Classical Field Research Articles

Microscopic Structure of Neat Linear Alkylamine Liquids: An X-Ray Scattering and Computer Simulation Study.

Linear amines, from propylamine to nonylamine, are studied under ambient conditions by X-ray scattering and molecular dynamics simulations of various force field models. The major finding is that the prepeak in alkylamines is about 1 order of magnitude weaker than that in alkanols, hence suggesting much weaker hydrogen bonding-induced clustering of the amine groups than for the hydroxyl groups. Computer simulation studies reveal that the OPLS-UA model reproduces the prepeak, but with larger amplitudes, while the GROMOS-UA and CHARMM-AA force fields show almost no prepeak. Simulations of all models show the existence of hydrogen-bonded clusters, equally confirmed by the prominent prepeak of the structure factor between the nitrogen atoms. The hydrogen bond strength, as modeled by the Coulomb association in classical force field models, is about the same order of magnitude for both systems. Then, one may ask what is the origin of the weaker prepeak in alkylamines? Simulation data reveal that the existence of the prepeak is controlled through the cancellation of the positive contributions from the charged group correlations by the negative contributions from the cross charged-uncharged correlations. The C2v symmetry of the amine headgroup hinders clustering, which favors cross correlations with the tail atoms. This is opposite to alkanols where the symmetry of the hydroxyl headgroup favors clustering and hinders cross correlations with the alkyl tail. This competition between charged and uncharged atomic groups appears as a general mechanism to explain the existence of scattering prepeaks, including their position and amplitude.

Conformal Yang-Mills field in (A)dS space

Ordinary-derivative (second-derivative) Lagrangian formulation of classical conformal Yang-Mills field in the (A)dS space of six, eight, and ten dimensions is developed. For such conformal field, we develop two gauge invariant Lagrangian formulations which we refer to as generic formulation and decoupled formulation. In both formulations, the usual Yang-Mills field is accompanied by additional vector and scalar fields where the scalar fields are realized as Stueckelberg fields. In the generic formulation, the usual Yang-Mills field is realized as a primary field, while the additional vector fields are realized as auxiliary fields. In the decoupled formulation, the usual Yang-Mills field is realized as massless field, while the additional vector fields together with the Stueckelberg are realized as massive fields. Some massless/massive fields appear with the wrong sign of kinetic terms, hence demonstrating explicitly that the considered models are not unitary. The use of embedding space method allows us to treat the isometry symmetries of (A)dS space manifestly and obtain conformal transformations of fields in a relatively straightforward way. By accompanying each vector field by the respective gauge parameter, we introduce an extended gauge algebra. Levy-Maltsev decomposition of such algebra is noted. Use of the extended gauge algebra setup allows us to present concise form for the Lagrangian and gauge transformations of the conformal Yang-Mills field. Higher-derivative representation of the Lagrangian is also obtained.

Qubit-qubit quantum coherence mediated by an epsilon-near-zero waveguide

Quantum coherence, as a more general quantum resource compared to quantum entanglement, has attracted increasing attention over recent years. Establishing stable quantum coherence is crucial for implementing reliable quantum information tasks. In this study, we propose a scheme to generate stable quantum coherence of two qubits via an epsilon-near-zero (ENZ) waveguide. We find that employing Si3N4 rather than SiO2 results in stronger qubit-qubit coupling and maximal quantum coherence in a certain range. We derive analytical expressions for both quantum coherence and quantum entanglement, allowing for direct comparison within a unified framework. To achieve stable quantum coherence, classical field driving is introduced. We find that stable coherence is much larger and easier mediated than that of stable entanglement. Our work contributes to the creation of a new stable quantum resource via an ENZ waveguide.

Canonical coordinates for Yang-Mills-Chern-Simons theory

We consider the classical field theory of 2+1-dimensional Yang-Mills-Chern-Simons theory on an arbitrary spatial manifold. We first define a gauge covariant transverse electric field strength, which together with the gauge covariant scalar magnetic field strength can be taken as coordinates on the classical phase space. We then determine the Poisson-Dirac bracket and find that these coordinates are canonically conjugate to each other. The Hamiltonian is non-polynomial when expressed in terms of these coordinates, but can be expanded in a power series in the coupling constant with polynomial coefficients.

Harmonic flow-field representations of quantum bits and gates

We describe a general procedure for mapping arbitrary n-qubit states to two-dimensional (2D) vector fields. The mappings use complex rational function representations of individual qubits, producing classical vector field configurations that can be interpreted in terms of 2D inviscid fluid flows or electric fields. Elementary qubits are identified with localized defects in 2D harmonic vector fields, and multiqubit states find natural field representations via complex superpositions of vector field products. In particular, separable states appear as highly symmetric flow configurations, making them both dynamically and visually distinct from entangled states. The resulting real-space representations of entangled qubit states enable an intuitive visualization of their transformations under quantum logic operations. We demonstrate this for the quantum Fourier transform and the period finding process underlying Shor's algorithm, along with other quantum algorithms. Due to its generic construction, the mapping procedure suggests the possibility of extending concepts such as entanglement or entanglement entropy to classical continuum systems, and thus may help guide new experimental approaches to information storage and nonstandard computation. Published by the American Physical Society 2024

Charge Scaling in Classical Force Fields for Lithium Ions in Polymers.

Polymer electrolytes are of interest for applications in energy storage. Molecular simulations of ion transport in polymer electrolytes have been widely used to study the conductivity in these materials. Such simulations have generally relied on classical force fields. A peculiar feature of such force fields has been that in the particular case of lithium ions (Li+), their charge must be scaled down by approximately 20% to achieve agreement with experimental measurements of ion diffusivity. In this work, we present first-principles calculations that serve to justify the charge-scaling factor and van der Waals interaction parameters for Li+ diffusion in poly(ethylene glycol) (PEO) with bistriflimide (TFSI-) counterions. Our results indicate that a scaling factor of 0.79 provides good agreement with DFT calculations over a relatively wide range of Li+ concentrations and temperatures, consistent with past reports where that factor was adjusted by trial and error. We also show that such a scaling factor leads to diffusivities that are in quantitative agreement with experimental measurements.

Machine learning force field for thermal oxidation of silicon.

Looking back at seven decades of highly extensive application in the semiconductor industry, silicon and its native oxide SiO2 are still at the heart of several technological developments. Recently, the fabrication of ultra-thin oxide layers has become essential for keeping up with trends in the down-scaling of nanoelectronic devices and for the realization of novel device technologies. With this comes a need for better understanding of the atomic configuration at the Si/SiO2 interface. Classical force fields offer flexible application and relatively low computational costs, however, suffer from limited accuracy. Ab initio methods give much better results but are extremely costly. Machine learning force fields (MLFF) offer the possibility to combine the benefits of both worlds. We train a MLFF for the simulation of the dry thermal oxidation process of a Si substrate. The training data are generated by density functional theory calculations. The obtained structures are in line with abinitio simulations and with experimental observations. Compared to a classical force field, the most recent reactive force field, the resulting configurations are vastly improved. Our potential is publicly available in an open-access repository.

Parameterization of a Fluctuating Charge Model for Complexes Containing 3d Transition Metals.

Metalloproteins widely exist in biology, playing pivotal roles in diverse life processes. Meanwhile, molecular dynamics (MD) simulations based on classical force fields has emerged as an important tool in scientific research. Partial charges are critical parameters within classical force fields and usually derived from quantum mechanical (QM) calculations. However, QM calculations are often time-consuming and prone to basis set dependence. Alternatively, fluctuating charge (FQ) models offer another avenue for partial charge derivation, which has significant speed advantages and can be used for large-scale screening. Building upon our previous work, which introduced an FQ model for zinc-containing complexes, herein we extend this model to include additional 3d transition metals which are important to the life sciences, namely chromium, manganese, iron, cobalt, and nickel. Employing CM5 charges as target for parametrization, our FQ model accurately reproduces partial charges for 3d metal complexes featuring biologically relevant ligands. Furthermore, by using atomic charges derived by our FQ model, MD simulations have been performed. These charges showed excellent performance in simulating proteomic metal sites housing multiple metal ions, specifically, a metalloprotein containing an iron-sulfur cluster and another containing a dimanganese metal site, showcasing comparable performance to those of RESP charges. We anticipate that our study can accelerate the parametrization of atomic charges for metalloproteins featuring 3d transition metals, thereby facilitating simulations of relevant systems.

Contour Detection Algorithm for α Phase Structure of TB6 Titanium Alloy fused with Multi-Scale Fretting Features

Aiming at the problems of inaccuracy in detecting the α phase contour of TB6 titanium alloy. By combining computer vision technology with human vision mechanisms, the spatial characteristics of the α phase can be simulated to obtain the contour accurately. Therefore, an algorithm for α phase contour detection of TB6 titanium alloy fused with multi-scale fretting features is proposed. Firstly, through the response of the classical receptive field model based on fretting and the suppression of new non-classical receptive field model based on fretting, the information maps of the α phase contour of the TB6 titanium alloy at different scales are obtained; then the information map of the smallest scale contour is used as a benchmark, the neighborhood is constructed to judge the deviation of other scale contour information, and the corresponding weight value is calculated; finally, Gaussian function is used to weight and fuse the deviation information, and the contour detection result of TB6 titanium alloy α phase is obtained. In the Visual Studio 2013 environment, 484 metallographic images with different temperatures, strain rates, and magnifications were tested. The results show that the performance evaluation F value of the proposed algorithm is 0.915, which can effectively improve the accuracy of α phase contour detection of TB6 titanium alloy.

Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning

Understanding structural flexibility of metal-organic frameworks (MOFs) via molecular dynamics simulations is crucial to design better MOFs. Density functional theory (DFT) and quantum-chemistry methods provide highly accurate molecular dynamics, but the computational overheads limit their use in long time-dependent simulations. In contrast, classical force fields struggle with the description of coordination bonds. Here we develop a DFT-accurate machine-learning spectral neighbor analysis potentials for two representative MOFs. Their structural and vibrational properties are then studied and tightly compared with available experimental data. Most importantly, we demonstrate an active-learning algorithm, based on mapping the relevant internal coordinates, which drastically reduces the number of training data to be computed at the DFT level. Thus, the workflow presented here appears as an efficient strategy for the study of flexible MOFs with DFT accuracy, but at a fraction of the DFT computational cost.

On the Role of the Interlayer Interactions in Atomistic Simulations of Kaolinite Clay

A systematic simulation study was performed to investigate the interlayer interactions in a 1:1 layered phyllosilicate clay, kaolinite. Atomistic simulations with classical realistic force fields (INTERFACE and ClayFF) were used to examine the influence of the individual non-bonded interactions on the interlayer binding in the kaolinite model system. By switching off selected pairwise interactions in the applied force fields (leaving the intralayer interactions intact), it was confirmed that the tetrahedral–octahedral-type pairwise interactions held the kaolinite plates together and that interlayer hydrogen bonding, modeled by Coulombic forces, played a dominant role in this. Furthermore, it was observed that the number of hydrogen bonds formed had a significant influence on the basal spacing, and thus there was a striking change in the layer–layer interaction strength when there were only two kaolinite plates in the system, rather than several plates, as in real kaolinite particles. Contrary to expectations, the dispersion forces of the studied force fields alone were found to be strong enough to hold the kaolinite plates together.

Error-robust hybrid atom-photon entangling gate via Gaussian soft control

Hybrid atom-photon gates play an important role for the realization of a quantum interface capable of mapping atomic states to photons for communication across quantum networks. Here, we propose a feasible theoretical scheme for implementing a hybrid atom-photon controlled-Z gate between an atom and a microwave photon in a superconducting coplanar waveguide resonator based on the Gaussian soft control technique. The gate protocol employs a classical auxiliary field that induces an atomic transition between one state of the atomic qubit and Rydberg states for obtaining strong coupling of the atom and microwave resonator. By tailoring the amplitude of this field with Gaussian temporal modulation, the gate performances are improved in various aspects. Numerical simulations demonstrate that the controlled-Z gate based on Gaussian soft control is resilient to the variation of the atom-photon coupling strength and deviation in the gate time, and it is less sensitive to the Rydberg level shifts caused by stray electric fields.

Structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations.

Deep eutectic solvents have recently gained significant attention as versatile and inexpensive materials with many desirable properties and a wide range of applications. In particular, their characteristics, similar to those of ionic liquids, make them a promising class of liquid electrolytes for electrochemical applications. In this study, we utilized a local equivariant neural network interatomic potential model to study a series of deep eutectic electrolytes based on lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) using molecular dynamics (MD) simulations. The use of equivariant features combined with strict locality results in highly accurate, data-efficient, and scalable interatomic potentials, enabling large-scale MD simulations of these liquids with first-principles accuracy. Comparing the structure of the liquids to the reported results from classical force field (FF) simulations indicates that ion-ion interactions are not accurately characterized by FFs. Furthermore, close contacts between lithium ions, bridged by oxygen atoms of two amide molecules, are observed. The computed cationic transport numbers (t+) and the estimated ratios of Li+-amide lifetime (τLi-amide) to the amide's rotational relaxation time (τR), combined with the ionic conductivity trend, suggest a more structural Li+ transport mechanism in the LiTFSI:urea mixture through the exchange of amide molecules. However, a vehicular mechanism could have a larger contribution to Li+ ion transport in the LiTFSI:N-methylacetamide electrolyte. Moreover, comparable diffusivities of Li+ cation and TFSI- anion and a τLi-amide/τR close to unity indicate that vehicular and solvent-exchange mechanisms have rather equal contributions to Li+ ion transport in the LiTFSI:acetamide system.

SNN using color-opponent and attention mechanisms for object recognition

The current spiking neural network (SNN) relies on spike-timing-dependent plasticity (STDP) primarily for shape learning in object recognition tasks, overlooking the equally critical aspect of color information. To address this gap, our study introduces an unsupervised variant of STDP that incorporates principles from color-opponency mechanisms (COM) and classical receptive fields (CRF) found in the biological visual system, facilitating the integration of color information during parameter updates within the SNN architecture. Our approach initially preprocesses images into two distinct feature maps: one for shape and another for color. Then, signals derived from COM and intensity concurrently drive the STDP process, thereby updating parameters associated with both color and shape feature maps. Furthermore, we propose a channel-wise attention mechanism to enhance differentiation among objects sharing similar shapes or colors. Specifically, this mechanism utilizes convolution to generate an output spike-wave, identifying a winner based on earliest spike timing and maximal potential. The winning kernel computes attention, which is then applied via convolution to each input image feature map, generating post-feature maps. A STDP-like normalization rule compares firing times between pre- and post-feature maps, dynamically adjusting channel weights to optimize object recognition during the training phase.We assessed the proposed algorithm using SNN with both single-layer and multi-layer architectures across three datasets. Experimental findings highlight its efficacy and superiority in complex object recognition tasks compared to state-of-the-art (SOTA) algorithms. Notably, our approach achieved a significant 20% performance improvement over the SOTA on the Caltech-101 dataset. Moreover, the algorithm is well-suited for hardware implementation and energy efficiency, leveraging a winner-selection mechanism based on the earliest spike time.

From the Cosserats mechanics backgrounds to modern field theory

In the paper, yet weekly known, Cosserats’ original four concepts as follow: the four-time unification of rigid body dynamics, statics of flexible rods, statics of elastic surfaces and 3D deformable body dynamics; the intrinsic formulation based on the local, von Helmholtz symmetry group of monodromy; the invariance under the Euclidean group. The concept of a set of low-dimensional branes immersed into Euclidean space are revalorized and explained in terms of the modern gauge field theory and the extended strings theory. Additionally, some useful mathematical tools that connect the continuum mechanics and the classical field theory (for instance, the convective coordinates, von Mises’ “Motorrechnung”, the Grassmann extensions, Euclidean invariance, etc.) are involved in the historical explanation that how the ideas were developing themself.

Non-degenerate metrics, hypersurface deformation algebra, non-anomalous representations and density weights in quantum gravity

Classical General Relativity is a dynamical theory of spacetime metrics of Lorentzian signature. In particular the classical metric field is nowhere degenerate in spacetime. In its initial value formulation with respect to a Cauchy surface the induced metric is of Euclidian signature and nowhere degenerate on it. It is only under this assumption of non-degeneracy of the induced metric that one can derive the hypersurface deformation algebra between the initial value constraints which is absolutely transparent from the fact that the inverse of the induced metric is needed to close the algebra. This statement is independent of the density weight that one may want to equip the spatial metric with. Accordingly, the very definition of a non-anomalous representation of the hypersurface deformation algebra in quantum gravity has to address the issue of non-degeneracy of the induced metric that is needed in the classical theory. In the Hilbert space representation employed in Loop Quantum Gravity (LQG) most emphasis has been laid to define an inverse metric operator on the dense domain of spin network states although they represent induced quantum geometries which are degenerate almost everywhere. It is no surprise that demonstration of closure of the constraint algebra on this domain meets difficulties because it is a sector of the quantum theory which is classically forbidden and which lies outside the domain of definition of the classical hypersurface deformation algebra. Various suggestions for addressing the issue such as non-standard operator topologies, dual spaces (habitats) and density weights have been proposed to address this issue with respect to the quantum dynamics of LQG. In this article we summarise these developments and argue that insisting on a dense domain of non-degenerate states within the LQG representation may provide a natural resolution of the issue thereby possibly avoiding the above mentioned non-standard constructions.

Dependence of Contextual Modulation in Macaque V1 on Interlaminar Signal Flow.

In visual cortex, neural correlates of subjective perception can be generated by modulation of activity from beyond the classical receptive field (CRF). In macaque V1, activity generated by nonclassical receptive field (nCRF) stimulation involves different intracortical circuitry than activity generated by CRF stimulation, suggesting that interactions between neurons across V1 layers differ under CRF and nCRF stimulus conditions. Using Neuropixels probes, we measured border ownership modulation within large, local populations of V1 neurons. We found that neurons in single columns preferred the same side of objects located outside of the CRF. In addition, we found that cross-correlations between pairs of neurons situated across feedback/horizontal and input layers differed between CRF and nCRF stimulation. Furthermore, independent of the comparison with CRF stimulation, we observed that the magnitude of border ownership modulation increased with the proportion of information flow from feedback/horizontal layers to input layers. These results demonstrate that the flow of signals between layers covaries with the degree to which neurons integrate information from beyond the CRF.

The pericardium forms as a distinct structure during heart formation.

The heart integrates diverse cell lineages into a functional unit, including the pericardium, a mesothelial sac that supports heart movement, homeostasis, and immune responses. However, despite its critical roles, the developmental origins of the pericardium remain uncertain due to disparate models. Here, using live imaging, lineage tracking, and single-cell transcriptomics in zebrafish, we find the pericardium forms within the lateral plate mesoderm from dedicated anterior mesothelial progenitors and distinct from the classic heart field. Imaging of transgenic reporters in zebrafish documents lateral plate mesoderm cells that emerge lateral of the classic heart field and among a continuous mesothelial progenitor field. Single-cell transcriptomics and trajectories of hand2-expressing lateral plate mesoderm reveal distinct populations of mesothelial and cardiac precursors, including pericardial precursors that are distinct from the cardiomyocyte lineage. The mesothelial gene expression signature is conserved in mammals and carries over to post-natal development. Light sheet-based live-imaging and machine learning-supported cell tracking documents that during heart tube formation, pericardial precursors that reside at the anterior edge of the heart field migrate anteriorly and medially before fusing, enclosing the embryonic heart to form a single pericardial cavity. Pericardium formation proceeds even upon genetic disruption of heart tube formation, uncoupling the two structures. Canonical Wnt/β-catenin signaling modulates pericardial cell number, resulting in a stretched pericardial epithelium with reduced cell number upon canonical Wnt inhibition. We connect the pathological expression of secreted Wnt antagonists of the SFRP family found in pediatric dilated cardiomyopathy to increased pericardial stiffness: sFRP1 in the presence of increased catecholamines causes cardiomyocyte stiffness in neonatal rats as measured by atomic force microscopy. Altogether, our data integrate pericardium formation as an independent process into heart morphogenesis and connect disrupted pericardial tissue properties such as pericardial stiffness to pediatric cardiomyopathies.

Bound-constrained optimization using Lagrange multiplier for a length scale insensitive phase field fracture model

The classical phase field model using the second-order geometric function α(φ)=φ2 (i.e., AT2 model), where φ∈0,1 is an auxiliary phase field variable representing material damage state, has wide applications in static and dynamic scenarios for brittle materials, but nonlinearity and inelasticity are found in its stress–strain curve. The phase field model using the linear geometric function α(φ)=φ (i.e., AT1 model), can avoid this, and a linear elastic threshold is available in its stress–strain curve. However, both AT2 and AT1 models are length scale sensitive phase field models, which could have difficulty in adjusting fracture strength and crack band simultaneously through a single parameter (the length scale). In this paper, a generalized quadratic geometric function (linear combination of AT1 and AT2 models) is used in the phase field model, where the extra parameter in this geometric function makes it a length scale insensitive phase field model. Similar to the AT1 model, negative phases can happen in the proposed generalized quadratic geometric function model. To solve this problem, a bound-constrained optimization using the Lagrange multiplier is derived, and the Karush–Kuhn–Tucker (KKT) conditions change from strain energy and maximum history strain energy (an indirect method acting on phase) to phase and Lagrange multiplier (a direct method acting on phase). Several simulations successfully validated the proposed model. A single element analysis and a bar under cyclic loading show the different stress–strain curves obtained from different models. A simulation of Mode I Brazilian test is compared with the experiment conducted by the authors, and two more simulations of Mode II shear test and mixed mode PMMA tensile test are compared with results from the literature.

Hybrid Quantum Mechanical, Molecular Mechanical, and Machine Learning Potential for Computing Aqueous-Phase Adsorption Free Energies on Metal Surfaces.

Performing reliable computer simulations of elementary processes occurring at metal-water interfaces is pivotal for novel catalyst design in sustainable energy applications. Computational catalyst design hinges on the ability to reliably and efficiently compute the potential energy surface (PES) of the system. Due to the large system sizes needed for studying processes at liquid water-metal interfaces, these systems can currently not be described using density functional theory (DFT). In this work, we used a hybrid quantum mechanical, molecular mechanical, and machine learning potential for studying the adsorption behavior of phenol, atomic hydrogen, 2-butanol, and 2-butanone on the (0001) facet of Ru under reducing conditions when Ru is not oxidized. Specifically, we describe the adsorbate and the surrounding metal atoms at the DFT level of theory. Here, we also considered the electrostatic field effect of the water molecules on adsorbate-metal interactions. Next, for the water-water and water-adsorbate interactions, we used established classical force fields. Finally, for the water-Ru surface interaction, for which no reliable force fields have been published, we used Behler-Parrinello high-dimensional neural network potentials (HDNNPs). Employing this setup, we used our explicit solvation for metal surface (eSMS) approach to compute the aqueous-phase effect on the low-coverage adsorption of selected molecules and atoms on the (0001) facet of Ru. In agreement with previous experimental and computational studies of oxygenated molecules over transition metal facets, we found that liquid water destabilizes the tested adsorbates on Ru(0001). Interestingly, our findings indicate that adsorbates on Ru are less affected by the presence of an aqueous phase than on other transition metals (e.g., Pt), highlighting the necessity of experimental investigations of Ru-based catalytic systems in liquid water.