Configuration Space Research Articles

What is a degree of freedom? Configuration spaces and their topology

What is a degree of freedom? Configuration spaces and their topology

Non-thermal fusion burning processes, relevant collective modes and gained perspectives

Abstract New tridimensional plasma structures, that are oscillatory and classified as non-separable ballooning modes, can emerge in inhomogeneous plasmas and undergo resonant mode-particle interactions, e.g., with a minority population, that can lead them to modify their spatial profiles. Thus, unlike the case of previously known ballooning modes their amplitudes are not separable functions of time and space. The relevant resonance conditions are intrinsically different from those of the well-known Landau conditions for (ordinary) plasma waves: they involve the mode geometry and affect different regions of the distribution in momentum space at different positions in configuration space. A process for a transfer of energy among different particle populations is envisioned.

Robertson’s conjecture and universal finite generation in the homology of graph braid groups

We formulate a categorification of Robertson’s conjecture analogous to the categorical graph minor conjecture of Miyata–Proudfoot–Ramos. We show that these conjectures imply the existence of a finite list of atomic graphs generating the homology of configuration spaces of graphs—in fixed degree, with a fixed number of particles, under topological embeddings. We explain how the simplest case of our conjecture follows from work of Barter and Proudfoot–Ramos, implying that the category of cographs is Noetherian, a result of potential independent interest.

Impact of High-density Planting Spacing on Physiological Traits of Cocoa Grown under Coconut Trees

High-density planting (HDP) maximizes land productivity. Optimizing cocoa spacing under coconut trees enhances physiological traits and yield potential. Despite cocoa's integration into coconut agroforestry, spacing's impact on cocoa physiology is unclear. Studying this influence provides insights for better planting strategies, improved crop performance, and sustainable cocoa production in agroforestry settings. The experiment was conducted at the Coconut Farm of the Horticultural College and Research Institute, Tamil Nadu Agricultural University, Coimbatore. Employing a Randomized Block Design (RBD) with eight treatments replicated three times, the study aimed to explore how different spacing levels influence physiological traits in cocoa cultivation. The treatments involved in the experiment included a double row of cocoa planted between two rows of coconut trees, with spacing configurations as follows: T1 (3m x 1.2m), T2 (3m x 2m), T3 (3m x 2.5m), and T4 (3m x 3m). Additionally, a single row of cocoa between two coconut rows was examined, with spacings represented by T5 (1.5m), T6 (2m), T7 (2.5m), and T8 (3m). Results indicated distinct patterns among spacing treatments, with significant differences observed in various physiological characteristics. Notably, T1 (3m x 1.2m) demonstrated the highest leaf area (462.71cm2) and leaf area index (4.85), while T8 (3m) exhibited the highest light interception (74.12%). Additionally, T3 (3m x 2.5m) showcased the highest chlorophyll index (40.52) in cocoa leaves. These findings underscore the importance of spacing configurations in influencing key physiological parameters in cocoa cultivation, providing valuable insights for optimizing planting practices.

Nano‐High Entropy Materials in Electrocatalysis

AbstractHigh entropy materials (HEMs) compositing of at least five elements have gained widespread attention in the field of electrocatalysis due to their tunable activities and high stability. These intrinsic properties can be further highlighted when the size of HEMs comes to the nanoscale. In nanostructured HEMs, the fascinating properties including large composition space, multi‐element synergy, and high configuration entropy are expected to endow nano‐HEMs with excellent catalytic activity and stability, thus providing greater potential for the design of advanced electrocatalysts. In this review, nano‐HEMs are differentiated in detail in dimensions and the common synthesis methods are summarized. Additionally, from the perspective of the complex nanostructure‐performance relationship, the applications of nano‐HEMs in electrocatalysis systems, including water‐splitting (hydrogen evolution reaction (HER), oxygen evolution reaction (OER)), hydrogen oxidation reaction (HOR), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), nitrogen reduction reaction (NRR) and alcohol oxidation reaction (AOR) are discussed. Finally, the main challenges faced by nano‐HEMs electrocatalysis are underscored. This review is expected to provide more insights into understanding and developing efficient electrocatalytic materials for practical applications.

Assessing the Accuracy of Quantum Dynamics Performed in the Time-Dependent Basis Representation.

A full quantum-mechanical (QM) description of large amplitude nuclear motion, associated with chemical reactions or isomerization of high-dimensional molecular systems, is inherently challenging due to the exponential scaling of the QM complexity with system size. To ameliorate the scaling bottleneck in studies of realistic systems, typically modeled in the configuration space, the nuclear wave functions are represented in terms of time-dependent basis functions. Such bases are expected to give an accurate description with a modest number of basis functions employed, by adapting them to the wave function solving the time-dependent Schrödinger equation. It is not, however, straightforward to estimate the accuracy of the resulting solution: in QM the energy conservation, a convenient such measure for a classical trajectory evolving in a time-independent potential, is not a sufficient criterion of the dynamics' accuracy. In this work, we argue that the expectation value of the Hamiltonian's "variance", quantifying the basis completeness, is a suitable practical measure of the quantum dynamics' accuracy. Illustrations are given for several chemistry-relevant test systems, modeled employing time-independent as well as time-dependent bases, including the coupled and variational coherent states methods and the quantum-trajectory guided adaptable Gaussians (QTAG) as the latter basis type. A novel semilocal definition of the QTAG basis time-evolution for placing the basis functions "in the right place at the right time" is also presented.

Relating behaviour of data-aware process models

Data Petri nets (DPNs) have gained traction as a model for data-aware processes, thanks to their ability to balance simplicity with expressiveness, and because they can be automatically discovered from event logs. While model checking techniques for DPNs have been studied, more complex analysis tasks that are highly relevant for BPM are beyond methods known in the literature. We focus here on equivalence and inclusion of process behaviour with respect to language and configuration spaces, optionally taking data into account. Such comparisons are important in the context of key process mining tasks, namely process repair and discovery, and related to conformance checking. To solve these tasks, we propose approaches for bounded DPNs based on constraint graphs, which are faithful abstractions of the reachable state space. Though the considered verification tasks are undecidable in general, we show that our method is a decision procedure DPNs that admit a finite history set. This property guarantees that constraint graphs are finite and computable, and was shown to hold for large classes of DPNs that are mined automatically, and DPNs presented in the literature. The new techniques are implemented in the tool ada, and an evaluation proving feasibility is provided.

Quantum computing and tensor networks for laminate design: A novel approach to stacking sequence retrieval

As with many tasks in engineering, structural design frequently involves navigating complex and computationally expensive problems. A prime example is the weight optimization of laminated composite materials, which to this day remains a formidable task, due to an exponentially large configuration space and non-linear constraints. The rapidly developing field of quantum computation may offer novel approaches for addressing these intricate problems. However, before applying any quantum algorithm to a given problem, it must be translated into a form that is compatible with the underlying operations on a quantum computer. Our work specifically targets stacking sequence retrieval with lamination parameters, which is typically the second phase in a common bi-level optimization procedure for minimizing the weight of composite structures. To adapt stacking sequence retrieval for quantum computational methods, we map the possible stacking sequences onto a quantum state space. We further derive a linear operator, the Hamiltonian, within this state space that encapsulates the loss function inherent to the stacking sequence retrieval problem. Additionally, we demonstrate the incorporation of manufacturing constraints on stacking sequences as penalty terms in the Hamiltonian. This quantum representation is suitable for a variety of classical and quantum algorithms for finding the ground state of a quantum Hamiltonian. For a practical demonstration, we performed numerical state-vector simulations of two variational quantum algorithms and additionally chose a classical tensor network algorithm, the DMRG algorithm, to numerically validate our approach. For the DMRG algorithm, we derived a matrix product operator representation of the loss function Hamiltonian and the penalty terms. Although this work primarily concentrates on quantum computation, the application of tensor network algorithms presents a novel quantum-inspired approach for stacking sequence retrieval.

Singlet-Triplet Inversion in Triangular Boron Carbon Nitrides.

The discovery of singlet-triplet (ST) inversion in some π-conjugated triangle-shaped boron carbon nitrides is a remarkable breakthrough that defies Hund's first rule. Deeply rooted in strong electron-electron interactions, ST inversion has garnered significant interest due to its potential to revolutionize triplet harvesting in organic LEDs. Using the well-established Pariser-Parr-Pople model for correlated electrons in π-conjugated systems, we employ a combination of CISDT and restricted active space configuration interaction calculations to investigate the photophysics of several triangular boron carbon nitrides. Our findings reveal that ST inversion in these systems is primarily driven by a network of alternating electron-donor and electron-acceptor groups in the molecular rim, rather than by the triangular molecular structure itself.

Obstacle avoidance planning for industrial robots based on singular manifold splitting configuration space

Obstacle avoidance planning for industrial robots based on singular manifold splitting configuration space

Assessing the efficacy of green walls versus street green lanes in mitigating air pollution: A critical evaluation

Urban green spaces, particularly street trees and greenways, are increasingly recognized as tools for mitigating air pollution. However, their effectiveness can vary depending on their configuration and urban canyon geometry. This study, aimed at providing practical solutions, assessed the impact of different green space configurations, including street trees, green lanes, green walls, and hedges, on particulate matter (PM) concentrations within urban canyons of varying aspect ratios by employing environmental simulations and wind tunnel tests performed in ENVI-met software.Results demonstrate that green walls significantly outperform street trees in reducing PM concentrations by up to 43%, attributing this to reduced air flow disruption. Green walls also create a more uniform PM distribution within canyons, decreasing concentration differences by 31% compared to green (tree) lanes. Conversely, dense tree lanes exacerbate PM deposition due to wind flow interference.These findings suggest that incorporating green walls into urban design can be a highly effective strategy for improving air quality within densely built environments.

Nonequivalence of Carroll limits in relativistic string theory

We construct new Carroll strings in flat space by considering the Carroll limit of equivalent relativistic string theories at the classical level. In the limit these Carroll strings are no longer equivalent and have different degrees of freedom. This fact is due to a general phenomenon that the configuration space and canonical Lagrangians are no longer equivalent after a singular redefinition of the variables and of the parameters of the starting Lagrangians. Published by the American Physical Society 2024

Accelerated convergence via adiabatic sampling for adsorption and desorption processes.

Under isothermal conditions, phase transitions occur through a nucleation event when conditions are sufficiently close to coexistence. The formation of a nucleus of the new phase requires the system to overcome a free energy barrier of formation, whose height rapidly rises as supersaturation decreases. This phenomenon occurs both in the bulk and under confinement and leads to a very slow kinetics for the transition, ultimately resulting in hysteresis, where the system can remain in a metastable state for a long time. This has broad implications, for instance, when using simulations to predict phase diagrams or screen porous materials for gas storage applications. Here, we leverage simulations in an adiabatic statistical ensemble, known as adiabatic grand-isochoric ensemble (μ, V, L) ensemble, to reach equilibrium states with a greater efficiency than its isothermal counterpart, i.e., simulations in the grand-canonical ensemble. For the bulk, we show that at low supersaturation, isothermal simulations converge slowly, while adiabatic simulations exhibit a fast convergence over a wide range of supersaturation. We then focus on adsorption and desorption processes in nanoporous materials, assess the reliability of (μ, V, L) simulations on the adsorption of argon in IRMOF-1, and demonstrate the efficiency of adiabatic simulations to predict efficiently the equilibrium loading during the adsorption and desorption of argon in MCM-41, a system that exhibits significant hysteresis. We provide quantitative measures of the increased rate of convergence when using adiabatic simulations. Adiabatic simulations explore a wide temperature range, leading to a more efficient exploration of the configuration space.

Geometrical description and Faddeev-Jackiw quantization of electrical networks

In lumped-element electrical circuit theory, the problem of solving Maxwell's equations in the presence of media is reduced to two sets of equations, the constitutive equations encapsulating local geometry and dynamics of a confined energy density, and the Kirchhoff equations enforcing conservation of charge and energy in a larger, topological, scale. We develop a new geometric and systematic description of the dynamics of general lumped-element electrical circuits as first order differential equations, derivable from a Lagrangian and a Rayleigh dissipation function. Through the Faddeev-Jackiw method we identify and classify the singularities that arise in the search for Hamiltonian descriptions of general networks. The core of our solution relies on the correct identification of the reduced manifold in which the circuit state is expressible, e.g., a mix of flux and charge degrees of freedom, including the presence of compact ones. We apply our fully programmable method to obtain (canonically quantizable) Hamiltonian descriptions of nonlinear and nonreciprocal circuits which would be cumbersome/singular if pure node-flux or loop-charge variables were used as a starting configuration space. We also propose a specific assignment of topology for the branch variables of energetic elements, that when used as input to the procedure gives results consistent with classical descriptions as well as with spectra of more involved quantum circuits. This work unifies diverse existent geometrical pictures of electrical network theory, and will prove useful, for instance, to automatize the computation of exact Hamiltonian descriptions of superconducting quantum chips.

The structure of water-ammonia mixtures from classical and abinitio molecular dynamics.

The structure of aqueous ammonia solutions is investigated through classical molecular dynamics (MD) and abinitio molecular dynamics (AIMD) simulations. We have preliminarily compared three well-known classical force fields for liquid water (SPC, SPC/E, and TIP4P) in order to identify the most accurate one in reproducing AIMD results obtained at the Generalized Gradient Approximation (GGA) and meta-GGA levels of theory. Liquid ammonia has been simulated by implementing an optimized force field recently developed by Chettiyankandy et al. [Fluid Phase Equilib. 511, 112507 (2020)]. Analysis of the radial distribution functions for different ammonia concentrations reveals that the three water force fields provide comparable estimates of the mixture structure, with the SPC/E performing slightly better. Although a fairly good agreement between MD and AIMD is observed for conditions close to the equimolarity, at lower ammonia concentrations, important discrepancies arise, with classical force fields underestimating the number and strength of H-bonds between water molecules and between water and ammonia moieties. Here, we prove that these drawbacks are rooted in a poor sampling of the configurational space spanned by the hydrogen atoms lying in the H-bonds of H2O⋯H2O and, more critically, H2O⋯NH3 neighbors due to the lack of polarization and charge transfer terms. This way, non-polarizable classical force fields underestimate the proton affinity of the nitrogen atom of ammonia in aqueous solutions, which plays a key role under realistic dilute ammonia conditions. Our results witness the need for developing more suited polarizable models that are able to take into account these effects properly.

The explicit bonding reaction ensemble Monte Carlo method.

We present the explicit bonding Reaction ensemble Monte Carlo (eb-RxMC) method, designed to sample reversible bonding reactions in macromolecular systems in thermodynamic equilibrium. Our eb-RxMC method is based on the reaction ensemble method; however, its implementation differs from the latter by the representation of the reaction. In the eb-RxMC implementation, we are adding or deleting bonds between existing particles, instead of inserting or deleting particles with different chemical identities. This new implementation makes the eb-RxMC method suitable for simulating the formation of reversible linkages between macromolecules, which would not be feasible with the original implementation. To enable coupling of our eb-RxMC algorithm with molecular dynamics algorithm for the sampling of the configuration space, we biased the sampling of reactions only within a certain inclusion radius. We validated our algorithm using a set of ideally behaving systems undergoing dimerization and polycondensation reactions, for which analytical results are available. For dimerization reactions with various equilibrium constants and initial compositions, the degree of conversion measured in our simulations perfectly matched the reference values given by the analytical equations. We also showed that this agreement is not affected by the arbitrary choice of the inclusion radius or the stiffness of the harmonic bond potential. Next, we showed that our simulations can correctly match the analytical results for the distribution of the degree of polymerization and end-to-end distance of ideal chains in polycondensation reactions. Altogether, we demonstrated that our eb-RxMC simulations correctly sample both reaction and configuration spaces of these reference systems, opening the door to future simulations of more complex interacting macromolecular systems.

Towards a configurable and non-hierarchical search space for NAS

Neural Architecture Search (NAS) outperforms handcrafted Neural Network (NN) design. However, current NAS methods generally use hard-coded search spaces, and predefined hierarchical architectures. As a consequence, adapting them to a new problem can be cumbersome, and it is hard to know which of the NAS algorithm or the predefined hierarchical structure impacts performance the most. To improve flexibility, and be less reliant on expert knowledge, this paper proposes a NAS methodology in which the search space is easily customizable, and allows for full network search. NAS is performed with Gaussian Process (GP)-based Bayesian Optimization (BO) in a continuous architecture embedding space. This embedding is built upon a Wasserstein Autoencoder, regularized by both a Maximum Mean Discrepancy (MMD) penalization and a Fully Input Convex Neural Network (FICNN) latent predictor, trained to infer the parameter count of architectures. This paper first assesses the embedding’s suitability for optimization by solving 2 computationally inexpensive problems: minimizing the number of parameters, and maximizing a zero-shot accuracy proxy. Then, two variants of complexity-aware NAS are performed on CIFAR-10 and STL-10, based on two different search spaces, providing competitive NN architectures with limited model sizes.

FF-RRT*: A Sampling-Based Planner for Multirobot Global Formation Path Planning

Abstract This article presents a collision-free global path planning approach for multirobot systems called flexible formation—rapidly exploring randomized trees* (FF-RRT*). The algorithm is based on RRT* and provides a flexible and efficient representation of the formation geometry independent of the number of robots. It is designed to generate optimal paths for multirobot systems in a five-dimensional configuration space comprising the formation centroid, orientation, and scaling. An affine transformation is used to convert the path in the configuration space to the workspace of the robots. The proposed method employs a new distance function that eliminates the need for tuning weights and a sampling scheme for scaling factors to avoid inter-robot collisions. FF-RRT* is experimentally demonstrated using nonholonomic wheeled mobile robots and is effective in planning the collective motion of the robots. Robots could collectively reach goals by squeezing through narrow gaps and splitting and merging around large obstacles.

Smart prediction of rock crack opening displacement from noisy data recorded by distributed fiber optic sensing

The commonly used method for estimating crack opening displacement (COD) is based on analytical models derived from strain transferring. However, when large background noise exists in distributed fiber optic sensing (DFOS) data, estimating COD through an analytical model is very difficult even if the DFOS data have been denoised. To address this challenge, this study proposes a machine learning (ML)-based methodology to complete rock’s COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models. The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models. To ensure that the best hyper-parameters will not be missing, the configuration space in Hyperopt is specified by probability distribution. The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness. The proposed models are compared each other in terms of goodness of fit and mean squared error. The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.

A Two Phases Multiobjective Trajectory Optimization Scheme for Multi-UGVs in the Sight of the First Aid Scenario.

Timely delivery of first aid supplies is significant to saving lives when an accident happens. Among the promising solutions provided for such scenarios, the application of unmanned vehicles has attracted ever more attention. However, such scenarios are often very complex, while the existing studies have not fully addressed the trajectory optimization problem of multiple unmanned ground vehicles (multi-UGVs) against the scenario. This study focuses on multi-UGVs trajectory optimization in the sight of first aid supply delivery tasks in mass accidents. A two-stage completely decoupling fuzzy multiobjective optimization strategy is designed. On the first stage, with the proposed timescale involved tridimensional tunneled collision-free trajectory (TITTCT) algorithm, collision-free coarse tunnels are build within a tridimensional coordinate system, respectively, for the UGVs as the corresponding configuration space for a further multiobjective optimization. On the second stage, a fuzzy multiobjective transcription method is designed to solve the decoupled optimal control problem (OCP) within the configuration space with the consideration of priority constrains. Following the two-stage design, the computational time is significantly reduced when achieving an optimal solution of the multi-UGV trajectory planning, which is crucial in a first aid task. In addition, other objectives are optimized with the aspiration level reflected. Simulation studies and experiments have been curried out to testify the effectiveness and the improved computational performance of the proposed design.