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Within the context of a Chern-Simons running-vacuum-model (RVM) cosmology, one expects an early-matter dominated (eMD) reheating period after RVM inflation driven by the axion field. Treating thus in this work Chern-Simons RVM cosmology as an effective $f(R)$ gravity theory characterized by logarithmic corrections of the spacetime curvature, we study the gravitational-wave (GW) signal induced by the nearly-scale invariant inflationary adiabatic curvature perturbations during the transition from the eMD era driven by the axion to the late radiation-dominated era. Remarkably, by accounting for the extra GW scalaron polarization present within $f(R)$ gravity theories, we find regions in the parameter space of the theory where one is met with a distinctive induced GW signal with a universal $f^6$ high-frequency scaling compared to the $f^7$ scaling present in general relativity (GR). Interestingly enough, for axion masses $m_a$ higher than 1 GeV and axion gauge couplings $f_a$ above $10^{-3}$ Planck mass, one can produce induced GW spectra within the sensitivity bands of future GW observatories such as the Einstein Telescope (ET), the Laser Interferometer Space Antenna (LISA), the Big Bang Observer (BBO) and the Square Kilometer Arrays (SKA)., Comment: 17 pages without appendices (18 in total), 4 figures

Ultra-light primordial black holes (PBHs) with masses $M_{\rm PBH}<5\times 10^8\mathrm{g}$ can dominate transiently the energy budget of the Universe and reheat the Universe through their evaporation taking place before Big Bang Nucleosynthesis. The isocurvature energy density fluctuations associated to the inhomogeneous distribution of a population of such PBHs can induce an abundant production of GWs due to second-order gravitational effects. In this work, we discuss the effect of primordial non-Gaussianity on the clustering properties of PBHs and study the effect of a clustered PBH population on the spectral shape of the aforementioned induced GW signal. In particular, focusing on local-type non-Gaussianity we find a double-peaked GW signal with the amplitude of the low-frequency peak being proportional to the square of the non-Gaussian parameter $\tau_\mathrm{NL}$. Remarkably, depending on the PBH mass $M_{\rm PBH}$ and the initial abundance of PBHs at formation time, i.e. $\Omega_\mathrm{PBH,f}$, this double-peaked GW signal can lie well within the frequency bands of forthcoming GW detectors, namely LISA, ET, SKA and BBO, hence rendering this signal falsifiable by GW experiments and promoting it as a novel portal probing the primordial non-Gaussianity., Comment: 19 pages plus appendices (32 pages in total), 2 figures

Gravitational waves (GWs) can undoubtedly serve as a messenger from the early Universe acting as well as a novel probe of the underlying gravity theory. In this work, motivated by one-loop vacuum-polarization effects on curved spacetime, we investigate a gravitational theory with non-minimal curvature-electromagnetic coupling terms of the form $\xi R F_{\mu\nu}F^{\mu\nu}$, where $R$ is the scalar curvature and $F_{\mu\nu}$ the Faraday tensor, which can be responsible for the generation of primordial electromagnetic fields. We study then the GW signatures of such coupling terms deriving in particular for the first time to the best of our knowledge the modified tensor modes equation of motion. Remarkably, we find a universal infrared (IR) frequency scaling $f^5$ of the electromagnetically induced GW (EMIGW) signal, which, depending on the energy scale of inflation, the duration of inflation and reheating as well as the dynamical behaviour of the gauge coupling function $\xi$, can be well within the detection sensitivity bands of GW experiments such as SKA, LISA, ET and BBO, being thus potentially detectable in the future by GW observatories., Comment: 16 pages plus appendices (19 pages in total), 3 figures

In light of NANOGrav data we provide for the first time possible observational signatures of Superstring theory. Firstly, we work with inflection-point inflationary potentials naturally realised within Wess-Zumino type no-scale Supergravity, which give rise to the formation of microscopic primordial black holes (PBHs) triggering an early matter-dominated era (eMD) and evaporating before Big Bang Nucleosythesis (BBN). Remarkably, we obtain an abundant production of primordial gravitational waves (PGW) at the frequency ranges of nHz, Hz and kHz and in strong agreement with Pulsar Time Array (PTA) GW data. This PGW background could serve as a compelling observational signature for the presence of quantum gravity via no-scale Supergravity., Comment: This work has received an honorable mention by the Gravity Research Foundation essay competition on Gravitation (2024)

Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them difficult to address with standard ab initio techniques. This work addresses this challenge by employing accelerated machine learning (ML) molecular dynamics simulations through active learning. We conduct a comparative study of different ML-based interatomic potential schemes, including VASP, MACE, and CHGNet, utilizing various training strategies such as on-the-fly learning, pre-trained universal models, and fine-tuning. We obtain an optimal hydrogen diffusion coefficient value of \(2.1 \cdot 10^{-8}\) m\(^2\)/s at 673 K in MgH\(_{0.06}\), which aligns exceptionally well with experimental results, underlining the efficacy and accuracy of ML-assisted methodologies in the context of diffusive dynamics. Particularly, our procedure significantly reduces the computational effort associated with traditional transition state calculations or ad-hoc designed interatomic potentials. The results highlight the limitations of pre-trained universal solutions for defective materials and how they can be improved by fine-tuning. Specifically, fine-tuning the models on a database produced during on-the-fly training of VASP ML force field allows the retrieving of DFT-level accuracy at a fraction of the computational cost.

Gate-level clocking, typical in traditional approaches to Single Flux Quantum (SFQ) technology, makes the effective synthesis of superconducting circuits a significant engineering hurdle. This paper addresses this challenge by employing the recently introduced alternating SFQ (xSFQ) logic family. xSFQ leverages dual-rail alternating encoding to eliminate the clock dependency from the superconducting gate semantics. This obviates the need for ad hoc modifications to existing synthesis tools and avoids unnecessary circuit resource overheads, marking a significant advancement in superconducting circuit design automation. Our implementation results demonstrate an average reduction of over 80\% in the Josephson junction count for circuits from the ISCAS85, EPFL, and ISCAS89 benchmark suites., Comment: Accepted for publication at DAC '24

Printed electronics offer ultra-low manufacturing costs and the potential for on-demand fabrication of flexible hardware. However, significant intrinsic constraints stemming from their large feature sizes and low integration density pose design challenges that hinder their practicality. In this work, we conduct a holistic exploration of printed neural network accelerators, starting from the analog-to-digital interface - a major area and power sink for sensor processing applications - and extending to networks of ternary neurons and their implementation. We propose bespoke ternary neural networks using approximate popcount and popcount-compare units, developed through a multi-phase evolutionary optimization approach and interfaced with sensors via customizable analog-to-binary converters. Our evaluation results show that the presented designs outperform the state of the art, achieving at least 6x improvement in area and 19x in power. To our knowledge, they represent the first open-source digital printed neural network classifiers capable of operating with existing printed energy harvesters., Comment: Accepted for publication at ICCAD '24

Large language models manifest the ability of few-shot adaptation to a sequence of provided examples. This behavior, known as in-context learning, allows for performing nontrivial machine learning tasks during inference only. In this work, we address the question: can we leverage in-context learning to predict out-of-distribution materials properties? However, this would not be possible for structure property prediction tasks unless an effective method is found to pass atomic-level geometric features to the transformer model. To address this problem, we employ a compound model in which GPT-2 acts on the output of geometry-aware graph neural networks to adapt in-context information. To demonstrate our model's capabilities, we partition the QM9 dataset into sequences of molecules that share a common substructure and use them for in-context learning. This approach significantly improves the performance of the model on out-of-distribution examples, surpassing the one of general graph neural network models., Comment: 12 pages, 4 figures

The evolution toward sixth-generation (6G) wireless networks has introduced programmable wireless environments (PWEs) and reconfigurable intelligent surfaces (RISs) as transformative elements for achieving near-deterministic wireless communications. However, the enhanced capabilities of RISs within PWEs, especially as we move toward more complex electromagnetic functions by increasing the number of reflecting elements, underscore the need for high-precision user localization, since inaccurate localization could lead to erroneous configuration of RISs, which would then compromise the effectiveness of PWEs. In this direction, this paper investigates the integration of RISs and optical anchors within PWEs, emphasizing the crucial role of ultra-precise localization in unlocking advanced electromagnetic functionalities. Specifically, we present an in-depth analysis of various localization techniques, both RISbased and RIS-independent, while introducing the concept of empowering PWEs with optical anchors for enhanced localization precision. Our findings highlight that accurate localization is essential to fully exploit the capabilities of RISs, paving the way for future applications. Through this exploration, we contribute to the advancement of PWEs in line with the ambitious goals of the 6G standards and improve the quality of service in next generation wireless networks.

We present a novel model-independent generic mechanism for primordial black hole formation within the context of non-singular matter bouncing cosmology. In particular, considering a short duration transition from the matter contracting phase to the Hot Big Bang expanding Universe, we find naturally enhanced curvature perturbations on very small scales which can collapse and form primordial black holes. Interestingly, the primordial black hole masses that we find can lie within the observationally unconstrained asteroid-mass window, potentially explaining the totality of dark matter. Remarkably, the enhanced curvature perturbations, collapsing to primordial black holes, can induce as well a stochastic gravitational-wave background, being potentially detectable by future experiments, in particular by SKA, PTAs, LISA and ET, hence serving as a new portal to probe the potential bouncing nature of the initial conditions prevailed in the early Universe., Comment: Accepted at JCAP

We study Active Model B+, a scalar field theory extending the paradigmatic Model B for equilibrium coexistence through including terms that do not arise from an underlying free energy functional and thus break detailed balance. In the first part of the manuscript, we provide a pedagogical and self-contained introduction to one-loop dynamic renormalization. We then address the technical challenge of complex vertex functions through developing a symbolic computer algebra code that allows us to obtain the graphical corrections of model parameters. We argue that the additional terms of Active Model B+ imply the generation of, potentially relevant, higher-order terms; strongly restricting the parameter regime in which we can apply a perturbative renormalization scheme. Moreover, we elucidate the role of the cubic coefficient, which, in contrast to passive Model B, is incessantly generated by the new terms. Analyzing its behavior with and without field shift near the Wilson-Fisher fixed point, we find that additional fixed points in the one-loop flow equations are likely artifacts. Additionally, we characterize the renormalization flow of perturbatively accessible field theories derived from Active Model B+., Comment: Overlaps with arXiv:2303.02222

Concentrated solid--solution alloys (CSAs) in single--phase form have recently garnered considerable attention owing to their potential for exceptional irradiation resistance. This computational study delves into the intricate interplay of alloying elements on the generation, recombination, and evolution of irradiation-induced defects. Molecular dynamics simulations were conducted for collision cascades at room temperature, spanning a range of primary knock-on atom energies from 1 to 10 keV. The investigation encompasses a series of model crystals, progressing from pure Ni to binary CSAs such as NiFe$_{20}$, NiFe, NiCr$_{20}$, and culminating in the more intricate NiFeCr$_{20}$ CSA. We observe that materials rich in chromium actively facilitate dislocation emissions and induce the nucleation of stacking fault tetrahedra in the proximity of nanovoids, owing to Shockley partial interactions. This result is validated by molecular static simulations, which calculate the surface, vacancy, and defect formation energies. Among various shapes considered, the spherical void proves to be the most stable, followed by the truncated octahedron and octahedron shapes. On the other hand, the tetrahedron cubic shape is identified as the most unstable, and stacking fault tetrahedra exhibit the highest formation energy. Notably, among the materials studied, NiCr$_{20}$ and NiFeCr$_{20}$ CSAs stood out as the sole alloys capable of manifesting this mechanism, mainly observed at high impact energies.

We propose a new probe of primordial non-Gaussianities (NGs) through the observation of gravitational waves (GWs) induced by ultra-light ($M_{\text{PBH}}< 10^{9}\rm{g}$) primordial black holes (PBHs). Interestingly enough, the existence of primordial NG can leave imprints on the clustering properties of PBHs and the spectral shape of induced GW signals. Focusing on a scale-dependent local-type NG, we identify a distinct double-peaked GW energy spectrum that, contingent upon $M_{\text{PBH}}$ and the abundance of PBHs at the time of formation, denoted as $\Omega_\mathrm{PBH,f}$, may fall into the frequency bands of upcoming GW observatories, including LISA, ET, SKA, and BBO. Thus, such a signal can serve as a novel portal for probing primordial NGs. Intriguingly, combining BBN bounds on the GW amplitude, we find for the first time the joint limit on the product of the effective non-linearity parameter for the primordial tri-spectrum, denoted by $\bar{\tau}_\mathrm{NL}$, and the primordial curvature perturbation power spectrum $\mathcal{P}_{\cal R}(k)$, which reads as $\bar{\tau}_\mathrm{NL} \mathcal{P}_{\cal R}(k) < 4\times 10^{-20} \Omega^{-17/9}_\mathrm{PBH,f} \left( \frac{M_{\rm PBH}}{10^4\mathrm{g}} \right)^{-17/9}$., Comment: Accepted in Physics Letters B

Light primordial black holes (PBHs) with masses $M_\mathrm{PBH}<10^9\mathrm{g}$ can interestingly dominate the Universe's energy budget and give rise to early matter-dominated (eMD) eras before Big Bang Nucleosyntesis (BBN). During this eMD era, one is met with an abundant production of induced gravitational waves (GWs) serving as a portal to constrain the underlying theory of gravity. In this work, we study this type of induced GWs within the context of string-inspired running-vaccuum models (StRVMs), which, when expanded around de Sitter backgrounds, include logarithmic corrections of the space-time curvature. In particular, we discuss in detail the effects of StRVMs on the source as well as on the propagation of these PBH-induced GWs. Remarkably, under the assumption that the logarithmic terms represent quantum gravity corrections in the PBH era, we show that GW overproduction can be avoided if one assumes a coefficient of these logarithmic corrections that is much larger than the square of the reduced Planck mass. The latter cannot characterise quantum gravity corrections, though, prompting the need for revision of the quantisation of StRVMs in different than de Sitter backgrounds, such as those characterising PBH-driven eMD eras. This non trivial result suggests the importance of light PBHs as probes of new physics., Comment: Accepted in Physical Review D

In this paper, we demonstrate the communication capabilities of light-fidelity (LiFi) systems based on highbrightness and high-bandwidth integrated laser-based sources in a surface mount device (SMD) packaging platform. The laserbased source is able to deliver 450 lumens of white light illumination and the resultant light brightness is over 1000 cd mm2. It is demonstrated that a wavelength division multiplexing (WDM) LiFi system with ten parallel channels is able to deliver over 100 Gbps data rate with the assistance of Volterra filter-based nonlinear equalisers. In addition, an aggregated transmission data rate of 4.8 Gbps has been achieved over a link distance of 500 m with the same type of SMD light source. This work demonstrates the scalability of LiFi systems that employ laserbased light sources, particularly in their capacity to enable highspeed short range, as well as long-range data transmission.

Concentrated solid solution alloys (CSAs) have attracted attention for their promising properties; however, current manufacturing methods face challenges in complexity, high costs, and limited scalability, raising concerns about industrial viability. The prevalent technique, arc melting, yields high-purity samples with complex shapes. In this study, we explore nanoindentation tests at room temperature where arc-melted samples exhibit larger grain sizes, diminishing the effects of grain boundaries on the results. Motivated by these findings, our investigation focuses on the atomistic-level exploration of plasticity mechanisms, specifically dislocation nucleation and propagation during nanoindentation tests. The intricate chemistry of NiFeCrCo CSA influences pile-ups and slip traces, aiming to elucidate plastic deformation by considering both pristine and pre-existing stacking fault tetrahedra. Our analysis scrutinizes dynamic deformation processes, defect nucleation, and evolution, complemented by stress-strain and dislocation densities-strain curves illustrating the hardening mechanism of defective materials. Additionally, we examine surface morphology and plastic deformation through atomic shear strain and displacement mappings. This integrated approach provides insights into the complex interplay between material structure and mechanical behavior, paving the way for an enhanced understanding and potential advancements in CSA applications.

In this work we propose simple, effective and computationally efficient transfer learning approaches for structure-property relation predictions in the context of materials, with highly informative input from different modalities. As materials properties stand from their electronic structure, representations are extracted directly from datasets of electronic charge density profile images using Neural Networks. We demonstrate transferability of the existing pre-trained Convolutional Neural Networks and Large Language Models knowledge to physics domain data, exploring a wide set of compositions for the regression of energetics- or structure- related properties, and the role of semantic crystallographic information in the context of multimodal approaches. We test the applicability of the CLIP multimodal model, and employ as well a training protocol for building a more interpretable and versatile stacked custom solution from different pre-trained modalities. The study offers a promising avenue for enhancing the effectiveness of descriptor identification in physical systems, shedding light on the power of multimodal transfer learning for materials property prediction, without any support of complex geometrical architectures and with potential impact on decision making for new low-cost AI methods in the field of Materials and Chemoinformatics.

Crystal dislocation dynamics, especially at high temperatures, represents a subject where experimental phenomenological input is commonly required, and parameter-free predictions, starting from quantum methods, have been beyond reach. This is especially true for phenomena like stacking faults and dislocation cross-slip, which are computationally intractable with methods like density functional theory, as $\sim 10^5-10^6$ atoms are required to reliably simulate such systems. Hence, this work extends quantum-mechanical accuracy to mesoscopic molecular dynamics simulations and opens unprecedented possibilities in material design for extreme mechanical conditions with direct atomistic insight at the deformation mesoscale. To accomplish this, we construct a Bayesian machine-learned force field (MLFF) from ab initio quantum training data, enabling direct observations of high-temperature and high-stress dislocation dynamics in single-crystalline Cu with atomistic resolution. In doing so, a generalizable training protocol is developed for construction of MLFFs for dislocation kinetics, with wide-ranging applicability to other single element systems and alloys. The resulting FLARE MLFF provides excellent predictions of static bulk elastic properties, stacking fault widths and energies, dynamic evolutions and mobilities of edge and screw dislocations, as well as cross-slip energy barriers for screw dislocations, all of which are compared to available experimental measurements. This work marks the first reliable quantitative determination of dislocation mobilities and cross-slip barriers, demonstrating a substantial advancement over previous empirical and machine learned force field models., Comment: 15 pages, 7 figures, 3 tables

Exploring the vast composition space of multi-component alloys presents a challenging task for both \textit{ab initio} (first principles) and experimental methods due to the time-consuming procedures involved. This ultimately impedes the discovery of novel, stable materials that may display exceptional properties. Here, the Crystal Diffusion Variational Autoencoder (CDVAE) model is adapted to characterize the stable compositions of a well studied multi-component alloy, NiFeCr, with two distinct crystalline phases known to be stable across its compositional space. To this end, novel extensions to CDVAE were proposed, enhancing the model's ability to reconstruct configurations from their latent space within the test set by approximately 30\% . A fact that increases a model's probability of discovering new materials when dealing with various crystalline structures. Afterwards, the new model is applied for materials generation, demonstrating excellent agreement in identifying stable configurations within the ternary phase space when compared to first principles data. Finally, a computationally efficient framework for inverse design is proposed, employing Molecular Dynamics (MD) simulations of multi-component alloys with reliable interatomic potentials, enabling the optimization of materials property across the phase space.

Deep learning models trained with large amounts of data have become a recent and effective approach to predictive problem solving -- these have become known as "foundation models" as they can be used as fundamental tools for other applications. While the paramount examples of image classification (earlier) and large language models (more recently) led the way, the Segment Anything Model (SAM) was recently proposed and stands as the first foundation model for image segmentation, trained on over 10 million images and with recourse to over 1 billion masks. However, the question remains -- what are the limits of this foundation? Given that magnetic resonance imaging (MRI) stands as an important method of diagnosis, we sought to understand whether SAM could be used for a few tasks of zero-shot segmentation using MRI data. Particularly, we wanted to know if selecting masks from the pool of SAM predictions could lead to good segmentations. Here, we provide a critical assessment of the performance of SAM on magnetic resonance imaging data. We show that, while acceptable in a very limited set of cases, the overall trend implies that these models are insufficient for MRI segmentation across the whole volume, but can provide good segmentations in a few, specific slices. More importantly, we note that while foundation models trained on natural images are set to become key aspects of predictive modelling, they may prove ineffective when used on other imaging modalities.

Material characterization in nano-mechanical tests requires precise interatomic potentials for the computation of atomic energies and forces with near-quantum accuracy. For such purposes, we develop a robust neural-network interatomic potential (NNIP), and we provide a test for the example of molecular dynamics (MD) nanoindentation, and the case of body-centered cubic crystalline molybdenum (Mo). We employ a similarity measurement protocol, using standard local environment descriptors, to select ab initio configurations for the training dataset that capture the behavior of the indented sample. We find that it is critical to include generalized stacking fault (GSF) configurations, featuring a dumbbell interstitial on the surface, to capture dislocation cores, and also high-temperature configurations with frozen atom layers for the indenter tip contact. We develop a NNIP with distinct dislocation nucleation mechanisms, realistic generalized stacking fault energy (GSFE) curves, and an informative energy landscape for the atoms on the sample surface during nanoindentation. We compare our NNIP results with nanoindentation simulations, performed with three existing potentials -- an embedded atom method (EAM) potential, a gaussian approximation potential (GAP), and a tabulated GAP (tabGAP) potential -- that predict different dislocation nucleation mechanisms, and display the absence of essential information on the shear stress at the sample surface in the elastic region. We believe that these features render specialized NNIPs essential for simulations of nanoindentation and nano-mechanics with near-quantum accuracy.

In the recent years, primordial black holes (PBHs) have emerged as one of the most interesting and hotly debated topics in cosmology. Among other possibilities, PBHs could explain both some of the signals from binary black hole mergers observed in gravitational wave detectors and an important component of the dark matter in the Universe. Significant progress has been achieved both on the theory side and from the point of view of observations, including new models and more accurate calculations of PBH formation, evolution, clustering, merger rates, as well as new astrophysical and cosmological probes. In this work, we review, analyse and combine the latest developments in order to perform end-to-end calculations of the various gravitational wave signatures of PBHs. Different ways to distinguish PBHs from stellar black holes are emphasized. Finally, we discuss their detectability with LISA, the first planned gravitational-wave observatory in space., Comment: 161 pages, 47 figures, comments welcome

Under plastic flow, multi-element high/medium-entropy alloys (HEAs/MEAs) commonly exhibit complex intermittent and collective dislocation dynamics owing to inherent lattice distortion and atomic-level chemical complexities. Using atomistic simulations, we report on an avalanche study of slowly-driven model face-centered cubic (fcc) NiCoCrFeMn and NiCoCr chemically complex alloys aiming for microstructural/topological characterization of associated dislocation avalanches. The results of our avalanche simulations reveal a close correspondence between the observed serration features in the stress response of the deforming HEA/MEA and the incurred slip patterns within the bulk crystal. We show that such correlations become quite pronounced within the rate-independent (quasi-static) regime exhibiting scale-free statistics and critical scaling features as universal signatures of dislocation avalanches.

The no-scale flipped SU(5) superstring framework constitutes a very promising paradigm for physics below the Planck scale providing us with a very rich cosmological phenomenology in accordance with observations. In particular, it can accommodate Starobinsky-like inflation, followed by a reheating phase, which is driven by a light "flaton" field, and during which the GUT phase transition occurs. In this Letter, we extract for the first time a gravitational-wave (GW) signal which naturally arises in the context of the flipped SU(5) cosmological phenomenology and is related to the existence of an early matter era (eMD) driven by the flaton field. Specifically, we study GWs non-linearly induced by inflationary perturbations and which are abundantly produced during a sudden transition from the flaton-driven eMD era to the late-time radiation-dominated era. Remarkably, we find a GW signal with a characteristic peak frequency $f_\mathrm{GW,peak}$ depending only on the string slope $\alpha'$ and reading as $f_\mathrm{GW,peak} \propto 10^{-9} \left(\frac{\alpha'}{\alpha'_*}\right)^4 \mathrm{Hz}$, where $\alpha'_*$ is the fiducial string slope being related directly to the reduced Planck scale $M_\mathrm{Pl}$ as $\alpha'_* = 8/M^2_\mathrm{Pl}$. Interestingly enough, $f_\mathrm{GW,peak}$ lies within the $\mathrm{nHz}$ frequency range; hence rendering this primordial GW signal potentially detectable by SKA, NANOGrav and PTA probes at their very low frequency region of their detection bands., Comment: Accepted in Physics Letters B. arXiv admin note: text overlap with arXiv:2307.08601

Materials design has traditionally evolved through trial-error approaches, mainly due to the non-local relationship between microstructures and properties such as strength and toughness. We propose 'alloy informatics' as a machine learning based prototype predictive approach for alloys and compounds, using electron charge density profiles derived from first-principle calculations. We demonstrate this framework in the case of hydrogen interstitials in face-centered cubic crystals, showing that their differential electron charge density profiles capture crystal properties and defect-crystal interaction properties. Radial Distribution Functions (RDFs) of defect-induced differential charge density perturbations highlight the resulting screening effect, and, together with hydrogen Bader charges, strongly correlate to a large set of atomic properties of the metal species forming the bulk crystal. We observe the spontaneous emergence of classes of charge responses while coarse-graining over crystal compositions. Nudge-Elastic-Band calculations show that RDFs and charge features also connect to hydrogen migration energy barriers between interstitial sites. Unsupervised machine-learning on RDFs supports classification, unveiling compositional and configurational non-localities in the similarities of the perturbed densities. Electron charge density perturbations may be considered as bias-free descriptors for a large variety of defects.

Shear localization in slowly-driven bulk metallic glasses (BMGs) is typically accompanied by a sharp drop in the bulk stress response as a signature of the plastic yielding transition. It is also observed that the sharpness of this elastic-plastic dynamical transition depends on the extent of local chemical and microstructural orders, as well as the glass preparation protocol ( ie. thermal annealing). Here, we investigate sheared multi-element BMGs in molecular dynamics (MD) simulations, and demonstrate that glass aging, implemented through a hybrid Monte-Carlo(MC)-MD process, sharpens the elastic-plastic transition through a distinct crossover, seen in strain patterns that gradually shift from diffuse features in as-quenched samples to localized (yet system-spanning) patterns in well-annealed glasses. This effect of glass aging on the elastic-plastic transition is found to be correlated to the inherent interplay between aging-induced icosahedra ordering and co-operative formation of shear transformation zones. The observed crossover is quantified through a measure of the age-dependent susceptibility to plastic rearrangements, exhibiting strong (anti-)correlations to local ordering features, and the corresponding spatial correlation length grows with the aging timescale., Comment: arXiv admin note: text overlap with arXiv:2110.12152

In this paper, we study optical simultaneous lightwave information and power transfer (SLIPT) systems employing photovoltaic optical receivers (RXs). To be able to efficiently harvest energy from broadband light, we propose to employ multi-junction photovoltaic cells at the optical RX. We consider the case, where the optical RX is illuminated by ambient light, an intensity-modulated information free space optical (FSO) signal, and since the ambient light may not be always present, a dedicated energy-providing broadband optical signal. Based on the analysis of the equivalent electrical circuit of the multi-junction photovoltaic RX, we model the current flow through the photovoltaic cell and derive a novel accurate and two novel approximate energy harvesting (EH) models for the two cases, where the optical RX is equipped with a single and multiple p-n junctions, respectively. We derive the optimal distribution of the transmit information signal that maximizes the achievable information rate. We validate the proposed EH models by circuit simulations and show that the photovoltaic RXs saturate for high received signal powers. For single-junction RXs, we compare the proposed EH model with two well-known baseline models and demonstrate that, in contrast to the proposed EH model, they are not able to fully capture the RX non-linearity. Since multi-junction RXs allow a more efficient allocation of the optical power, they are more robust against saturation, and thus, are able to harvest significantly more power and achieve higher data rates than RXs employing a single p-n junction. Finally, we highlight a tradeoff between the information rate and harvested power in SLIPT systems and demonstrate that the proposed optimal distribution yields significantly higher achievable information rates compared to uniformly distributed transmit signals, which are optimal for linear optical information RXs., Comment: 13 pages, 10 figures, submitted for possible IEEE journal publication. arXiv admin note: text overlap with arXiv:2305.03524

Deformation plasticity mechanisms in alloys and compounds may unveil the material capacity towards optimal mechanical properties. We conduct a series of molecular dynamics (MD) simulations to investigate plasticity mechanisms due to nanoindentation in pure tungsten, molybdenum and vanadium body-centered cubic single crystals, as well as the also body-centered cubic, equiatomic, random solid solutions (RSS) of tungsten--molybdenum and tungsten--vanadium alloys. Our analysis focuses on a thorough, side-by-side comparison of dynamic deformation processes, defect nucleation, and evolution, along with corresponding stress--strain curves. We also check the surface morphology of indented samples through atomic shear strain mapping. As expected, the presence of Mo and V atoms in W matrices introduces lattice strain and distortion, increasing material resistance to deformation and slowing down dislocation mobility of dislocation loops with a Burgers vector of 1/2 $\langle 111 \rangle$. Our side-by-side comparison displays a remarkable suppression of the plastic zone size in equiatomic W--V RSS, but not in equiatomic W--Mo RSS alloys, displaying a clear prediction for optimal hardening response equiatomic W--V RSS alloys. If the small-depth nanoindentation plastic response is indicative of overall mechanical performance, it is possible to conceive a novel MD-based pathway towards material design for mechanical applications in complex, multi-component alloys.

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

In this study, we employ a computational chemistry-based modeling approach to investigate the adsorption mechanisms of CH$_4$ and CO$_2$ on monolayer GPNL, with a specific focus on their effects on optical adsorption and electrical transport properties at room temperature. To simulate the adsorption dynamics as closely as possible to experimental conditions, we utilize the self-consistent charge tight-binding density functional theory (SCC-DFTB). Through semi-classical molecular dynamics (MD) simulations, we observe the formation of H$_2$ molecules from the dissociation of CH$_4$ and the formation of CO+O species from carbon dioxide molecules. This provides insights into the adsorption and dispersion mechanisms of CH$_4$ and CO$_2$ on GPNL. Furthermore, we explore the impact of molecular adsorption on optical absorption properties. Our results demonstrate that CH$_4$ and CH$_2$ affects drastically the optical adsorption of GPNL, while CO$_2$ does not significantly affect the optical properties of the two-dimensional material. To analyze electron transport, we employ the open-boundary non-equilibrium Green's function method. By studying the conductivity of GPNL and graphene under voltage bias up to 300 mV, we gain valuable insights into the electrical transport properties of GPNL under optical absorption conditions. The findings from our computational modeling approach might contribute to a deeper understanding of the potential applications of GPNL in hydrogen production and advanced electronic devices.

In this Letter, we derive for the first time a characteristic three-peaked GW signal within the framework of no-scale Supergravity, being the low-energy limit of Superstring theory. We concentrate on the primordial gravitational wave (GW) spectrum induced due to second-order gravitational interactions by inflationary curvature perturbations as well as by isocurvature energy density perturbations of primordial black holes (PBHs) both amplified due to the presence of an early matter-dominated era (eMD) era before Big Bang Nucleosythesis (BBN). In particular, we work with inflection-point inflationary potentials naturally-realised within Wess-Zumino type no-scale Supergravity and giving rise to the formation of microscopic PBHs triggering an eMD era and evaporating before BBN. Remarkably, we obtain an abundant production of gravitational waves at the frequency ranges of $\mathrm{nHz}$, $\mathrm{Hz}$ and $\mathrm{kHz}$ and in strong agreement with Pulsar Time Array (PTA) GW data. Interestingly enough, a simultaneous detection of all three $\mathrm{nHz}$, $\mathrm{Hz}$ and $\mathrm{kHz}$ GW peaks can constitute a potential observational signature for no-scale Supergravity., Comment: Accepted in Physics Letters B