Ref HAL: hal-05391292_v1
DOI: 10.1101/2025.03.21.644586
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While island biogeography focuses on species richness equilibrium driven by immigration and extinction, and metapopulation theory examines single-species dynamics across fragmented habitats, their interplay remains poorly understood. In particular, the species-area relationship remains a subject of ongoing debate, yet there are limited theoretical foundations to explain it. To address this, we developed a multiscale stochastic metapopulation model to investigate diversity patterns on islands, bridging the gap between island biogeography and metapopulation theory. Our model integrates regional colonization from a mainland with local colonization-extinction processes within islands at the single-species level, then extends this to multiple, independent species. By analyzing the stationary properties of this model, we generate novel predictions of Species Area Relationship (SAR) based on local extinction rates, within-island colonization rates, and mainland immigration rates. We demonstrate how the interplay of these parameters influences the relationship, predicting patterns that can resemble either the power-law of Arrhenius or the semi-logarithmic relationship of Gleason, depending on the relative importance of mainland immigration versus within-island dynamics, and on the nature of the species abundance distribution in the mainland. This unified framework offers new insights into the mechanisms driving species richness and distribution across spatial scales, providing a more holistic understanding of biodiversity patterns in fragmented landscapes.

Ref HAL: hal-05373131_v1
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Stacking 2D materials into van der Waals heterostructures implies the emergence of interlayer interactions, modifying electronic scattering in a given layer. We develop an efficient ab initio approach to compute the electrodynamic response of 2D heterostructures, including long range Coulomb interactions between electrons, polar phonons, and plasmons. We extract electron scattering from this model and solve a system of coupled Boltzmann transport equations for electrons and electrodynamic modes. This accounts for non-trivial momentum exchanges such as the so-called phonon drag. We study the effects of remote electron-plasmon-phonon couplings on the electronic transport properties of 2D materials. We also show that a dynamical treatment of electronic screening has a significant impact on the mobility of doped semiconductors with a Fermi level around the band edge. Indeed, polar phonons dynamically coupled with electron-hole excitations scatter momentum differently than the standard polar phonons, whether bare or statically screened.

Ref HAL: hal-05370132_v1
Ref Arxiv: 2409.11834
DOI: 10.1103/PhysRevResearch.7.013290
Ref. & Cit.: NASA ADS
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Superconductivity in few-layer semiconducting transition-metal dichalcogenides (TMDs) can be induced by field-effect doping through ionic-liquid gating. While several experimental observations have been collected over the years, a fully consistent theoretical picture is still missing. Here we develop a realistic framework that combines the predictive power of first-principles simulations with the versatility and insight of Bardeen-Cooper-Schrieffer gap equations to rationalize such experiments. The multivalley nature of semiconducting TMDs is taken into account, together with the doping- and momentum-dependent electron-phonon and Coulomb interactions. Consistently with experiments, we find that superconductivity occurs when the electron density is large enough that the Q valleys get occupied, as a result of a large enhancement of electron-phonon interactions. Despite being phonon driven, the superconducting state is predicted to be sensitive to Coulomb interactions, which can lead to the appearance of a relative sign difference between valleys and thus to a s + − character. We discuss qualitatively how such scenario may account for many of the observed physical phenomena for which no microscopic explanation has been found so far, including in particular the presence of a large subgap density of states, and the sample-dependent dome-shaped dependence of T c on accumulated electron density. Our results provide a comprehensive analysis of gate-induced superconductivity in semiconducting TMDs, and introduce an approach that will likely be valuable for other multivalley electronic systems, in which superconductivity occurs at relatively low electron density.

Ref HAL: hal-05370159_v1
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2D materials have shown fascinating fundamental physics and exciting technological prospects. Many applications rely on the ability to conduct electrons efficiently. At room temperature, this is mostly limited by the scattering of electrons by phonons. A clear understanding of transport mechanisms, along with predictive ab initio simulations, are then key to design performant and energy-efficient devices.The prototypical 2D transport setup is an electrostatically-doped semiconducting layer encapsulated by a dielectric material. We will discuss 3 major challenges to model electron-phonon scattering in this system: dimensionality, electrostatic doping and interlayer couplings. The first 2 concern the proper simulation of electron-phonon interactions in single 2D layers. The third one accounts for the additional scattering mechanisms between layers in van der Waals heterostructures. Combining solutions to all 3 challenges paves the way towards a full description of electron-phonon scattering and electronic transport in 2D heterostructures.Reduced dimensionality has strong, universal impact on all materials, notably via changes in the long wavelength behaviour of the Fröhlich interaction. Electrostatic doping refers the ubiquitous use of field effects to induce charges in 2D materials and tune their Fermi level. The added carriers screen electron-phonon scattering. We will see how to include this effect in doped semiconductors. Usually, it tends to decrease scattering, but we will also discuss a counter-intuitive enhancement mechanism in multi-valley materials like transition-metal dichalcogenides. Interlayer couplings refers the remote interactions between electrons and phonons in different layers. They are very too costly to simulate directly in first-principles codes due to the size of the associated supercells. We will discuss a newly developed model to compute long-range Coulomb interactions between electrons and polar excitations including phonons and plasmons in van der Waals heterostructures, at a reasonable relatively negligible computational cost.

Ref HAL: hal-05050636_v1
DOI: 10.1016/j.ecss.2025.109261
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Macrophytes are a key component of lagoon ecosystems, and the spatio-temporal variation of their biomass reflects the effect of environmental drivers. Understandingthese dynamics is essential for managing, preserving and anticipating the fate of such fragile ecosystems. First, using both satellite data and field observations,we showcase here a machine learning approach to accurately detect submerged vegetation in a preserved lagoon in South France, the Bagnas lagoon. Second,we investigate the statistical relationship between the surface area of Stuckenia pectinata and abiotic factors known to influence the life history of this aquatic plant.We thus identify and evaluate the factors that most impact Stuckenia pectinata population dynamics within the lagoon. We find that water temperature, salinity, anddepth are significantly related to submerged aquatic vegetation (SAV) coverage. We show that the SAV surface area reaches a peak during summer, which can varyacross years. Furthermore, we examine the lifetime distribution of SAV during each year cycle across the pond. We show that the average annual lifespan differssignificantly from one year to the next, which can be explained by annual differences in exposure time to sufficiently high water levels.

Ref HAL: hal-04931333_v1
DOI: 10.1038/s41586-024-08514-6
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Ref HAL: hal-04344224_v1
Ref Arxiv: 2311.14752
Ref INSPIRE: 2726911
DOI: 10.1038/s42254-024-00791-4
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Unraveling the connections between microscopic structure, emergent physical properties, and slow dynamics has long been a challenge in the field of the glass transition. The absence of clear visible structural order in amorphous configurations complicates the identification of the key features related to structural relaxation and transport properties. The difficulty in sampling equilibrated configurations at low temperatures hampers thorough numerical and theoretical investigations. This roadmap article explores the potential of machine learning (ML) techniques to face these challenges, building on the algorithms that have revolutionized computer vision and image recognition. We present successful ML applications, as well as many open problems for the future, such as transferability and interpretability of ML approaches. We highlight new ideas and directions in which ML could provide breakthroughs to better understand glassy liquids. To foster a collaborative community effort, the article introduces the "GlassBench" dataset, providing simulation data and benchmarks for both two-dimensional and three-dimensional glass-formers. Emphasizing the importance of benchmarks, we identify critical metrics for comparing the performance of emerging ML methodologies, in line with benchmarking practices in image and text recognition. The goal of this roadmap is to provide guidelines for the development of ML techniques in systems displaying slow dynamics, while inspiring new directions to improve our understanding of glassy liquids.