Abstract:
The computational activity proposed takes place in the context of the ERC Consolidator project Integrated System Analysis of Urban Vegetation and agriculture(URBAG, 2019-2024). Given the need of cities to increment green areas and local agriculture to promote urban sustainability, URBAG aims to provide the knowledge and tools to evaluate which combinations of agriculture and green spaces result in the best performance in terms of air quality, heat wave and climate change mitigation, as well as ecosystem services provided to urban dwellers. To do so, URBAG uses, develops, and improves atmospheric models at the urban scale with the aim to evaluate 1) the efficacy of green areas on heat wave episodes, 2) how land-use changes affect carbon budget and 3) improve the execution time of atmospheric simulations.

Abstract:
This project involves obtaining the stress tensor and transport/ elastic properties of warm dense matter. This is of interest to understand the crust of neutron stars and coalescence of binary neutron star mergers. We incorporate GR and relativistic effects. These properties also determine the gravitational wave emission in perturbations or modes of oscillation in individual objects or ejecta properties and light curves in kilonova events following the merger. We also consider superfluid states of nucleon matter arising at very cold and dense systems. Our codes involve realistic particle interaction to compute the microscopic dynamics at finite temperature and yet the possible admixture with a new type of matter, the dark matter, giving an imprint on present and future astrophysical multimessenger measurements .

Abstract:
Gravitational-wave (GW) astronomy relies on accurate and efficient models for the signals detected by ground-based detectors. However, in the most generic case no fully generic GW models for these binary black hole (BBH) mergers yet exist, due to a lack of numerical relativity (NR) simulations.This project aims to demonstrate the capability of the Spanish Supercomputing Network (RES) to perform NR simulations of generic BBHs using the Spectral Einstein Code, which is one of the most accurate NR codes and has produced the largest public catalog up to date. This project will serve as a testbed for upcoming larger computing proposals within the context of a Beatriz de Galindo research program, which pursues to generate larger sets of NR simulations in order to address the open problem of modeling the GW signal emitted by generic BBHs.

Abstract:
The fourth observing run of the LIGO-Virgo-KAGRA network offers unprecedented distance reach and event rates for studying gravitational waves from compact binary coalescences. This also allows finding promising candidates for the never-before detected gravitational lensing of gravitational waves by massive objects in the universe. This can produce both multiple images of the same source or complex waveform deformations. We use RES supercomputers to study such candidates in-depth with massively parallel Bayesian parameter estimation and the latest signal models, in addition to tests on realistic simulated signals. Such detections will provide a new probe of the cosmos, in particular for measuring the cosmic expansion rate, testing the nature of gravity, and studying populations of exotic compact objects.

Abstract:
Around 100 gravitational wave (GW) detections have been made to date with the ground-based LVK detector network. Accurate measurements of the properties of the binaries producing these signals are crucial for astrophysical inferences (such as population studies and binary formation mechanisms) and for cosmological studies (such as the measurement of the Hubble constant). Precessing binaries, where the spins of the black holes are not aligned with the orbital angular momentum of the binary, are of particular interest. Hints of a population of such systems have already been identified. We propose an assessment of the reliability and accuracy with which current GW signal models and data analysis techniques can ascertain the properties of these systems and the improvements required to meet the challenges posed by current and future detectors.

Abstract:
This project focuses on optimizing the detection of eccentric and precessing binary black holes (BBHs) using the unmodeled gravitational wave (GW) search pipeline, pycWB. Building on expertise and resources from prior RES projects, we aim to enhance the pipeline’s sensitivity and explore advanced clustering algorithms to capture multi-burst signatures of eccentric BBHs. Detecting such systems is crucial for understanding BBH formation channels and orbital dynamics, offering insights into GW astronomy. This study also prepares pycWB for integration into LIGO’s standard pipeline and future observatories like LISA and the Einstein Telescope.

Abstract:
With the LIGO-Virgo network operating through its fourth observing run, the number of gravitational-wave detections is bound to grow at a steady pace. Gravitational waveform models are a key element in the analysis of compact binary mergers. In this project, we will thoroughly test several advancements in the field of gravitational waveform modelling that will allow us to reach a better understanding of the properties of coalescing binaries. We will focus on three main aspects that can leave characteristic imprints on gravitational-wave signals: eccentricity, gravitational-wave memory and extreme matter effects. Our new models combine accuracy and efficiency to fully reap the potential of future observations.

Abstract:
This Activity takes place within project entitled “Studying the genetic mechanisms of antibiotic resistance in enterococci” funded by a “Consolidación Investigadora 2023” grant (grant reference: CNS2023-144312) awarded by the Agencia Estatal de Investigación. The aim of this project is to establish a complete concordance between phenotypic and genotypic antibiotic resistance in enterococci, building on previous findings (DOI: 10.1016/S2666-5247(23)00297-5), with the goal of enabling accurate antibiotic resistance detection from enterococci genomes for diagnosis and surveillance purposes. Large supercomputing resources are required to analyse a collection of up to 7,000 enterococcal genomes, including large volumes of small jobs and large population-based analyses requiring thousands of CPU hours and large RAM requirements.

Abstract:
Are there some general principles that developmental networks need to fulfill in order to be able to lead to the development of complex robust morphologies? To answer this, we will simulated morphological evolution using complexity as selection criterion. Evolution will be studied by integrating a realistic computational model of development, with a population genetics model that considers reproduction, mutation, genetic recombination and evaluates fitness of morphologies. Then morphological transitions will be traced and the underlying changes in developmental mechanisms analyzed. We expect to identify how developmental mechanisms (changes in the topology, interaction strengths, and regulated cellular behaviors of gene networks) affect adaptive dynamics in the in silico evolution experiments and lead to complex and robust morphologies.

Abstract:
Vortices are one of the most characteristic structures present in fluid flows. Much attention has been given to how vortex tubes, rings or sheets interact with each other or with solid boundaries. However, the purposeful breaking or “cutting” of topological vortex lines with solid objects has seen less work due to experimental and numerical challenges. In this project, we use direct numerical simulations to analyze how the impact of objects on vortices affects their behavior. Specifically, we focus on analyzing the transition to the "bending" regime of body-vortex interaction. By using objects of different sizes and shapes to impact a vortex, we analyze how the vortex bends and distorts in response to the presence of the body, and rationalize when and how the size of the body becomes important during a body-vortex interaction.

Abstract:
This project proposes the development of a Retrieval Augmented Generation (RAG) system to enable natural language access to the INSPIRE-HEP database of High Energy Physics. The system will allow users to "talk" with INSPIRE, asking questions and receiving answers in natural language, effectively turning INSPIRE into a vast, accessible knowledge base. The project aims to explore the potential of AI technologies, particularly large language models, in the context of High Energy Physics, and to create new AI models specialized in this domain.

Abstract:
In this activity we aim to refine our new implementation to solve the Bethe-Salpeter equation in materials employing Gaussian atom-centred basis functions, and use it to compute excitonic spectra and optical properties (absorption and non-linear responses) in systems beyond those described by the Rytova-Keldysh potential, i.e. single layers. Our methodology is based on the density fitting technique in conjunction with Ewald summations to solve the conditional convergence of the emergent Coulomb series in solids. This approach is suitable to any kind of non-metallic material, and it may hopefully provide accurate enough spectra to describe non-linear optical tensors, such as the second-harmonic or the bulk photovoltaic effect. The developments will be released as part of the open-source code XATU.

Abstract:
Hydrates are nonstoichiometric inclusion solid compounds in which molecules, such as nitrogen (N2), hydrogen (H2), and tetrahydrofuran (THF), are enclathrated in the voids left by a periodic network of water molecules. Hydrates are regarded as strategic materials from environmental, energy, and economic perspectives. The stability of hydrates is crucial to these applications and can be enhanced using hydrate thermodynamic promoters, such as THF. We determine the hydrate-water interfacial energy of CO2 and H2 hydrates, as well as hydroquinone clathrates of H2, analyze the effect of multiple occupation in N2 and H2 hydrates on thermodynamic properties and estimate nucleation rates of THF hydrate from kinetic and thermodynamic routes. All predictions obtained in this work will be compared with experimental data taken from the literature.

Abstract:
In this project, we perform extremely efficient ab initio simulations in order to obtain a remarkably accurate description of the diffusion process of the proton and hydroxide ions in heavy and light water. This is crucial for understanding a huge number of scientific problems, and for developing and improving many technological and industrial applications, in particular for materials for energy generation and storage in many domains (from electrochemistry to nuclear technology).In addition, the data generated will be used as extremely reliable reference data for the training of machine learning potentials, further extending the number and depth of the possible applications.

Abstract:
A highly active and selective catalyst for low temperature CO2 methanation consisting of a combination of metallic ruthenium (Ru0), ruthenium oxide (RuO2) and a ruthenium oxycarbonate phase (RuOxCy) formed by interstitial carbon doped into RuO2 has been recently described. To maintain its long-term catalytic performance it is key to understand the reaction mechanism and the competing processes leading to catalyst deactivation, such as reduction of RuO2 to form Ru0 nanoparticles. In this project we use periodic DFT calculations to investigate the mechanism of H2 dissociation and CO2 hydrogenation to methane on different models including Ru0 NPs, RuO2 and RuOxCy phase, in order to identify the role of each phase on the catalyst activity and stability.

Abstract:
An alternative catalytic route to the energy-intensive steam reforming of ethane to produce ethylene is investigated in this project. The process consists of two steps, a partial oxidation of ethane to ethanol followed by ethanol dehydration to ethylene. The proposed catalysts are Cu-exchanged zeolites containing both Cu cations acting as redox active sites for the oxidation step and Brønsted acid sites that promote the dehydration step. The periodic DFT study of the reaction mechanism on different catalyst models will allow to predict the relative concentration and distribution of redox and acid sites that maximizes the ethane conversion and the selectivity to ethylene, facilitating the synthesis of optimized catalyst for an efficient transformation of ethane into ethylene.

Abstract:
This project investigates the catalytic properties of molybdate (AMoO₄) and tungstate (AWO₄) nanostructures, focusing on their ability to generate reactive oxygen species (ROS) and perform selective oxidation. Their wide bandgaps promote efficient charge separation, enabling ROS production under light and dark conditions, making them versatile for environmental and biomedical applications. Structural and electronic tuning, including defect engineering, enhances reactivity by stabilizing polarons and modifying surface properties. Advanced density functional theory (DFT) methods are employed to explore the impact of oxygen vacancies on electronic structures and adsorption dynamics, providing mechanistic insights that drive rational catalyst design.