Global Structure Optimization Research Articles

Cascading symmetry constraint during machine learning-enabled structural search for sulfur-induced Cu(111)-(43×43) surface reconstruction.

In this work, we investigate how exploiting symmetry when creating and modifying structural models may speed up global atomistic structure optimization. We propose a search strategy in which models start from high symmetry configurations and then gradually evolve into lower symmetry models. The algorithm is named cascading symmetry search and is shown to be highly efficient for a number of known surface reconstructions. We use our method for the sulfur-induced Cu (111) (43×43) surface reconstruction for which we identify a new highly stable structure that conforms with the experimental evidence.

On the brink of self-hydration: the water heptadecamer.

For pure, neutral, isolated molecular clusters, (H2O)17 marks the transition from structures with all water molecules on the cluster surface to water self-hydration, i.e., cluster structures around one central water molecule. Getting this right with water model potentials turns out to be challenging. Even the best water potentials currently available, which reproduce collective properties very well, still deliver contradicting results for (H2O)17, when different low-energy isomers from global structure optimizations are examined. Interestingly, ab initio quantum chemistry also struggles with the only seemingly simple question if (H2O)17 is all-surface or water-centered. Hence, although the long history of water potential development may be entering its final phase, it is not quite finished yet.

Global structure optimization following imaginary phonon modes accelerated by machine learning potentials in Cu, Ag, and Au

Global structure optimization following imaginary phonon modes accelerated by machine learning potentials in Cu, Ag, and Au

Fabricable concentric-ring metalens with high focusing efficiency based on two-dimensional subwavelength unit splicing.

To address the challenges posed by computational resource consumption and data volume in the development of large-aperture metalenses, a design method for concentric-ring metalens based on two-dimensional unit splicing is proposed in this paper. In the method, the unit structure library is constructed through global traversal under the machining process constraints. The phase matching is performed for two polarization states with specific weights and the design of binary-height, concentric-ring structures with arbitrary polarization sensitivity is realized, whose focusing efficiency (the encircled power within 3×FWHM of the focal spot divided by the near-field outgoing power) is up to 90%. Based on this method, a polarization-insensitive metalens with a design wavelength of 10µm, diameter of 2 cm, and numerical aperture of 0.447 is obtained. The method combines the advantages of lower computation requirements for a building block array of a metalens and lower structure data for a concentric-ring metalens. Consequently, it becomes possible to reduce calculation and processing costs by several orders of magnitude during the development process of metalenses with diameters ranging from 103 to 105 wavelengths. The resulting focusing efficiency can approach the upper limit achievable through global structural optimization and significantly surpass that of binary-height Fresnel lenses.

Research on the implementation path of industrial structure optimization in Fujian Province under the goal of “double carbon”

Abstract Facing the dual pressure of domestic environmental pollution and international climate change mitigation, China, the world’s largest emitter of carbon emissions, has proposed a “double carbon” target of 3060. Fujian Province is a clean energy province and a major economic province in southeastern China, with inherent energy conservation and emission reduction advantages. However, as industrialization is still in progress and urbanization is accelerating, the problems of low industrial level and sloppy development are still prominent, and the future low-carbon transformation will be difficult and long. Based on the theory of industrial structure optimization and the theory of low carbon economy, this paper analyzes the influence mechanism of industrial structure optimization on carbon emission from three perspectives: efficiency improvement, technological progress, and structural adjustment. Secondly, an econometric model is constructed by combining the environmental Kuznets curve, and the time series data of Fujian Province from 2000-2021 are analyzed empirically to derive the optimization path of industrial structure on carbon emissions in Fujian Province through co-integration analysis, multiple regression, mediating effect analysis, and robustness testing methods. The results of this paper show that from the structure of energy consumption in Fujian Province, coal and oil consumption account for 72% of the total consumption, which is the main cause of carbon emissions in Fujian Province. The mediating effect and stability analysis of the optimization path through the benchmark regression model shows that the p-value variance is 0.0862, and there is no significant difference between them. The optimization path proposed in this paper has significant utility for carbon emission reduction, improves the efficiency of energy use, strengthens the introduction of FDI and industrial structure optimization in various regions of Fujian Province in a targeted manner, and provides a reference for decision-making for global industrial structure optimization and upgrading.

Screen space shape manipulation by global structural optimization

Existing structure-aware shape manipulation frameworks have greatly facilitated the shape manipulation procedure. However, they still require users to possess the necessary 3D modeling experience since the manipulation, e.g., positioning shape handles, is performed in 3D space. For novice users without such experience, this procedure remains tedious. On the other hand, performing manipulation in screen space is more natural to novice users, especially with 2D input and display devices. This paper presents a novel interactive framework for manipulating 3D man-made shapes in screen space. Users only need to edit abstract proxies of input shapes in 2D to obtain desired deformed shapes without considering their 3D geometries. This is achieved by first analyzing the structures of the shapes and then estimating the deformed shapes with a structure-preserving optimization method. Our efficient algorithm can produce real-time shape manipulation results as previews, thus avoiding a trial-and-error editing procedure. To verify the effectiveness of our framework, we conducted a user study that contained four shape manipulation tasks. In this study, we compared our framework with a baseline framework that requires users to edit shape’s geometry in 3D space and does not provide previews for the manipulation results. The statistics and the user feedback confirm that our framework is more intuitive and efficient for shape manipulation.

Development of Deep Convolutional Neural Network for Structural Topology Optimization

This research develops a highly effective deep-learning-based surrogate model that can provide the optimum topologies of 2D and 3D structures. In general, structural topology optimization requires plenty of computations because of a large number of finite element analyses to obtain optimal structural layouts by reducing the weight and satisfying the constraints. Therefore, many researchers have developed a deep-learning-based model using the initial static analysis results to predict the optimum designs. However, these studies still considered relatively simple example problems, such as a cantilever plate and MBB beam, even though they required a large number of data to achieve an accurate surrogate model for the simple application. To overcome these limitations, we propose a new framework, which 1) efficiently uses limited data by normalizing it and 2) employs a surrogate model capable of handling more practical cases, such as a 2D panel and 3D stiffened panel subjected to a distributed load, and can be used for local–global structural optimization. In all cases, the developed surrogate models can predict the optimum layouts with structural performance levels equal to those of the structures considered to be ground truths. Also, when the optimal structures were obtained using the proposed method, the total calculation time was reduced by 98% as compared to conventional topology optimization once the convolutional neural network had been trained.

Global structural optimization of annular thermoelectric generators based on a dual-finite-element multiphysical model

The conventional methods to improve the performance of annular thermoelectric generators (ATEGs) heavily rely on optimizing the thermal design of individual annular thermoelectric couples (ATECs). However, since a practical ATEG consists of many ATECs, the optimal structure of the ATEG can differ from the ATEC-based design. On the other hand, optimization by simply considering all ATECs can lead to a heavy computation burden. This work first proposes a high-fidelity, fluid-thermal-electric multiphysical ATEG model, solved by a computationally-efficient dual-finite-element method to cope with the challenge. This model explores the effects of ATEC microstructure and heat exchanger structure on ATEG performance under various operating conditions. Comparative multi-objective optimization studies were performed at three levels, i.e., for a single ATEC, a single ring of ATEG, and the entire ATEG. The results reveal that the optimized structural parameters of ATECs have some new features when considering the entire ATEG as the optimization objective. The optimal height, angle, and thickness of ATECs are 12 mm, 2.35°, and 10 mm, respectively. The corresponding net power, efficiency, and power density of ATEG are 321.6 W, 6.58 %, and 634.15 W/m3, respectively. Compared to the traditional design method based on a single ATEC and a single ring, the net power of the ATEG designed with the proposed method can be enhanced by 168 % and 197 %, respectively, at the expense of only a 20 % reduction in the power density.

Periodic energy transmission and regulation of photovoltaic-phase change material-thermoelectric coupled system under space conditions

Phase change material (PCM) as highly efficient temperature control material has been used in photovoltaic-thermoelectric (PV-TE) coupling system to achieve higher power generation efficiency. The periodic energy balance and PCM regeneration are the key factors that affect the temperature control effect and system total performance. In present paper, a numerical model for space PV-PCM-TE system was established with the composite of paraffin and aluminum foam as PCM. Firstly, the impact of periodic energy imbalance on PCM regeneration characteristics and system overall performance was investigated under space conditions. Then, to regulate the periodic energy transmission of the coupling system, a global structural parameters optimization was performed, and the optimal parameter combination was derived and validated, where porosity of aluminum foam, thickness of PCM layer, thermal concentration factor, length of TE legs, and surface area ratio are 0.94, 4.08 mm, 25.83, 1.09 mm and 1.80, respectively. The validation results show that after optimization, the periodic energy balance and PCM full utilization and regeneration can be achieved. The average total efficiency of optimal case is 30.72%, which is a little lower than that with the surface area ratio of 7.0 (31.77%), but the average power density is 49.64% higher. The present work validates that periodic energy balance design is important for space PV-PCM-TE system and the global parameter optimization method is an effective optimization method, which is significant for the design and optimization of PCM based temperature control system.

Growth Pattern of Large Morse Clusters with Medium-Range Potentials.

Due to the extremely complex potential energy surfaces of large Morse clusters with medium-range potentials (i.e., ρ = 6 and 10), global optimization studies in the literature are limited to a cluster size (N) of ≤240. Starting from completely random structures, we successfully systematically studied Morse clusters with up to 700 atoms using our unbiased fuzzy global optimization (FGO) method. While all of the putative global minima reported previously have been efficiently obtained, new global minima with lower energies are identified for N values of 176, 258, 485, 561, 817, and 923 with ρ = 6 and for N values of 151, 202, 226, and 229 with ρ = 10. A detailed growth pattern and magic clusters are obtained. For the first time, we find that a central vacancy is present in Morse clusters containing 542, 543, 548, and 922 atoms with ρ = 6. FGO has achieved high performance in large clusters with different interatomic interaction ranges, thus showing great application potential in the global structure optimization of general clusters.

Optimal design of space assembly microsatellite structure based on sequential quadratic programming

PurposeOn-orbit assembly technology is a promising research topic in spaceflight field. For purposes of studying the dynamic performance and reducing weight of an on-orbit assembly satellite structure frame, this paper aims to propose a structural optimization design method based on natural frequency.Design/methodology/approachThe dynamic stability of the satellite under working condition depends on the mechanical properties of the structure matrix. A global structural optimization model is established, with the objective of mass minimization and the constraints of given natural frequencies and given structure requirements. The structural optimization and improvement design method is proposed using sequential quadratic programming calculation.FindingsThe optimal result of objective function is effectively obtained, and the best combination of structural geometric parameters is configurated. By analyzing the relationship between the structural variables and optimization parameters, the primary and secondary factors to the mass optimization process of the microsatellite satisfying the dynamic performance requirements are obtained, which improves the effectiveness and accuracy of the system optimization design.Originality/valueThis method can coordinate the relation between satellite vibration stability and weight reduction, which provides an effective way for the optimization design of on-orbit assembly microsatellite. It has reference significance for the similar spacecraft framework structure design.

The Chemical Nature of Ti4O10-: Vibrational Predissociation Spectroscopy Combined with Global Structure Optimization.

The gas-phase infrared spectrum of Ti4O10- is studied in the spectral range from 400 cm-1 to 1250 cm-1 using cryogenic ion trap vibrational spectroscopy, in combination with density functional theory (DFT). The infrared photodissociation (IRPD) spectrum of D2-tagged Ti4O10- provides evidence for a structure of lower symmetry that contains a superoxo group (1121 cm-1) and two terminal Ti=O moieties. DFT combined with a genetic algorithm for global structure optimization predicts two isomers which feature a superoxo group: the Cs symmetric global minimum-energy structure and a similar isomer (C1) that is slightly higher in energy. Coupled cluster calculations confirm the relative stability. Comparison of the harmonic DFT spectra (different functionals) with the IRPD spectrum suggests that both of these isomers contribute. Earlier assignments to the adamantane-like C3v isomer with three terminal Ti-O• - groups in a quartet state are not confirmed. They were based on the infrared multiple photon photodissociation (IRMPD) spectrum of bare Ti4O10- and local DFT structure optimizations.

First-principles design of halide-reduced electrides: Magnetism and topological phases

We propose a design scheme for potential electrides derived from conventional materials. Starting with rare-earth-based ternary halides, we exclude halogens and perform global structure optimization to obtain thermodynamically stable or metastable phases but having an excess of electrons confined inside interstitial cavities. Then, spin-polarized interstitial states are induced by chemical substitution with magnetic lanthanides. To demonstrate the capability of our approach, we test with 11 ternary halides and successfully predict 30 stable and metastable phases of nonmagnetic electrides subject to 3 different stoichiometric categories, and successively 28 magnetic electrides via chemical substitution with Gd. 56 out of these 58 designed electrides are discovered for the first time. Two electride systems, the monoclinic $A$C ($A=$ La, Gd) and the orthorhombic $A_2$Ge ($A=$ Y, Gd), are thoroughly studied to exemplify the set of predicted crystals. Interestingly, both systems turn out to be topological nodal line electrides (TNLE) in the absence of spin-orbit coupling and manifest spin-polarized interstitial states in the case of $A=$ Gd. Our work establishes a novel computational approach of functional electrides design and highlights the magnetism and topological phases embedded in electrides.

MAISE: Construction of neural network interatomic models and evolutionary structure optimization

Module for ab initio structure evolution (MAISE) is an open-source package for materials modeling and prediction. The code’s main feature is an automated generation of neural network (NN) interatomic potentials for use in global structure searches. The systematic construction of Behler–Parrinello-type NN models approximating ab initio energy and forces relies on two approaches introduced in our recent studies. An evolutionary sampling scheme for generating reference structures improves the NNs’ mapping of regions visited in unconstrained searches, while a stratified training approach enables the creation of standardized NN models for multiple elements. A more flexible NN architecture proposed here expands the applicability of the stratified scheme for an arbitrary number of elements. The full workflow in the NN development is managed with a customizable ‘MAISE-NET’ wrapper written in Python. The global structure optimization capability in MAISE is based on an evolutionary algorithm applicable for nanoparticles, films, and bulk crystals. A multitribe extension of the algorithm allows for an efficient simultaneous optimization of nanoparticles in a given size range. Implemented structure analysis functions include fingerprinting with radial distribution functions and finding space groups with the SPGLIB tool. This work overviews MAISE’s available features, constructed models, and confirmed predictions. Program summaryProgram Title: MAISECPC Library link to program files:https://doi.org/10.17632/vfzgt2gnsh.1Licensing provisions: GNU General Public License v3.0Programming language: CNature of problem: Construction of NN interatomic potentials suitable for evolutionary structure searches, molecular dynamics, phonon calculations, and other applications presents a host of challenges ranging from sampling relevant parts of vast configuration spaces to tuning multitudes of NN parameters.Solution method: Evolutionary data generation and modular NN training algorithms featured in the open-source parallelized MAISE package enable automated development of NN models for multiple chemical species. Customizable MAISE-NET wrapper streamlines all stages of the iterative process.Unusual features: NN training stratification allows one to build libraries of reusable models from the bottom up, starting from elements and proceeding to multielement chemical systems. A multitribe evolutionary algorithm improves the efficiency of ground state structure searches by simultaneously optimizing nanoparticles of different sizes and periodically exchanging best motifs between the tribes.

Efficient Global Structure Optimization with a Machine-Learned Surrogate Model

We propose a scheme for global optimization with first-principles energy expressions of atomistic structure. While unfolding its search, the method actively learns a surrogate model of the potential energy landscape on which it performs a number of local relaxations (exploitation) and further structural searches (exploration). Assuming Gaussian processes, deploying two separate kernel widths to better capture rough features of the energy landscape while retaining a good resolution of local minima, an acquisition function is used to decide on which of the resulting structures is the more promising and should be treated at the first-principles level. The method is demonstrated to outperform by 2 orders of magnitude a well established first-principles based evolutionary algorithm in finding surface reconstructions. Finally, global optimization with first-principles energy expressions is utilized to identify initial stages of the edge oxidation and oxygen intercalation of graphene sheets on the Ir(111) surface.

Structure and electronic excitation spectra of low-lying isomers of Aun clusters (n = 2–20). A DFT study

The global structure optimization using the DFT-calibrated empirical potentials followed by the geometry optimization at the DFT level (BPW91/LANL2DZ) has been carried out in order to locate the low-lying isomers of Aun clusters (n = 2–20) modeling the sub-nanoparticles of the photocatalytic composite materials. The total energies, thermodynamic parameters and electronic properties of the located global and low-lying structures of various multiplicities are estimated, and their dependence on the nuclearity and cluster morphology are discussed. For the low-lying isomers, the electronic excitations spectra were calculated at the CAM-B3LYP/LANL2DZ level, and the electronic density redistribution during the excitations were analyzed. This analysis demonstrates that the planar clusters have the highest absorption ability and, at the same time, are the favorable structures for the intermolecular charge redistribution.

Global Structure Optimization of Pt Clusters Based on the Modified Empirical Potentials, Calibrated using Density Functional Theory

The geometry of platinum clusters Ptn (n = 2–10, 13, 19, 24, 38, 55, and 75) was optimized at the UBLYP/CRENBS, UBPW91/CRENBS, UBPW91/LANL2DZ, and UPBE0/LANL08 density functional theory (DFT) level...

Aeroelastic global structural optimization using an efficient CFD-based reduced order model

Abstract In aeroelastic structure optimization, the structural model needs to be modified repeatedly to meet all targets. Although computational fluid dynamics (CFD) based reduced order models (ROMs) have been successfully applied to transonic aeroelastic analysis, the existing CFD-based ROMs are at a fixed flight condition for a frozen aeroelastic model configuration. The aerodynamic and structural model have to be reconstructed to ensure accuracy, when a structural modification was made. These reconstructions take a considerable time and the computational costs become prohibitive. To overcome the realistic challenge, we have developed an efficient CFD-based ROM, which is robust to aeroelastic system. This paper presents a new optimization process using the efficient method for aeroelastic global structural optimization. The optimization process employs Genetic Algorithms (GAs) as optimization tool. In order to assess the performance of presented optimization process, the AGARD 445.6 wing model is taken as numerical example. The results show that the most feasible and optimal solutions are effectively obtained by the presented optimization process.

Exploring self-organization of molecular tether molecules on a gold surface by global structure optimization.

We employ nondeterministic global cluster structure optimization, based on the evolutionary algorithms paradigm, to model the self-assembly of complex molecules on a surface. As a real-life application example directly related to many recent experiments, we use this approach for the assembly of triazatriangulene "platform" molecules on the Au(111) surface. Without additional restrictions like spatial discretizations, coarse-graining or precalculated adsorption poses, and despite the proof-of-principle character of this study, we achieve satisfactory qualitative agreement with several experimental observations and can provide answers to questions that experiments on these species had left open so far. © 2019 Wiley Periodicals, Inc.

DFTB-Assisted Global Structure Optimization of 13- and 55-Atom Late Transition Metal Clusters.

Finding globally optimal structures of nanoclusters is critically important to understand their physicochemical properties but remains prohibitively expensive even with comparatively efficient density functionals. Semiempirical methods such as density functional tight-binding (DFTB), on the other hand, offer a better accuracy-efficiency trade-off but require suitable parametrization. In the present work, we present a largely automatic scheme where, starting from initial guesses based on bulk properties, the atomic confinement, and repulsive potentials are further refined so as to accurately represent the potential energy landscapes of 13- and 55-atom nanoclusters of the late transition metals (Ni, Cu, Pd, Ag, Pt, and Au). With the exception of Ni13, Ni55, Cu55, and Ag55, low-symmetry (often disordered) structures are found to be preferred over the symmetric icosahedral arrangement. Similar to what has been previously reported for Au55, the lowest-energy Pt55 structures also appear to contain small cavities below the outer shell.