arXiv subjects

Manuel González Lastre

Publications and source records attributed to Manuel González Lastre.

6 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 $π$-Conjugated Polymers on Au(111): Organometallic Intermediates and Edge Terminations

Kagome lattices provide an exciting space for the exploration of graphene-like $π$-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↗

arXiv2026

MAD-SURF: a machine learning interatomic potential for molecular adsorption on coinage metal surfaces

Predicting how organic molecules adsorb, assemble, and interact on metal surfaces is central to surface chemistry and molecular electronics, particularly in the context of interpreting high-resolution scanning probe microscopy. Yet, the application of first-principles simulations to interfaces is hampered by the computational cost for evaluating the electronic structure for the large number of atoms typically involved. We hereby present MAD-SURF, a machine learning interatomic potential specifically tailored for molecular adsorption on coinage metal surfaces. Trained on a broad dataset spanning diverse molecules, adsorption motifs, surfaces, molecular dynamics trajectories and non-covalent aggregates, MAD-SURF achieves accuracy comparable to the underlying DFT reference while enabling simulations orders of magnitude faster than density functional theory. The model reliably reproduces energies, forces and adsorption geometries across the three coinage metal substrates. We demonstrate its capabilities on experimentally characterized systems, including organic monolayers, polycyclic aggregates, flexible biomolecules and the long-range herringbone reconstruction of gold. By merging accuracy, speed, and generalizability, MAD-SURF offers a practical framework for accelerating atomistic simulations and advancing data-driven workflows in surface science.

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↗

arXiv2023

Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks

We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum correlations in discrete photon-counting data characterized by quantum jumps. We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. By using numerical experiments on a two-level quantum system, we demonstrate that our approach can achieve a similar optimal performance as Bayesian inference, while drastically reducing computational costs. Additionally, the method exhibits robustness against the presence of imperfections in both measurement and training data. This approach offers a promising and computationally efficient tool for quantum parameter estimation with photon-counting data, relevant for applications such as quantum sensing or quantum imaging, as well as robust calibration tasks in laboratory-based settings.

quant-ph↗