Range Of Interactions Research Articles

Initial tensor construction and dependence of the tensor renormalization group on initial tensors

We propose a method to construct a tensor network representation of partition functions without singular value decompositions nor series expansions. The approach is demonstrated for one- and two-dimensional Ising models and we study the dependence of the tensor renormalization group (TRG) on the form of the initial tensors and their symmetries. We further introduce variants of several tensor renormalization algorithms. Our benchmarks reveal a significant dependence of various TRG algorithms on the choice of initial tensors and their symmetries. However, we show that the boundary TRG technique can eliminate the initial tensor dependence for all TRG methods. The numerical results of TRG calculations can thus be made significantly more robust with only a few changes in the code. Furthermore, we study a three-dimensional Z2 gauge theory without gauge fixing and confirm the applicability of the initial tensor construction. Our method can straightforwardly be applied to systems with longer range and multisite interactions, such as the next-nearest neighbor Ising model. Published by the American Physical Society 2024

Two particles in one-dimensional confinement: the role of mass imbalance and Pauli's exclusion principle within short and long range interactions

Abstract We consider the problem of two interacting particles in one-dimensional harmonic confinement. By considering both particles are subject to the same harmonic curvature, we obtain exact numerical solutions in a straightforward way for any choice of interaction potential. The formulation is then applied in the situations of ({\it i}) non-identical particles with different masses $m_1$ and $m_2$; ({\it ii}) bosons and fermions; ({\it iii}) short and long range interactions; and ({\it iv}) combining all these ingredients within a time-dependent applied electric field. We analyze the role of the mass imbalance and Pauli's exclusion principle by investigating the formation of bound pairs, the expected value of the separation between the particles and the effects of interaction and exclusion principle on the density distributions.

General features and contact modeling of $N$-body isolated resonances near threshold

NN-body non efimovian bound or quasi-bound states for particles with short range interactions are considered in arbitrary dimensions. The different resonance regimes near the threshold are depicted by using a generalization of the effective range approximation. This two-parameter description can be used in various contexts from ultracold to hadronic physics. The universal character of these states makes it possible a formulation in terms of a contact model. The singularity at the contact imposes the introduction of a modified scalar product to solve the normalization catastrophe and to restore the self-adjoint character of the model. An equivalence with the standard scalar product used for realistic finite range models is derived.

No violations of critical-point wetting in ternary three fluid-phase systems with short range interactions

No violations of critical-point wetting in ternary three fluid-phase systems with short range interactions

Machine learning the electric field response of condensed phase systems using perturbed neural network potentials

The interaction of condensed phase systems with external electric fields is of major importance in a myriad of processes in nature and technology, ranging from the field-directed motion of cells (galvanotaxis), to geochemistry and the formation of ice phases on planets, to field-directed chemical catalysis and energy storage and conversion systems including supercapacitors, batteries and solar cells. Molecular simulation in the presence of electric fields would give important atomistic insight into these processes but applications of the most accurate methods such as ab-initio molecular dynamics (AIMD) are limited in scope by their computational expense. Here we introduce Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) to push back the accessible time and length scales of such simulations. We demonstrate that important dielectric properties of liquid water including the field-induced relaxation dynamics, the dielectric constant and the field-dependent IR spectrum can be machine learned up to surprisingly high field strengths of about 0.2 V Å−1 without loss in accuracy when compared to ab-initio molecular dynamics. This is remarkable because, in contrast to most previous approaches, the two neural networks on which PNNP MD is based are exclusively trained on molecular configurations sampled from zero-field MD simulations, demonstrating that the networks not only interpolate but also reliably extrapolate the field response. PNNP MD is based on rigorous theory yet it is simple, general, modular, and systematically improvable allowing us to obtain atomistic insight into the interaction of a wide range of condensed phase systems with external electric fields.

(Invited) Spatially–Resolved Luminescence Properties of Etched Quantum Well Microstructures

Fabrication processes for photonic devices, especially micro- or nano-photonic devices, call for some kind of control of the materials’ optical properties at the stage of the final device. Our study focusses on measuring the optical quality of quantum well (QW) containing materials after device processing, using steps such as etching which allows fabrication of optical waveguides for example. We first designed and realized specific material structures based on an InP substrate, with a series of InAsxP1-x quantum wells such as illustrated on the figure. The samples were grown by gas-phase molecular beam epitaxy. The samples were characterized by spatially-resolved photoluminescence (PL) at low temperature (10 K). The top right part shows a typical spectrum measured from the sample we have grown: sharp lines associated with each QW can be clearly identified thanks to a careful grading of the As/P composition in each QW, while keeping its thickness to a constant value (typically 7 to 8 nm). Then we etched the samples (in the form of rectangular ridges as shown) using different etching processes, based either on wet etching or reactive ion etching (RIE), and recorded the PL spectra with high spatial resolution across the obtained structures. In parallel, the samples after etching were also characterized by cross-sectional scanning electron microscopy. Data processing from the series of PL spectra was then performed in order the characterize the effects of etching on the QW materials.We show PL intensity profiles across a series of etched stripes of different widths resulting from a RIE process. These intensity profiles were obtained from the lines associated with the different QWs in the structure. One can observe, first of all, that the PL intensity gradually increases from the edges to the center of the stripes, over a range of up to 10 µm. One can also observe that for “wide” stripes, i.e. 30 or 50 µm, the PL intensity for each line saturates at the same level in the central part. However, for the narrower stripes (10 or 20 µm), the PL signal does not reach the same level. This is the signature of some PL-limitation phenomenon, which extends its effects far from the etched sidewalls. A “standard” phenomenon resulting solely from non-radiative recombination at the etched sidewalls is excluded. Non-radiative recombination can only affect regions which are less than one charge carrier diffusion length from the sidewalls. Diffusion length in our materials is less than 1 µm. Therefore, some kind of “long range” interaction seems to affect our samples. We propose that this long range interaction is due to some stray static electric field resulting from the presence of residual ions left by the etching process at the sidewalls. The electric field affects the PL intensity through dissociation of excitons. Moreover, we observed that different conditions for the etching process (wet etching versus RIE, crystallographic orientation of the stripes) impact the magnitude of this PL intensity quenching.[1] Pearton S 1997 Materials Science and Engineering: B 44 1–7[2] Landesman J P, Isik-Goktas N, LaPierre R R, Levallois C, Ghanad-Tavakoli S, Pargon E, Petit-Etienne C and Jiménez J 2021 Journal of Physics D: Applied Physics 54 445106[3] Fiore A, Rossetti M, Alloing B, Paranthoen C, Chen J X, Geelhaar L and Riechert H 2004 Physical Review B 70 205311[4] Bludau W and Wagner E 1976 Physical Review B 13 5410–5414 Figure 1

One dimensional lattice fluid mixture with nearest neighbour interactions

We present an exact derivation of the free energy functional of a fluid mixture of hard rods with arbitrary sizes on a one-dimensional lattice. Our approach is based on the Wertheim cluster theory which consists of mapping a system with finite range interactions to the system with pure hard-core interaction but with modified activities. We show that the free energy functional has the same form as the fundamental measure functional. The interactions part of the free energy has two contributions, one from the one-particle cavity restricted to the hard rod or hard-sphere diameter and a second from the two-particle cavity which includes the finite range of the interaction. In the limit of a one-component system, our results reduce to the one derived using the Markov chain approach. For vanishing interactions, the density functionals coincide exactly with the previously derived for the mixture of hard rods with pure hard-core interaction.

Unsteady MHD dusty fluid flow over a non-isothermal cone embedded in a porous medium

An investigation of the impacts of magnetic field, heat generation/absorption and thermal radiation on unsteady free convection dusty fluid flow over a non-isothermal vertical cone enclosed inside a porous medium is explored. The Crank–Nicolson approach is used to get the numerical solutions for these nonlinear, coupled partial differential equations (PDEs). The interaction of physical parameter range on temperature and velocity distribution is calculated and graphically presented. The results demonstrate that when the porosity, heat generation/absorption, and thermal radiation parameters are increased, the velocities rise, whereas the magnetic and mass concentration of particle phase parameters have an opposite effect. Furthermore, raising the fluid-particle interaction parameter causes a rise in dust phase velocity but a reduction in fluid phase velocity.

Philicity indices of carbenes

New electronic philicity indices Ie, P for donor–acceptor interactions of a wide range of carbenes of both nucleophilic and electrophilic type were assessed using the DFT methods. For typical nucleophilic neutral carbenes and hydrogen ions PH are in the range of 1.4–2.8 eV, PH′ 1.5– 1.9, IeH 9.5–11.9 eV; for typical electrophilic carbenes pH 0–0.6 eV, PH′ 1.0– −0.3, IeH −2.0–3.6 eV. The electronegativities χ and global electrophilicities ω of the same carbenes for one-electron processes were also determined. It was established that the electronegativity is suitable criterium of philicity. For typical nucleophilic neutral carbenes ω are in the range of 0.1–1.1 eV, χ 1.5–3.9 eV, for typical electrophilic carbenes ω 1.4–4.9 eV, χ 4.7–7.1 eV. The data obtained can be used in the design of structures for synthetic and practical applications.

Heat current properties of a rotor chain type model with next-nearest-neighbor interactions

In this article, to study the heat flow behavior, we perform analytical investigations in a rotor chain type model (involving inner stochastic noises) with next and next-nearest-neighbor (NN) interactions. It is known in the literature that the chain rotor model with long range interactions presents an insulating phase for the heat conductivity. But we show, in contrast with such a behavior, that the addition of a next-NN potential increases the thermal conductivity, at least in the low temperature regime, indicating that the insulating property is a genuine long range interaction effect. We still establish, now by numerical computations, the existence of a thermal rectification in systems with graded structures.

Study of Acoustic Emission from the Gate of Gallium Nitride High Electron Mobility Transistors

Nitrides are the leading semiconductor material used for the fabrication of high electron mobility transistors (HEMTs). They exhibit piezoelectric properties, which, coupled with their high mechanical stiffness, expand their versatile applications into the fabrication of piezoelectric devices. Today, due to advances in device technology that result in a reduction in the size of individual transistor elements and due to increased structural complexity (e.g., multi-gate transistors), the integration of piezoelectric materials into HEMTs leads to an interesting occurrence, namely acoustic emission from the transistor gate due to piezoelectric effects. This could affect the device’s performance, reliability, and durability. However, this phenomenon has not yet been comprehensively described. This paper aims to examine this overlooked aspect of AlGaN/GaN HEMT operation, that is, the acoustic emission from the gate region of the device induced by piezoelectric effects. For this purpose, dedicated test structures were designed, consisting of two narrow 1.7 μm-wide metallization strips placed at distances ranging from 5 μm to 200 μm fabricated in AlGaN/GaN heterostructures to simulate and examine the gate behavior of the HEMT transistor. For comparison, the test device structures were also fabricated on sapphire, which is not a piezoelectric material. Measurements of acoustic and electrical interactions in the microwave range were carried out using the “on wafer” method with Picoprobe’s signal–ground–signal (SGS)-type microwave probes. The dependence of reflectance |S11| and transmittance |S21| vs. frequency was investigated, and the coupling capacitance was determined. An equivalent circuit model of the test structure was developed, and finite element method simulation was performed to study the distribution of the acoustic wave in the nitride layers and substrate for different frequencies using Comsol Multiphysics software. At frequencies up to 2–3 GHz, the formation of volume waves and a surface wave, capable of propagating over long distances (in the order of tens of micrometers) was observed. At higher frequencies, the resulting distribution of displacements as a result of numerous reflections and interferences was more complicated. However, there was always the possibility of a surface wave occurrence, even at large distances from the excitation source. At small gate distances, electrical interactions dominate. Above 100 µm, electrical interactions are comparable to acoustic ones. With further increases in distance, weakly attenuated surface waves will dominate.

Cu-optimized long-range interaction between Co nanoparticles and Co single atoms: Improved Fenton-like reaction activity

The interplay between multi-atom assembly configurations and single atoms (SAs) has been gaining attention in research. However, the effect of long-term range interactions between SAs and multi-atom assemblies on the orbital filling characteristics has yet to be investigated. In this context, we introduced copper (Cu) doping to strengthen the interaction between cobalt (Co) nanoparticles (NPs) and Co SAs by promoting the spontaneous formation of Co–Cu alloy NPs that tends toward aggregation owing to its negative cohesive energy (−0.06454), instead of forming Cu SAs. The incorporation of Cu within the Co–Cu alloy NPs, compared to the pure Co NPs, significantly expedites the kinetics of peroxymonosulfate (PMS) oxidation processes on Co SAs. Unlike Co NPs, Co–Cu NPs facilitate electron rearrangement in the d orbitals (especially dz2 and dxz) near the Fermi level in Co SAs, thereby optimizing the dz2–O (PMS) and dxz–O (SO5−) orbital interaction. Eventually, the Co–Cu alloy NPs embedded in nitrogen-doped carbon (CC@CNC) catalysts rapidly eliminated 80.67% of 20 mg L−1 carbamazepine (CBZ) within 5 min. This performance significantly surpasses that of catalysts consisting solely of Co NPs in a similar matrix (C@CNC), which achieved a 58.99% reduction in 5 min. The quasi in situ characterization suggested that PMS acts as an electron donor and will transfer electrons to Co SAs, generating 1O2 for contaminant abatement. This study offers valuable insights into the mechanisms by which composite active sites formed through multi-atom assembly interact at the atomic orbital level to achieve high-efficiency PMS-based advanced oxidation processes at the atomic orbital level.

Theoretical and practical investigation of ion-ion association in electrolyte solutions.

In this study, we present a new equation of state for electrolyte solutions, integrating the statistical associating fluid theory for variable range interactions utilizing the generic Mie form and binding Debye-Hückel theories. This equation of state underscores the pivotal role of ion-ion association in determining the properties of electrolyte solutions. We propose a unified framework that simultaneously examines the thermodynamic properties of electrolyte solutions and their electrical conductivity, given the profound impact of ion pairing on this transport property. Using this equation of state, we predict the liquid density, mean ionic activity coefficient, and osmotic coefficient for binary NaCl, Na2SO4, and MgSO4 aqueous solutions at 298.15K. Additionally, we evaluate the molar conductivity of these systems by considering the fraction of free ions derived from our equation of state in conjunction with two advanced electrical conductivity models. Our results reveal that, while ion-ion association has a minimal influence on the modification of the predicted properties of sodium chloride solutions, their impact on sodium and magnesium sulfate solutions is considerably more noticeable.

Structural, Spectroscopic, and Computational Insights from Canavanine-Bound and Two Catalytically Compromised Variants of the Ethylene-Forming Enzyme.

The ethylene-forming enzyme (EFE) is an Fe(II), 2-oxoglutarate (2OG), and l-arginine (l-Arg)-dependent oxygenase that either forms ethylene and three CO2/bicarbonate from 2OG or couples the decarboxylation of 2OG to C5 hydroxylation of l-Arg. l-Arg binds with C5 toward the metal center, causing 2OG to change from monodentate to chelate metal interaction and OD1 to OD2 switch of D191 metal coordination. We applied anaerobic UV-visible spectroscopy, X-ray crystallography, and computational approaches to three EFE systems with high-resolution structures. The ineffective l-Arg analogue l-canavanine binds to the EFE with O5 pointing away from the metal center while promoting chelate formation by 2OG but fails to switch the D191 metal coordination from OD1 to OD2. Substituting alanine for R171 that interacts with 2OG and l-Arg inactivates the protein, prevents metal chelation by 2OG, and weakens l-Arg binding. The R171A EFE had electron density at the 2OG binding site that was identified by mass spectrometry as benzoic acid. The substitution by alanine of Y306 in the EFE, a residue 12 Å away from the catalytic metal center, generates an interior cavity that leads to multiple local and distal structural changes that reduce l-Arg binding and significantly reduce the enzyme activity. Flexibility analyses revealed correlated and anticorrelated motions in each system, with important distinctions from the wild-type enzyme. In combination, the results are congruent with the currently proposed enzyme mechanism, reinforce the importance of metal coordination by OD2 of D191, and highlight the importance of the second coordination sphere and longer range interactions in promoting EFE activity.

Potentiostatic oscillations of current and surface structure in a 3D cellular automaton model of metal passivation

Some recently obtained data from simulations in our 3D cellular automaton model on metal passivation are presented and discussed. The model is a non-equilibrium heterogeneous Ising-like model with moving boundary due to metal depletion by corrosion. The mobile particles representing metal oxide are produced in corrosion events. They bind to the metal surface and to each other with short range interaction. They diffuse by random walk with asymmetric exclusion and disappear in the bulk solution.The results of this paper are obtained in potentiostatic conditions. In our model, this amounts to a fixed bare metal corrosion rate. The results include current transients and surface snapshots at a number of moments of time. We define several other dynamic variables. Their transients allow us to construct phase portraits, to identify the bare metal surface area and the oxide layer thickness as the essential variables for the oscillations. The first of those is equivalent to current density by model construction. The second plays a peculiar role in periodic current spiking, as discussed in the text.

Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning

BackgroundDiscovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have been developed for DDI prediction. However, they generally require a huge number of samples, while known DDIs are rare.MethodsIn this work, we present KnowDDI, a graph neural network-based method that addresses the above challenge. KnowDDI enhances drug representations by adaptively leveraging rich neighborhood information from large biomedical knowledge graphs. Then, it learns a knowledge subgraph for each drug-pair to interpret the predicted DDI, where each of the edges is associated with a connection strength indicating the importance of a known DDI or resembling strength between a drug-pair whose connection is unknown. Thus, the lack of DDIs is implicitly compensated by the enriched drug representations and propagated drug similarities.ResultsHere we show the evaluation results of KnowDDI on two benchmark DDI datasets. Results show that KnowDDI obtains the state-of-the-art prediction performance with better interpretability. We also find that KnowDDI suffers less than existing works given a sparser knowledge graph. This indicates that the propagated drug similarities play a more important role in compensating for the lack of DDIs when the drug representations are less enriched.ConclusionsKnowDDI nicely combines the efficiency of deep learning techniques and the rich prior knowledge in biomedical knowledge graphs. As an original open-source tool, KnowDDI can help detect possible interactions in a broad range of relevant interaction prediction tasks, such as protein-protein interactions, drug-target interactions and disease-gene interactions, eventually promoting the development of biomedicine and healthcare.

Effects of irrigation dams on riverine biota in mountain streams

Mountain streams harbor unique biodiversity and provide essential ecosystem services to human societies. Yet, these ecosystems face numerous threats, such as the construction of dams and land use changes, leading to rapid habitat degradation, water pollution, and biodiversity loss. In this study, we assess the effect of irrigation dams on mountain riverine biota using traditional biotic indices and trait-based approaches. We selected diatom and macroinvertebrate communities surveyed between 2015 and 2017 in mountain streams located in different regions in northern Spain (Cantabrian Cordillera, Iberian System, and Pyrenees) under natural and altered flow conditions (i.e., downstream of irrigation dams). Hydrological and biological changes related to the presence of dams, the mountain range, and the interaction between these two factors were identified. Summer flows, frequency of high flow events, and minimum annual flows timing were significantly affected by irrigation dams, independently of the region. Winter flows, the magnitude of high flow extremes, and the number of flow rises and falls varied significantly with the dam-mountain range interaction. The frequency and duration of flow pulses depended on the mountain range only. In the Cantabrian Cordillera, a region with larger reservoirs (>150 hm3), impacted sites showed a marked inversion of the seasonal flow patterns (i.e., increased summer flows but reduced winter flows). In the other mountain ranges, reservoirs had smaller storage volumes and multiple purposes, causing significant flow change frequency variations. Diatom traits, taxonomic richness, diversity, and IPS score varied with dam presence and mountain ranges, while macroinvertebrate traits and biotic indices responded weakly. These findings suggest that diatom communities might be more sensitive to hydrological alteration, while macroinvertebrates might be more influenced by space-related factors, such as biogeography and dispersal, overriding dam-related impacts. Furthermore, dam-related changes in ecosystems may depend not only on the presence of dams and their characteristics (e.g., reservoir size and operation), but also on local conditions and biogeography. Our findings emphasize that, when using pre-existing biomonitoring datasets, although some dam-related patterns emerge (e.g., with diatoms), other patterns may be constrained by the datasets’ low spatio-temporal coverage and taxonomic resolution, highlighting the need of well-structured study designs.

Creating spatial doughnut-spot arrays and double-helix focal fields with prescribed characteristics

Spatially controllable focal fields play a pivotal role in light manipulation and provide significant opportunities for precisely manipulating light–matter interactions in a wide range of applications. In particular, the double-helix focal field—characterized by a distinctive helical structure—exhibits exceptional optical properties, thus differentiating it apart from conventional focal fields. However, the rapid construction of a double-helix focal field with controllable characteristics and a uniform intensity remains a challenging task. Based on the theory of pattern synthesis of an antenna array, we propose and realize the generation of three-dimensional doughnut-spot arrays and double-helix focal fields with specified characteristics in a 4π system by reverse-solving the radiation field of the virtual antenna. Numerical examples indicate that the desired novel focal fields, including features such as shape, orientation, length, and period, could be rapidly, conveniently, and flexibly customized by selecting appropriate parameters for the magnetic dipole array antennas. This method could reveal an avenue for enhanced light manipulation for applications in materials processing, optical lithography, and optical communications.

Optimising peak energy reduction in networks of buildings

Buildings are amongst the world’s largest energy consumers and simultaneous peaks in demand from networks of buildings can decrease electricity system stability. Current mitigation measures either entail wasteful supply-side over-specification or complex centralised demand-side control. Hence, a simple schema is developed for decentralised, self-organising building-to-building load coordination that requires very little information exchange and no top-down management—analogous to other complex systems with short range interactions, such as coordination between flocks of birds or synchronisation in fireflies. Numerical and experimental results reveal that a high degree of peak flattening can be achieved using surprisingly small load-coordination networks. The optimum reductions achieved by the simple schema can outperform existing techniques, giving substantial peak-reductions as well as being remarkably robust to changes in other system parameters such as the interaction network topology. This not only demonstrates that significant reductions in network peaks are achievable using remarkably simple control systems but also reveals interesting theoretical results and new insights which will be of great interest to the complexity and network science communities.

Spectral information content of Compton scattering events in silicon photon counting detectors.

Silicon (Si) is a possible sensor material for photon counting detectors (PCDs). A major drawback of Si is that roughly two-thirds of x-ray interactions in the diagnostic energy range are Compton scattering. Because Compton scattering is an energy-insensitive process, it is commonly assumed that Compton events retain little spectral information. To quantify how much information can be recovered from Compton scattering events in models of Si PCDs. We built a simplified model of Si interactions including two interaction mechanisms: photoelectric effect and Compton scattering. We considered three different binning options that represent strategies for handling Compton events: in Compton censoring, all events under 38keV (the maximum energy possible from Compton scattering for a 120keV incident photon) were discarded; in Compton counting, all events between 1 and 38keV were placed into a single bin; in Compton binning, all events were placed into energy bins of uniform width. These were compared to the ideal detector, which always recorded the correct energy (i.e., 100% photoelectric effect). Every photon was assumed to interact once and only once with Si, and the energy bin width was 5keV. In the primary analysis, the Si detector was irradiated with a 120kV spectrum filtered by 30cm of water, with 99.5% of the arriving spectrum above 38keV so that there was good separation between photoelectric effect and Compton scattering, and the figures of merit were the Cramér-Rao lower bound (CRLB) of the variance of iodine and water basis material decomposition images, as well as the CRLB of virtual monoenergetic images (i.e., linear combinations of material images) that maximize iodine CNR or water CNR. We also constructed a local linear estimator that attains the CRLB. In secondary analyses, we applied other sources of spectral distortion: (1) a nonzero minimum energy threshold; (2) coarser, 10keV energy bins; and (3) a model of charge sharing. With our chosen spectrum, 67% of the interactions were Compton scattering. Consistent with this, the material decomposition variance for the Compton censoring model, averaged over both basis materials, was 258% greater than the ideal detector. If Compton events carried no spectral information, the Compton counting model would show similar variance. Instead, its basis material variance was 103% greater than the ideal detector, implying that Compton counts indeed carry significant spectral information. The Compton binning model had a basis material variance 60% greater than the ideal detector. The Compton binning model was not affected by a 5keV minimum energy threshold, but the variance increased from 60% to 107% when charge sharing was included and to 78% with coarser energy bins. For optimized CNR images, the average variance was 149%, 12%, and 10% higher than the ideal detector for the Compton censoring, counting, and binning models, reinforcing the hypothesis that Compton counts are useful for detection tasks and that precise energy assignments are not necessary. Substantial spectral information remains after Compton scattering events in silicon PCDs.