Energy Minimization Research Articles

Cascaded variational quantum eigensolver algorithm

We present a cascaded variational quantum eigensolver algorithm that only requires the execution of a set of quantum circuits once rather than at every iteration during the parameter optimization process, thereby increasing the computational throughput. This algorithm uses a quantum processing unit to probe the needed probability mass functions and a classical processing unit perform the remaining calculations, including the energy minimization. The ansatz form does not restrict the Fock space and provides full control over the trial state, including the implementation of symmetry and other physically motivated constraints. Published by the American Physical Society 2024

Intrinsically Multistable Soft Actuator Driven by Mixed-Mode Snap-Through Instabilities.

Actuators utilizing snap-through instabilities are widely investigated for high-performance fast actuators and shape reconfigurable structures owing to their rapid response and limited reliance on continuous energy input. However, prevailing approaches typically involve a combination of multiple bistable actuator units and achieving multistability within a single actuator unit still remains an open challenge. Here, a soft actuator is presented that uses shape memory alloy (SMA) and mixed-mode elastic instabilities to achieve intrinsically multistable shape reconfiguration. The multistable actuator unit consists of six stable states, including two pure bendingstates and four bend-twist states. The actuator is composed of a pre-stretched elastic membrane placed between two elastomeric frames embedded with SMA coils. By controlling the sequence and duration of SMA activation, the actuator is capable of rapid transition between all six stable states within hundreds of milliseconds. Principles of energy minimization are used to identify actuation sequences for various types of stable state transitions. Bendingand twisting angles corresponding to various prestretch ratios are recorded based on parameterizations of the actuator's geometry. To demonstrate its application in practical conditions, the multistable actuator is used to perform visual inspection in a confined space, light source tracking during photovoltaic energy harvesting, and agilecrawling.

Glassy-like Transients in Semiconductor Nanomaterials.

Glassy behavior is manifested by three time-dependent characteristics of a dynamic physical property. Such behaviors have been found in the electrical conductivity transients of various disordered systems, but the mechanisms that yield the glassy behavior are still under intensive debate. The focus of the present work is on the effect of the quantum confinement (QC) and the Coulomb blockade (CB) effects on the experimentally observed glassy-like behavior in semiconductor nanomaterials. Correspondingly, we studied the transient electrical currents in semiconductor systems that contain CdSe or Si nanosize crystallites, as a function of that size and the ambient temperature. In particular, in contrast to the more commonly studied post-excitation behavior in electronic glassy systems, we have also examined the current transients during the excitation. This has enabled us to show that the glassy behavior is a result of the nanosize nature of the studied systems and thus to conclude that the observed characteristics are sensitive to the above effects. Following this and the temperature dependence of the transients, we derived a more detailed macroscopic and microscopic understanding of the corresponding transport mechanisms and their glassy manifestations. We concluded that the observed electrical transients must be explained not only by the commonly suggested principle of the minimization of energy upon the approach to equilibrium, as in the mechanical (say, viscose) glass, but also by the principle of minimal energy dissipation by the electrical current which determines the percolation network of the electrical conductivity. We further suggest that the deep reason for the glassy-like behavior that is observed in the electrical transients of the nanomaterials studied is the close similarity between the localization range of electrons due to the Coulomb blockade and the caging range of the uncharged atomic-size particles in the classical mechanical glass. These considerations are expected to be useful for the understanding and planning of semiconductor nanodevices such as corresponding quantum dot memories and quantum well MOSFETs.

Strain-dependent transition of the relaxation dynamics in metallic glasses

Non-exponential relaxation is pervasive in glassy systems and intimately related to unique thermodynamic features, such as glass transition and aging; however, the underlying mechanisms remain unclear. The time scale of non-exponential relaxation goes beyond the time limit (nanosecond) of classic molecular dynamics simulation. Thus, the advanced time scaling atomistic approach is necessary to interpret the relaxation mechanisms at the experimental timescale. Here, we adopted autonomous basin climbing (ABC) to evaluate the long-time stress relaxation. At the same time, based on the energy minimization principle, we carried out simulations at continuum levels on the long-time stress relaxation kinetics of Cu–Zr metallic glass over timescales greater than 100 s. Combined with atomistic and continuum models, we demonstrate that a strain-dependent transition from compressed to stretched exponentials would happen, consistent with recent experimental observations on metallic glasses. Further examination of the spatial and temporal correlations of stress and plastic strain reveals two predominant driving forces: the thermal energy gradient governs in the compressed regime and leads to a release of the local internal stress; in the stretched regime, the strain energy gradient rules and causes long-range structural rearrangements. The discovery of the competition between two driving forces advances our understanding of the nature of aging dynamics in disordered solids.

SparseRNAfolD: optimized sparse RNA pseudoknot-free folding with dangle consideration

MotivationComputational RNA secondary structure prediction by free energy minimization is indispensable for analyzing structural RNAs and their interactions. These methods find the structure with the minimum free energy (MFE) among exponentially many possible structures and have a restrictive time and space complexity (O(n3)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^3)$$\\end{document} time and O(n2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^2)$$\\end{document} space for pseudoknot-free structures) for longer RNA sequences. Furthermore, accurate free energy calculations, including dangle contributions can be difficult and costly to implement, particularly when optimizing for time and space requirements.ResultsHere we introduce a fast and efficient sparsified MFE pseudoknot-free structure prediction algorithm, SparseRNAFolD, that utilizes an accurate energy model that accounts for dangle contributions. While the sparsification technique was previously employed to improve the time and space complexity of a pseudoknot-free structure prediction method with a realistic energy model, SparseMFEFold, it was not extended to include dangle contributions due to the complexity of computation. This may come at the cost of prediction accuracy. In this work, we compare three different sparsified implementations for dangle contributions and provide pros and cons of each method. As well, we compare our algorithm to LinearFold, a linear time and space algorithm, where we find that in practice, SparseRNAFolD has lower memory consumption across all lengths of sequence and a faster time for lengths up to 1000 bases.ConclusionOur SparseRNAFolD algorithm is an MFE-based algorithm that guarantees optimality of result and employs the most general energy model, including dangle contributions. We provide a basis for applying dangles to sparsified recursion in a pseudoknot-free model that has the potential to be extended to pseudoknots.

Comparative dynamic optimization study of batch hydrothermal liquefaction of two microalgal strains for economic bio-oil production

This work presents dynamic optimization strategies of batch hydrothermal liquefaction of two microalgal species, Aurantiochytrium sp. KRS101 and Nannochloropsis sp. to optimize the reactor temperature profiles. Three dynamic optimization problems are solved to maximize the endpoint biocrude yield, minimize the final time, and minimize the reactor thermal energy. The biocrude maximization and time minimization problems demonstrated 11% and 6.18% increment in the optimal biocrude yields and reduction of 78.2% and 61.66% in batch times compared to the base cases for the microalgae with higher lipid and protein fractions, respectively. The energy minimization problem revealed a significant reduction in the reactor thermal energies to generate the targeted biocrude yields compared to the biocrude maximization. Therefore, the identified optimal temperature trajectories outperformed the conventional fixed temperature profiles and could improve the overall economics of the batch bio-oil production from the algal-based biorefineries by significantly enhancing the reactor performance.

Learning-Based Cloud Server Configuration for Energy Minimization Under Reliability Constraint

Cloud computing has attracted wide attention from both academia and industry, since it can provide flexible and on-demand hardware and software resources as services. Energy consumption of cloud servers is the main concern of cloud service providers since reducing energy consumption can bring them a lower operation cost (and hence a higher profit) and alleviate carbon footprints to the environment. Typically, the common power management techniques for enhancing energy efficiency would make cloud servers more vulnerable to soft errors and hence adversely impact the quality of services. Thus, reliability cannot be ignored in the design of methodologies for improving the energy efficiency of cloud servers. In this article, we aim to minimize the energy consumption of cloud servers under the soft-error reliability constraint by configuring the size and speed of servers. Specifically, we first derive the expected reliability based energy consumption of cloud servers to formulate the reliability-constrained energy minimization problem. We then leverage the reinforcement learning technique to obtain an optimal server configuration solution that maximizes system energy efficiency while maintaining the system reliability constraint. Finally, we perform extensive simulation experiments to analyze the relationship between system energy consumption and server configuration under varying arrival rates and execution requirements of service requests. Comparative experiments are also performed to validate the efficacy of the proposed learning-based server configuration scheme. Results show that compared to a benchmark method, the energy saved by the proposed scheme can reach up to 31.5%.

Phantom chain simulations for fracture of end-linking networks

Despite numerous studies, the relationship between network structure and fracture remains unclear. In this study, the fracture properties of end-linking networks were compared with those of loop-free analogs made from star prepolymers by performing phantom chain simulations. The networks were created from equilibrated sols of stoichiometric mixtures of linear prepolymers and f-functional linkers through end-linking reactions using Brownian dynamics schemes. The examined networks, with various f values (between 3 and 8) and strand-connection rates (φs), were evaluated in terms of the primary loop fraction and the cycle rank ξ. These structural characteristics were consistent with mean-field theories that assume independent reactions. Energy minimization and uniaxial stretch were applied to the networks until they broke without Brownian motion. The fracture characteristics, including strain at break (εb), stress at break (σb), and work for fracture (Wb), were obtained from stress-strain curves. The end-linking networks exhibited larger εb and smaller σb and Wb than those for star networks due to primary loops, at the same set of f and φs. However, εb, σb/νbr and Wb/νbr (with νbr being the branch point density) lie on the same master curves as those for star networks if they are plotted against ξ. This result implies that the fracture of end-linking networks is essentially the same as that for star analogs, and the effects of primary loops are embedded in ξ.

Energy or Accuracy? Near-Optimal User Selection and Aggregator Placement for Federated Learning in MEC

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user privacy, federated learning (FL) is emerging as a promising technique to train a machine learning model using the datasets of UEs locally without uploading the datasets to a central location. Customers require to train machine learning models based on different datasets of UEs, through issuing FL requests that are implemented by FL services in a mobile edge computing (MEC) network. A key challenge of enabling FL in MEC networks is how to minimize the energy consumption of implementing FL requests while guaranteeing the accuracy of machine learning models, given that the availabilities of UEs usually are uncertain. In this paper, we investigate the problem of energy minimization for FL in an MEC network with uncertain availabilities of UEs. We first consider the energy minimization problem for a single FL request in an MEC network. We then propose a novel optimization framework for the problem with a single FL request, which consists of (1) an online learning algorithm with a bounded regret for the UE selection, by considering various contexts (side information) that influence energy consumption; and (2) an approximation algorithm with an approximation ratio for the aggregator placement for a single FL request. We thirdly deal with the problem with multiple FL requests, for which we devise an online learning algorithm with a bounded regret. We finally evaluate the performance of the proposed algorithms by extensive experiments. Experimental results show that the proposed algorithms outperform their counterparts by reducing at least 13% of the total energy consumption while achieving the same accuracy.

Optimal Cooperative Eco-Driving of Multitrain With TLET Comprehensive System

Aiming to minimize the total electricity usage in realistic operation of urban rail transit (URT) trains, this paper establishes the coupled model for the train-line-electric network-timetable (TLET) comprehensive system, and proposes a multi-train cooperative eco-driving method to achieve the system energy optimal. The dynamic power flow within the DC railway system with multiple trains is analyzed by modeling the traction power supply system (TPSS), and an improved power flow calculation (PFC) method is proposed to calculate the dynamic power flow distribution. To achieve the total substation energy minimization, a space-time-speed (STS) three-dimensional network is presented to transform continuous train motion process into discrete states on space-speed plane, and a multiple individuals dynamic programming (MIDP) algorithm is throughly developed to cooperatively optimize the multi-train trajectories along the time horizon, with which the optimal speed profiles and timetables of each train can be obtained simultaneously. Moreover, a computationally efficient heuristic algorithm together with a train-to-train (T2T) communication policy are developed as a comparative study. Numerical experiment with field data from Guangzhou Metro Line 8 is implemented, to illustrate the energy-saving performance of the proposed methods.

Variational implicit solvation with Legendre-transformed Poisson–Boltzmann electrostatics

The variational implicit-solvent model (VISM) is an efficient approach to biomolecular interactions, where electrostatic interactions are crucial. The total VISM free energy of a dielectric boundary (i.e. solute–solvent interface) consists of the interfacial energy, solute–solvent interaction energy and dielectric electrostatic energy. The last part is the maximum value of the classical and concave Poisson–Boltzmann (PB) energy functional of electrostatic potentials, with the maximizer being the equilibrium electrostatic potential governed by the PB equation. For the consistency of energy minimization and computational stability, here we propose alternatively to minimize the convex Legendre-transformed Poisson–Boltzmann (LTPB) electrostatic energy functional of all dielectric displacements constrained by Gauss’ Law in the solute region. Both integrable and discrete solute charge densities are treated, and the duality of the LTPB and PB functionals is established. A penalty method is designed for the constrained minimization of the LTPB functional. In application to biomolecular interactions, we minimize the total VISM free energy iteratively, while in each step of such iteration, minimize the LTPB energy. Convergence of such a min–min algorithm is shown. Our numerical results on the solvation of a single ion indicate that the LTPB performs better than the PB formulation, providing possibilities for efficient biomolecular simulations.

Task level energy and performance assurance workload scheduling model in distributed computing environment

Scientific workload execution on distributed computing platform such as cloud environment is time intense and expensive. The scientific workload has task dependencies with different service level agreement (SLA) prerequisite at different levels. Existing workload scheduling (WS) design are not efficient in assuring SLA at task level. Alongside, induce higher cost as majority of scheduling mechanisms reduce either time or energy. In reducing, cost both energy and makespan must be optimized together for allocating resource. No prior work has considered optimizing energy and processing time together in meeting task level SLA requirement. This paper present task level energy and performance assurance (TLEPA)-WS algorithm for distributed computing environment. The TLEPA-WS guarantees energy minimization with performance requirement of parallel application under distributed computational environment. Experiment results shows significant reduction in using energy and makespan; thereby reduces cost of workload execution in comparison with various standard workload execution models.

Mechanical properties of Ir3V1-xTi x intermetallic compounds

The present work provides theoretical and experimental studies on the mechanical properties of Ir3V1-xTix (x = 0, 0.125, 0.250, 0.75, 0.875, 1) refractory intermetallic compounds. The ground state structural and elastic properties were computed using first-principles calculation based on density functional theory (DFT) within the generalized gradient approximation. The lattice parameter of Ir3V1-xTix calculated by total energy minimization increases with Ti, consistent with experimental results. Among the various ternary composition Ir3V0.25Ti0.75 alone exhibits Cauchy’s pressure, shear modulus/bulk modulus (G/B) ratio and Poisson ratio in the ductile range. Young’s modulus and hardness were experimentally measured in this compound less than Ir3V, qualitatively match the theoretical predictions.

Energy Minimization of Inland Waterway USVs for IRS-Assisted Hybrid UAV-Terrestrial MEC Network

Energy Minimization of Inland Waterway USVs for IRS-Assisted Hybrid UAV-Terrestrial MEC Network

Thermodynamic Modeling of Aqueous Nanobubble Dispersion

Summary The amount of gaseous species in water or brine can be greatly enhanced in the form of nanobubble (NB) dispersion. Aqueous NB dispersion has vast industrial applications, potentially in enhanced oil recovery (EOR) and carbon dioxide (CO2) sequestration to control the mobility of gaseous species and the geochemistry associated with CO2 dissolution. Development of such NB technologies depends on a proper understanding of thermodynamic properties of aqueous NB dispersion. The objectives of this research are to analyze the thermodynamic stability of aqueous NB dispersion and to apply a thermodynamic equilibrium model to analyze experimental data. We first present a thermodynamic formulation for modeling aqueous NB dispersion, which clarifies that aqueous NB dispersion occurs in the aqueous phase that is supersaturated by the gaseous species in the system. That is, the gaseous species are present in two modes—dispersion of gas bubbles under capillary pressure and molecule dispersion (supersaturation) in the external aqueous phase. Such a thermodynamic system is referred to as aqueous NB fluid in this research and specified by (NC + 3) variables (e.g., temperature, total volume, components’ mole numbers, and capillary pressure), in which NC is the number of components. We then present a novel implementation of the GERG-2008 equation of state (EOS) in minimization of the Helmholtz free energy to solve for equilibrium properties of aqueous NB fluid. GERG-2008 was used in this research because it is suitable for modeling an aqueous phase that is supersaturated by gaseous species. The thermodynamic equilibrium model was applied to experimental data of aqueous NB fluid with nitrogen (N2) at pressures up to 277 bara (4,019 psia) and 295.15 K (71.6°F). Application of the model to experimental data indicates that a large fraction (0.8–0.9) of the total amount of N2 is in the form of molecule dispersion, but such supersaturation of the aqueous phase is possible because of the presence of NB dispersion with capillary pressure. That is, NB dispersion can increase the gas content in aqueous NB fluid by enabling gas supersaturation in the aqueous phase as a thermodynamic system.

Synthesis, characterization, molecular modeling, binding energies of β-cyclodextrin-inclusion complexes of quercetin: Modification of photo physical behavior upon β-CD complexation

We prepared a naturally occurring flavanoid namely quercetin from tea leaves and analyzed by Absorption, Emission, FT-IR, 1H, 13C nmr spectra and ESI-MS analysis. The inclusion behavior of quercetin in cyclodextrins like α-, β-, γ-, per-6-ABCD and mono-6-ABCD cavities were supported such as UV–vis., Emission, FT-IR and ICD spectra and energy minimization studies. From the absorption and emission results, the type of complexes formed were found to depend on stoichiometry of Host:Guest. FT-IR data of CD complexes of quercetin supported inclusion complex formation of the substrate with α-, β- and γ-CDs. The inclusion of host–guest complexation of quercetin with α-, β-, γ-CDs, per-6-ABCD and mono-6-ABCDs provides very valuable information about the CD:quercetin complexes, the study also shows that β-CD complexation improves water solubility, chemical stability and bioavailability of quercetin. Besides, phase solubility studies also supported the formation of 1:1 drug-CD soluble complexes. All these spectral results provide insight into the binding behavior of substrate into CD cavity in the order per-6-ABCD > Mono-6-ABCD > γ-CD > β-CD > α-CD. The proposed model also finds strong support from the fact with excess CD this exciton coupling disappears indicates the formation of only 1:1 complex.

Utilization of Manipulator Redundancy for Torque Reduction During Force Interaction

Abstract The integration of robots into environments shared by humans has been enhanced through the use of redundant robots capable of executing primary tasks and secondary objectives such as obstacle avoidance and null-space impedance control. A critical secondary objective involves optimizing manipulator configurations to reduce torque and prevent torque saturation, similar to how athletes distribute loads to minimize the risk of injury. This paper suggests employing robotic redundancy to evenly distribute joint loads, thereby improving performance and avoiding torque saturation. Prior studies primarily focused on either endpoint stiffness control or kinetic energy minimization, each having its drawbacks. This paper introduces a novel objective function that responds to all external disturbances at the end-effector, aiming to lower joint torques via redundancy for precise trajectory tracking amidst disturbances. This method, which provides an inverse kinematics solution adaptable to various controllers, demonstrated a 29.85% reduction in peak torque and a 14.69% decrease in cumulative torques in the KUKA LBR iiwa 14 R820 robot.

Wetting Behavior of Inkjet-Printed Electronic Inks on Patterned Substrates.

In inkjet printing technology, one important factor influencing the printing quality and reliability of printed films is the interaction of the jetted ink with the substrate surface. This short-range interaction determines the wettability and the adhesion of the ink to the solid surface and is hence responsible for the final shape of the deposited ink. Here, we investigate wetting morphologies of inkjet-printed inks on patterned substrates by carefully designed experimental test structures and simulations. The contact angles, the surface properties, and drop shapes, as well as their influence on the device variability, are experimentally and theoretically analyzed. For the simulations, we employ the phase-field method, which is based on the free energy minimization of the two-phase system with the given wetting boundary conditions. Through a systematic investigation of printed drops on patterned substrates consisting of hydrophilic and hydrophobic areas, we report that the printed morphology is related not only to the designed layout and the drop volume but also to the printing strategy and the wettability. Furthermore, we show how one can modify the intrinsic wettability of the patterned substrates to enhance the printing quality and reliability. Based on the present findings, we cast light on the improvement of the fabrication quality of thin film transistors.

Dielectric spectroscopy of ferroelectric nematic liquid crystals: Measuring the capacitance of insulating interfacial layers

Numerous measurements of the dielectric constant ɛ of the recently discovered ferroelectric nematic (NF) liquid crystal (LC) phase report extraordinarily large values of ɛ′ (up to ∼30 000) in the NF phase. We show that what is in fact being measured in such experiments is the high capacitance of the nonferroelectric, interfacial, insulating layers of nanoscale thickness that bound the NF material in typical cells. Polarization reorientation as a linear response to AC electric field drive renders the NF layer effectively electrically conductive, exhibiting low resistance that enables the charging of the interfacial capacitors. We analyze the commonly employed parallel-plate cell filled with NF material of high polarization , oriented parallel to the plates at zero applied voltage. Minimization of the dominant electrostatic energy renders spatially uniform and orients the polarization to make the electric field in the NF as small as possible, a condition under which the voltage applied to the cell appears almost entirely across the high-capacity interfacial layers. This coupling of orientation and charge creates a combined polarization-external capacitance (PCG) Goldstone reorientation mode requiring applied voltages orders of magnitude smaller than that of the NF layer alone to effectively transport charge across the NF layer. The NF layer acts as a low-value resistor, and the interfacial capacitors act as reversible energy storage reservoirs, lowering the restoring force (mass) of the PCG mode and producing strong reactive dielectric behavior. Analysis of data from a variety of experiments on ferroelectric LCs (chiral smectics C, bent-core smectics, and the NF phase) supports the PCG model, showing that deriving dielectric constants from electrical impedance measurements of high-polarization ferroelectric LCs, without properly accounting for the self-screening effects of polarization charge and the capacitive contributions of interfacial layers, can result in overestimation of the ɛ′ values of the LC by many orders of magnitude. Published by the American Physical Society 2024

Massively parallel implementation of iterative eigensolvers in large-scale plane-wave density functional theory

The Kohn-sham density functional theory (DFT) is a powerful method to describe the electronic structures of molecules and solids in condensed matter physics, computational chemistry and materials science. However, large and accurate DFT calculations within plane waves process a cubic-scaling computational complexity, which is usually limited by expensive computation and communication costs. The rapid development of high performance computing (HPC) on leadership supercomputers brings new opportunities for developing plane-wave DFT calculations for large-scale systems. Here, we implement parallel iterative eigensolvers in large-scale plane-wave DFT calculations, including Davidson, locally optimal block preconditioned conjugate gradient (LOBPCG), projected preconditioned conjugate gradient (PPCG) and the Chebyshev subspace iteration (CheFSI) algorithms, and analyze the performance of these algorithms in massively parallel plane-wave computing tasks. We adopt a two-level parallelization strategy that combines the message passing interface (MPI) with open multi-processing (OpenMP) parallel programming to handle data exchange and matrix operations in the construction and diagonalization of large-scale Hamiltonian matrix within plane waves. Numerical results illustrate that these iterative eigensolvers can scale up to 42,592 processing cores with high peak performance of 30% on leadship supercomputers to study the electronic structures of bulk silicon systems containing 10,648 atoms. Program summaryProgram Title: Plane wave density functional theory (PWDFT)CPC Library link to program files:https://doi.org/10.17632/c8v2mx5vn4.1Developer's repository link:https://bitbucket.org/berkeleylab/scalesLicensing provisions: BSD 3-clauseProgramming language: C++Nature of problem: PWDFT is used for electronic structure calculations based on Kohn-Sham density functional theory. The key challenge to address is a constrained energy minimization problem, which can also be formulated as a nonlinear eigenvalue problem. MPI/OpenMP-based approaches are employed to provide multi-core acceleration for the study of the chemical and material properties of larger-scale molecules and solids.Solution method: PWDFT implements self-consistent field (SCF) iterations and direct constrained minimization algorithms with various acceleration strategies. It is written in C++ and offers parallel acceleration based on MPI/OpenMP.