arXiv subjects

Rubén Pérez

Publications and source records attributed to Rubén Pérez.

13 recordsLinked to original sources

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

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

Atomic Force Microscopy Simulations for CO-functionalized tips with Deep Learning

Atomic Force Microscopy (AFM) operating in the frequency modulation mode with a metal tip functionalized with a CO molecule images the internal structure of molecules with an unprecedented resolution. The interpretation of these images is often difficult, making the support of theoretical simulations important. Current simulation methods, particularly the most accurate ones, require expertise and resources to perform ab initio calculations for the necessary inputs (i.e charge density and electrostatic potential of the molecule). Here, we propose an efficient and simple alternative to simulate these AFM images based on a Conditional Generative Adversarial Network (CGAN), that avoids all force calculations, and uses as the only input a 2D ball--and--stick depiction of the molecule. We discuss the performance of the model when optimized using different training subsets. Our CGAN reproduces accurately the intramolecular contrast observed in the simulated images for quasi--planar molecules, but has significant limitations for molecules with a significant internal torsion, due to the strictly 2D character of the input.

cond-mat.mtrl-sci↗

arXiv2022

Force-dependent elasticity of nucleic acids

The functioning of double-stranded (ds) nucleic acids (NAs) in cellular processes is strongly mediated by their elastic response. These processes involve proteins that interact with dsDNA or dsRNA and distort their structures. The perturbation of the elasticity of NAs arising from these deformations is not properly considered by most theoretical frameworks. In this work, we introduce a novel method to assess the impact of mechanical stress on the elastic response of dsDNA and dsRNA through the analysis of the fluctuations of the double helix. Application of this approach to atomistic simulations reveals qualitative differences in the force dependence of the mechanical properties of dsDNA with respect to those of dsRNA, which we relate to structural features of these molecules by means of physically-sound minimalistic models.

physics.bio-ph↗

arXiv2022

Molecular Identification with Atomic Force Microscopy and Conditional Generative Adversarial Networks

Frequency modulation (FM) Atomic Force Microscopy (AFM) with metal tips functionalized with a CO molecule at the tip apex has provided access to the internal structure of molecules with totally unprecedented resolution. We propose a model to extract the chemical information from those AFM images in order to achieve a complete identification of the imaged molecule. Our Conditional Generative Adversarial Network (CGAN) converts a stack of AFM images at various tip-sample distances into a ball-and-stick depiction, where balls of different color and size represent the chemical species and sticks represent the bonds, providing complete information on the structure and chemical composition. The CGAN has been trained and tested with the QUAM-AFM data set, that contains simulated AFM images for a collection of 686,000 molecules that include all the chemical species relevant in organic chemistry. Tests with a large set of theoretical images and few experimental examples demonstrate the accuracy and potential of our approach for molecular identification.

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↗

arXiv2017

Platinum atomic contacts: from tunneling to contact

We present a theoretical study of the electronic transport through Pt nanocontacts. We show that the analysis of the tunnelling regime requires a very careful treatment of the technical details. For instance, an insufficient size of the system can cause unphysical charge oscillations to arise along the transport direction; moreover, the use of an inappropriate basis set can deviate the distance dependence of the conductance from the expected exponential trend. While the conductance decay can be either corrected by employing ghost atoms or a large-cutoff-radius basis set, the same does not apply to the corrugation, for which only the second option is recommended. Interestingly, these details were not found to have a remarkable impact in the contact regime. These findings are important for theoretical studies of distance-dependent phenomena in scanning-probe and break-junction experiments.

cond-mat.mes-hall↗

arXiv2016

Tailoring the thermal expansion of graphene via controlled defect creation

Contrary to most materials, graphene exhibits a negative thermal expansion coefficient (TEC), i.e it contracts when heated. This contraction is due to the thermal excitation of low energy out-of-plane vibration modes. These flexural modes have been reported to govern the electronic transport and the mechanical response of suspended graphene. In this work, we systematically investigate the influence of defects in the TEC of suspended graphene membranes. Controlled introduction of low densities of mono-vacancies reduces the graphene TEC, up to one order of magnitude for a defect density of $5\times10^{12}$~cm$^{-2}$ . Our molecular dynamics simulations reproduce the observed trend and show that TEC reduction is due to the suppression of out-of-plane fluctuations caused by the strain fields created by mono-vacancies in their surrounding areas. These results highlight the key role of defects in the properties of "real-life" graphene, and pave the way for future proposals of electronic and mechanical defect engineering.

cond-mat.mtrl-sci↗

arXiv2002

First-principles Simulations of the stretching and final breaking of Al nanowires: Mechanical properties and electrical conductance

The evolution of the structure and conductance of an Al nanowire subject to a tensile stress has been studied by first-principles total-energy simulations. Our calculations show the correlation between discontinuous changes in the force (associated to changes in the bonding structure of the nanowire) and abrupt modifications of the conductance as the nanowire develops a thinner neck, in agreement with the experiments. We reproduce the characteristic increase of the conductance in the last plateau, reaching a value close to the conductance quantum $G_0 = 2 e^2 / h$ before the breaking of the nanowire. A dimer defines the contact geometry at these last stages, with three channels (one dominant) contributing to the conductance.

cond-mat.mes-hall↗