on the same surface, AFM imaging and force spectroscopy provide direct identification of the exposed atomic species and their reactivity as the chemical interaction with the probe. Density functional theory based STM and AFM simulations reproduce the main experimental observations and show that STM contrast cannot be in general assigned to the atomic positions of certain chemical species, as traditionally assumed from previous studies. Simulated STM contrast of the two reconstructions across different reduction states associated with the removal of oxygen atoms in deeper layers, evidence that STM alone does not offer a robust fingerprint of the local reduction state for the cerium-terminated reconstruction, but it is sensitive to the reduced state in the case of the oxygen-terminated one, being able to provide information on a mixed distribution of Ce$^{3+}$ and Ce$^{4+}$ ions on the first sub-surface Ce layer.

cond-mat.mtrl-sci↗

arXiv2026

Mapping the Growth of Two-Dimensional $\pi$-Conjugated Polymers on Au(111): Organometallic Intermediates and Edge Terminations

Kagome lattices provide an exciting space for the exploration of graphene-like $\pi$-conjugated molecular systems with flat bands. Using heterotriangulene-derived precursors, along with an on-surface Ullmann coupling process, makes growing polymers with Kagome lattices accessible and straightforward. Here, we use scanning tunneling microscopy alongside high-resolution atomic force microscopy to examine the evolution of tribromotrioxaazatriangulene on Au(111) into ordered, covalent films. Using density functional theory and scanning probe methods, we find previously unreported organometallic intermediate states involving Au adatoms incorporated within the growing polymer lattice. We also find that a majority of polymer edges remain brominated up to 250 $^{\circ}$C and a large number of edges bonded to Au adatoms coordinated to an adjacent bromine atom. These observations suggest that residual bromine could play a role in stabilizing the polymer edges to Au adatoms and thereby influence the growth pathways that lead to ordered Kagome polymer lattices.

cond-mat.mtrl-sci↗

arXiv2025

Bond-resolved STM with density-based methods

Bond-resolved STM (BRSTM) is a recent technique that combines the advantages of scanning tunneling microscopy (STM) with the outstanding intramolecular resolution provided by non-contact atomic force microscopy (ncAFM) using a CO-functionalized tips, offering unique insights into molecular interactions at surfaces. In this work, we present a novel and easily implementable approach for simulating BRSTM images, which we have applied to reproduce new experimental BRSTM data of Perylene-3,4,9,10-tetracarboxylic dianhydride (PTCDA) on Ag(111), obtained with unprecedented control of tip-sample separation ($\sim$10~pm). Our method integrates the Full-Density-Based Model (FDBM) developed for High-Resolution Atomic Force Microscopy (HRAFM) with Chen's derivative approximation for tunneling channels, effectively capturing the contributions of both $\sigma$ and $\pi$ channels, while accounting for the CO-tip deflection induced by probe-sample interactions. This approach accurately reproduces the experimental results for both PTCDA/Ag(111) and 1,5,9-trioxo-13-azatriangulene (TOAT)/Cu(111) systems, including intricate tip-sample distance-dependent features. Furthermore, we also demonstrate the important role of substrate-induced effects, which can modify molecular orbital occupation and the relaxation of the CO probe, resulting in distinct BRSTM image characteristics.

cond-mat.mtrl-sci↗

arXiv2025

Resolving the Structural Duality of Graphene Grain Boundaries

Grain boundaries (GBs) are ubiquitous in large-scale graphene samples, playing a crucial role in their overall performance. Due to their complexity, they are usually investigated as model structures, under the assumption of a fully relaxed interface. Here, we present cantilever-based non-contact atomic force microscopy (ncAFM) as a suitable technique to resolve, atom by atom, the complete structure of these linear defects. Our experimental findings reveal a richer scenario than expected, with the coexistence of energetically stable and metastable graphene GBs. Although both GBs are structurally composed of pentagonal and heptagonal like rings, they can be differentiated by the irregular geometric shapes present in the metastable boundaries. Theoretical modeling and simulated ncAFM images, accounting for the experimental data, show that metastable GBs form under compressive uniaxial strain and exhibit vertical corrugation, whereas stable GBs remain in a fully relaxed, flat configuration. By locally introducing energy with the AFM tip, we show the possibility to manipulate the metastable GBs, driving them toward their minimum energy configuration. Notably, our high-resolution ncAFM images reveal a clear dichotomy: while the structural distortions of metastable grain boundaries are confined to just a few atoms, their impact on graphene's properties extends over significantly larger length scales.

cond-mat.mtrl-sci↗

arXiv2025

Near-surface Defects Break Symmetry in Water Adsorption on CeO$_{2-x}$(111)

Water interactions with oxygen-deficient cerium dioxide (CeO$_2$) surfaces are central to hydrogen production and catalytic redox reactions, but the atomic-scale details of how defects influence adsorption and reactivity remain elusive. Here, we unveil how water adsorbs on partially reduced CeO$_{2-x}$(111) using atomic force microscopy (AFM) with chemically sensitive, oxygen-terminated probes, combined with first-principles calculations. Our AFM imaging reveals water molecules as sharp, asymmetric boomerang-like features radically departing from the symmetric triangular motifs previously attributed to molecular water. Strikingly, these features localize near subsurface defects. While the experiments are carried out at cryogenic temperature, water was dosed at room temperature, capturing configurations relevant to initial adsorption events in catalytic processes. Density functional theory identifies Ce$^{3+}$ sites adjacent to subsurface vacancies as the thermodynamically favored adsorption sites, where defect-induced symmetry breaking governs water orientation. Force spectroscopy and simulations further distinguish Ce$^{3+}$ from Ce$^{4+}$ centers through their unique interaction signatures. By resolving how subsurface defects control water adsorption at the atomic scale, this work demonstrates the power of chemically selective AFM for probing site-specific reactivity in oxide catalysts, laying the groundwork for direct investigations of complex systems such as single-atom catalysts, metal-support interfaces, and defect-engineered oxides.

cond-mat.mtrl-sci↗

arXiv2024

Molecular Identification via Molecular Fingerprint extraction from Atomic Force Microscopy images

Non--Contact Atomic Force Microscopy with CO--functionalized metal tips (referred to as HR-AFM) provides access to the internal structure of individual molecules adsorbed on a surface with totally unprecedented resolution. Previous works have shown that deep learning (DL) models can retrieve the chemical and structural information encoded in a 3D stack of constant-height HR--AFM images, leading to molecular identification. In this work, we overcome their limitations by using a well-established description of the molecular structure in terms of topological fingerprints, the 1024--bit Extended Connectivity Chemical Fingerprints of radius 2 (ECFP4), that were developed for substructure and similarity searching. ECFPs provide local structural information of the molecule, each bit correlating with a particular substructure within the molecule. Our DL model is able to extract this optimized structural descriptor from the 3D HR--AFM stacks and use it, through virtual screening, to identify molecules from their predicted ECFP4 with a retrieval accuracy on theoretical images of 95.4\%. Furthermore, this approach, unlike previous DL models, assigns a confidence score, the Tanimoto similarity, to each of the candidate molecules, thus providing information on the reliability of the identification. By construction, the number of times a certain substructure is present in the molecule is lost during the hashing process, necessary to make them useful for machine learning applications. We show that it is possible to complement the fingerprint-based virtual screening with global information provided by another DL model that predicts from the same HR--AFM stacks the chemical formula, boosting the identification accuracy up to a 97.6\%. Finally, we perform a limited test with experimental images, obtaining promising results towards the application of this pipeline under real conditions

cond-mat.mtrl-sci↗

arXiv2022

Molecular Identification from AFM images using the IUPAC Nomenclature and Attribute Multimodal Recurrent Neural Networks

Despite being the main tool to visualize molecules at the atomic scale, AFM with CO-functionalized metal tips is unable to chemically identify the observed molecules. Here we present a strategy to address this challenging task using deep learning techniques. Instead of identifying a finite number of molecules following a traditional classification approach, we define the molecular identification as an image captioning problem. We design an architecture, composed of two multimodal recurrent neural networks, capable of identifying the structure and composition of an unknown molecule using a 3D-AFM image stack as input. The neural network is trained to provide the name of each molecule according to the IUPAC nomenclature rules. To train and test this algorithm we use the novel QUAM-AFM dataset, which contains almost 700,000 molecules and 165 million AFM images. The accuracy of the predictions is remarkable, achieving a high score quantified by the cumulative BLEU 4-gram, a common metric in language recognition studies.

cond-mat.mtrl-sci↗

arXiv2018

High-accuracy large-scale DFT calculations using localized orbitals in complex electronic systems: The case of graphene-metal interfaces

Over many years, computational simulations based on Density Functional Theory (DFT) have been used extensively to study many different materials at the atomic scale. However, its application is restricted by system size, leaving a number of interesting systems without a high-accuracy quantum description. In this work, we calculate the electronic and structural properties of a graphene-metal system significantly larger than in previous plane-wave calculations with the same accuracy. For this task we use a localised basis set with the \textsc{Conquest} code, both in their primitive, pseudo-atomic orbital form, and using a recent multi-site approach. This multi-site scheme allows us to maintain accuracy while saving computational time and memory requirements, even in our exemplar complex system of graphene grown on Rh(111) with and without intercalated atomic oxygen. This system offers a rich scenario that will serve as a benchmark, demonstrating that highly accurate simulations in cells with over 3000 atoms are feasible with modest computational resources.

cond-mat.mes-hall↗

arXiv2001

Electron correlation effects and ferromagnetism in Iron

Electron correlation effects in Fe are analyzed using a first principles LCAO-scheme. In our approach, we first use a local orbital DFT-LDA solution to introduce a Hubbard Hamiltonian without fitting parameters. In a second step, we introduce a many-body solution to this Hamiltonian using a DMF approximation. Our analysis shows that magnetism in Fe is an effect associated with the first atomic Hund's rule. Moreover, we also find important correlation effects in the Fe-spin polarized DOS. The photoemision spectra is explained using a value of U_eff as large as 4 eV, provided the satellite peaks appearing around 3-5 eV below the Fermi energy are interpreted appropriately.

cond-mat.str-el↗