Structure Search Research Articles

Probing the effect of magnesium doping on the structural and electronic properties of boron clusters

In this paper, a global search for the lowest energy structure of Mg2Bn0/- (n = 1–12) clusters is performed based on Density functional theory combined with the particle swarm optimization algorithm program CALYPSO. The geometry, stability, charge transfer, and bonding properties of the clusters were analyzed at the B3LYP/def2-tzvp level. Then Mg2B8 with high symmetry D4h was found to be the magic numbers cluster among them. The doped clusters Mg2Bn0/− were compared with the pure boron clusters Bn+20/− (n = 1–12) at the same level. The following results are obtained: as the number of boron atoms increases, the boron clusters doped with Mg atoms change from a two-dimensional planar or quasi-plane structure to a three-dimensional structure. The doped magnesium atoms are always positively charged in the convex capped position. And the doping of Mg atoms drives the overall stabilization of the boron clusters and makes the clusters more compact. There is a large amount of charge transfer between the Mg atom and the B atom, and the main orbitals involved are the S and p orbitals. There are strong interactions between the two atoms of boron and magnesium. Through the analysis of the bonding properties, it was found that the s-p hybridization mode between MgB is the main reason for the enhanced cluster stability.

Schema formation and stimulus-schema discrepancy: A basic unit and its properties.

Research with 2-day-old neonates shows that they create mental representations-schemata-for their experiences and that this cognitive ability is hardwired and functional at birth. This research and studies with older infants indicate that both the formation and the expansion of schemata occur through moderate discrepancies, a concept that Jerome Kagan promoted conceptually and through his research. Discrepancy, as distinct from novelty, is insufficiently acknowledged in the literature on schema theory. The schema is both cognitive and affective and develops in unison in a curvilinear pattern with a gradual onset and exponential expansion. Optimal attentiveness and positive affect occur at the peak of formation and to moderate discrepancies. Redundancy beyond the optimal level produces decreasing interest and positive affect and increasing negative affect resulting in boredom and avoidance. These characteristics of schema development are difficult to study with older children and adults. Rumelhart (1980) regarded the schema as the "building block of cognition" and Kagan (2002) called its expansion through moderate discrepancies an "engine of change" implying widespread application for cognition and behavior throughout life. Kagan urged the search for structure (form) as opposed to function in cognition, and the curvilinear pattern of schema development and its characteristics, it is argued, is the structure he sought. Implications and select applications of schema development and expansion are presented. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Prediction of Van Hove singularity systems in ternary borides

A computational search for stable structures among both α and β phases of ternary ATB4 borides (A = Mg, Ca, Sr, Ba, Al, Ga, and Zn, T is 3d or 4d transition elements) has been performed. We found that α-ATB4 compounds with A = Mg, Ca, Al, and T = V, Cr, Mn, Fe, Ni, and Co form a family of structurally stable or almost stable materials. These systems are metallic in non-magnetic states and characterized by the formation of the localized molecular-like state of 3d transition metal atom dimers, which leads to the appearance of numerous Van Hove singularities in the electronic spectrum. The closeness of such singularities to the Fermi level can be easily tuned by electron doping. For the atoms in the middle of the 3d row (Cr, Mn, and Fe), these singularities led to magnetic instabilities and magnetic ground states with a weakly metallic or semiconducting nature. Such states appear as non-trivial coexistence of the different spin ladders formed by magnetic dimers of 3d elements. These magnetic states can be characterized as an analog of the spin glass state. Experimental attempts to produce MgFeB4 and associated challenges are discussed, and promising directions for further synthetic studies are formulated.

Computing Minimal Boolean Models of Gene Regulatory Networks.

Models of gene regulatory networks (GRNs) capture the dynamics of the regulatory processes that occur within the cell as a means to understanding the variability observed in gene expression between different conditions. Arguably the simplest mathematical construct used for modeling is the Boolean network, which dictates a set of logical rules for transition between states described as Boolean vectors. Due to the complexity of gene regulation and the limitations of experimental technologies, in most cases knowledge about regulatory interactions and Boolean states is partial. In addition, the logical rules themselves are not known a priori. Our goal in this work is to create an algorithm that finds the network that fits the data optimally, and identify the network states that correspond to the noise-free data. We present a novel methodology for integrating experimental data and performing a search for the optimal consistent structure via optimization of a linear objective function under a set of linear constraints. In addition, we extend our methodology into a heuristic that alleviates the computational complexity of the problem for datasets that are generated by single-cell RNA-Sequencing (scRNA-Seq). We demonstrate the effectiveness of these tools using simulated data, and in addition a publicly available scRNA-Seq dataset and the GRN that is associated with it. Our methodology will enable researchers to obtain a better understanding of the dynamics of GRNs and their biological role.

BigSMARTS: A Topologically Aware Query Language and Substructure Search Algorithm for Polymer Chemical Structures.

Molecular search is important in chemistry, biology, and informatics for identifying molecular structures within large data sets, improving knowledge discovery and innovation, and making chemical data FAIR (findable, accessible, interoperable, reusable). Search algorithms for polymers are significantly less developed than those for small molecules because polymer search relies on searching by polymer name, which can be challenging because polymer naming is overly broad (i.e., polyethylene), complicated for complex chemical structures, and often does not correspond to official IUPAC conventions. Chemical structure search in polymers is limited to substructures, such as monomers, without awareness of connectivity or topology. This work introduces a novel query language and graph traversal search algorithm for polymers that provides the first search method able to fully capture all of the chemical structures present in polymers. The BigSMARTS query language, an extension of the small-molecule SMARTS language, allows users to write queries that localize monomer and functional group searches to different parts of the polymer, like the middle block of a triblock, the side chain of a graft, and the backbone of a repeat unit. The substructure search algorithm is based on the traversal of graph representations of the generating functions for the stochastic graphs of polymers. Operationally, the algorithm first identifies cycles representing the monomers and then the end groups and finally performs a depth-first search to match entire subgraphs. To validate the algorithm, hundreds of queries were searched against hundreds of target chemistries and topologies from the literature, with approximately 440,000 query-target pairs. This tool provides a detailed algorithm that can be implemented in search engines to provide search results with full matching of the monomer connectivity and polymer topology.

Empirical interatomic potentials for ZrO2 and YSZ polymorphs: Application to a tetragonal ZrO2 grain boundary

Yttria stabilized zirconia (YSZ) ceramics have been used for various engineering applications including structural ceramics, biomedical materials, and thermal barrier coatings. The versatile and excellent properties of YSZ stem from its unique microstructure consisting of monoclinic, tetragonal, and cubic phases, whose stability depends on yttria concentration and temperature. However, there are no empirical interatomic potentials (EIPs) that can reproduce the structures and energies of ZrO2 and YSZ polymorphs, limiting the atomic-scale investigation of lattice defect structures and their interactions that affect the YSZ microstructure and properties. Here, using a genetic algorithm and ab initio training datasets, we have optimized EIPs to sufficiently reproduce the structures and stability of ZrO2 and YSZ polymorphs, as well as the properties of the tetragonal and cubic phases at finite temperature. The potentials have also been applied to the search for a tetragonal grain boundary structure, showing that the obtained grain boundary structure is consistent with that obtained by ab initio calculations. The developed EIPs will aid in revealing the microstructure-property relationships in YSZ by performing large-scale and systematic calculations, which are practically difficult to perform with ab initio and machine-learning-potential calculations.

The 3Ranker: An AI-based Algorithm for Finding Non-animal Alternative Methods.

The search for existing non-animal alternative methods for use in experiments is currently challenging because of the lack of both comprehensive structured databases and balanced keyword-based search strategies to mine unstructured textual databases. In this paper we describe 3Ranker, which is a fast, keyword-independent algorithm for finding non-animal alternative methods for use in biomedical research. The 3Ranker algorithm was created by using a machine learning approach, consisting of a Random Forest model built on a dataset of 35 million abstracts and constructed with weak supervision, followed by iterative model improvement with expert curated data. We found a satisfactory trade-off between sensitivity and specificity, with Area Under the Curve (AUC) values ranging from 0.85-0.95. Trials showed that the AI-based classifier was able to identify articles that describe potential alternatives to animal use, among the thousands of articles returned by generic PubMed queries on dermatitis and Parkinson's disease. Application of the classification models on time series data showed the earlier implementation and acceptance of Three Rs principles in the area of cosmetics and skin research, as compared to the area of neurodegenerative disease research. The 3Ranker algorithm is freely available at www.open3r.org; the future goal is to expand this framework to cover multiple research domains and to enable its broad use by researchers, policymakers, funders and ethical review boards, in order to promote the replacement of animal use in research wherever possible.

Prediction of thermodynamic stability of actinide compounds by machine learning model

The thermodynamic phase stability plays a crucial role as it serves as a fundamental parameter governing the synthesizability of materials and their potential for degradation under specific operating conditions. In this study, two machine learning (ML) models, random forest (RF) and neural network (NN), were used to predict the thermodynamic phase stability of actinide compounds using a dataset consisting of 62204 DFT-calculated energies. Our study utilizes a comprehensive range of properties that do not contain structural information, making them applicable to materials composed of any number of constituent elements. Notably, the trained models achieve an approximation that closely aligns with the error obtained from DFT calculations, while drastically reducing computational time by several orders of magnitude. Moreover, we extended our analysis to predict binary phase diagrams of Generation IV nuclear fuels using the trained models. To address the limitations of a single model for predicting certain compounds and enhance model robustness, a simple ensemble learning approach, i.e., the multi-component learner was employed. By synergistically combining prediction outcomes from RF and NN models, the ensemble learning approach excels in accurately predicting phase diagrams of actinide compounds. Utilizing the compound components forecasted by the model as a foundation, we embarked on an extensive series of structural searches and conducted thorough phonon dispersion studies. The outcomes unequivocally affirm the model's efficacy in accurately predicting stable compound compositions.

Bayesian active machine learning for Cluster expansion construction

The Cluster expansion (CE) is a powerful method for representing the energetics of alloys from a fit to first principles energies. However, many common fitting methods are computationally demanding and do not provide the guarantee that the system’s ground states are preserved. This paper demonstrates the use of an efficient implementation of a Bayesian algorithm for cluster expansion construction that ensures all the input structural energies are fitted perfectly while reducing computational cost. The method incorporates an active learning scheme that searches for new optimal structures to include in the fit. As performance tests, we calculate the phase diagram of the Fe–Ir system and study the short range order in an equimolar MoNbTaVW system. The new method has been integrated into the Alloy Theoretic Automated Toolkit (ATAT).

Structurally Constrained Evolutionary Algorithm for the Discovery and Design of Metastable Phases.

Metastable materials are abundant in nature and technology, showcasing remarkable properties that inspire innovative materials design. However, traditional crystal structure prediction methods, which rely solely on energetic factors to determine a structure's fitness, are not suitable for predicting the vast number of potentially synthesizable phases that represent a local minimum corresponding to a state in thermodynamic equilibrium. Here, we present a new approach for the prediction of metastable phases with specific structural features and interface this method with the XtalOpt evolutionary algorithm. Our method relies on structural features that include the local crystalline order (e.g, the coordination number or chemical environment), and symmetry (e.g, Bravais lattice and space group) to filter the breeding pool of an evolutionary crystal structure search. The effectiveness of this approach is benchmarked on three known metastable systems: XeN8, with a two-dimensional polymeric nitrogen sublattice, brookite TiO2, and a high pressure BaH4 phase, which was recently characterized. Additionally, a newly predicted metastable melaminate salt, P1̅ WC3N6, was found to possess an energy that is lower than that of two phases proposed in a recent computational study. The method presented here could help in identifying the structures of compounds that have already been synthesized, and in developing new synthesis targets with desired properties.

Recent advances in the interface structure prediction for heteromaterial systems

The atomic structures of solid-solid interfaces in materials are of fundamental importance for understanding the physical properties of interfacial materials, which is, however, difficult to determine both in experimental and theoretical approaches. New theoretical methodologies utilizing various global optimization algorithms and machine learning (ML) potentials have emerged in recent years, offering a promising approach to unraveling interfacial structures. In this review, we give a concise overview of state-of-the-art techniques employed in the studies of interfacial structures, e.g., ML-assisted phenomenological theory for the global search of interface structure (ML-interface). We also present a few applications of these methodologies.

An automatic network structure search via channel pruning for accelerating human activity inference on mobile devices

During recent years, deep convolutional neural networks (CNNs) have become increasingly popular in sensor-based human activity recognition (HAR), due to powerful feature extraction capacity. However, the required high computational cost has hindered their practical deployment in real-world applications. To address this issue, this paper presents a novel approach named ABCSearch to prune channels through an artificial bee colony (ABC) algorithm, which is a meta-heuristic optimization algorithm inspired by the foraging behavior of honeybees. Unlike traditional pruning strategies that prioritize channel importance, the proposed method seeks to identify the optimal number of channels at each layer, i.e., the optimal pruning structure. To lessen human interference, we transform the search problem of the optimal pruning structure into a simple optimization problem and solve it automatically through the ABC algorithm, which can significantly decrease both model size and computational cost without compromising performance. Specifically, we validate the effectiveness of ABCSearch for a standard five-layer CNN backbone architecture, which achieves 97.05%, 98.45%, 91.71%, 78.89% classification accuracies on four HAR benchmarks, i.e., UCI-HAR, WISDM, PAMAP2, UniMiB-SHAR. Our approach can yield 87.44%, 89.41%, 89.06%, 89.59% FLOPs reductions, and 89.54%, 88.70%, 88.38%, 90.45% less memory consumptions, as well as 4.4×, 5.0×, 5.8×, 6.1× practical speedups on real hardware devices, while maintaining comparable accuracy.

Penta-MN8 family: First realization of type-5 pentagonal tessellation in 2D hexagonal crystals with intriguing properties

Tessellation of the Euclidean plane by congruent pentagons has long been of great interest. However, periodic pentagonal patterns realized in crystallography reported so far are limited to type-2 or type-4 tiling in typical square lattices. Based on the recently synthesized N18 macrocycles together with Hückel aromaticity and 18-electron rules, we design a family of pentagonal metal polynitrides, penta-MN8 (M = Mg, Ca, Sr, Ba; Sn, Pb; Cd; Mo, W), the first materials realization of type-5 mathematical pattern with novel pentagonal tiles in 2D hexagonal lattices. The penta-MN8 family exhibits rich stable phases (γ, α/α′, and β) and diverse Poisson's ratios ranging from 0.299 to -0.488. Among these nitrogen-richest monolayer nitrides hitherto, penta-MgN8 possesses firm stability up to 700 K and under low pressure originating from unique aromaticity over the polymeric nitrogen network, which could be synthesized under ∼50 GPa as confirmed by global structure search. Moreover, penta-MgN8 exhibits a fully flat band near Fermi level over the whole 2D Brillouin zone, deriving from the combination of pentagonal pattern and hexagonal lattice as verified with tight-binding model analysis. Such electronic structures fill in the blank of clear kagome flat bands in known kagome nitrides and pentagon-based materials, resulting in very heavy fermions with huge effective mass (55.7 m0) that are favorable to achieve quantum zero-field Wigner crystals. Our work presents a new pentagonal-materials class with unprecedented coordination, lattice symmetry, and physical properties compared to existing pentagon-based systems, which makes a breakthrough in exploring the materials realization of the mathematical models for pentagonal tessellation.

Isolated flat bands in 2D lattices based on a novel path-exchange symmetry

The increased ability to engineer two-dimensional (2D) systems, either using materials, photonic lattices, or cold atoms, has led to the search for 2D structures with interesting properties. One such property is the presence of flat bands. Typically, the presence of these requires long-ranged hoppings, fine-tuning of nearest neighbor hoppings, or breaking time-reversal symmetry by using a staggered flux distribution in the unit cell. We provide a prescription based on carrying out projections from a parent system to generate different flat band systems. We identify the conditions for maintaining the flatness and identify a path-exchange symmetry in such systems that cause the flat band to be degenerate with the other dispersive ones. Breaking this symmetry leads to lifting the degeneracy while still preserving the flatness of the band. This technique does not require changing the topology nor breaking time-reversal symmetry as was suggested earlier in the literature. The prescription also eliminates the need for any fine-tuning. Moreover, it is shown that the subsequent projected systems inherit the precise fine-tuning conditions that were discussed in the literature for similar systems, in order to have and isolate a flat band. As examples, we demonstrate the use of our prescription to arrive at the flat band conditions for popular systems like the Kagomé, the Lieb, and the Dice lattices. Finally, we are also able to show that a flat band exists in a recently proposed chiral spin-liquid state of the Kagomé lattice only if it is associated with a gauge field that produces a flux modulation of the Chern-Simons type.

Graph deep learning accelerated efficient crystal structure search and feature extraction

Structural search and feature extraction are a central subject in modern materials design, the efficiency of which is currently limited, but can be potentially boosted by machine learning (ML). Here, we develop an ML-based prediction-analysis framework, which includes a symmetry-based combinatorial crystal optimization program (SCCOP) and a feature additive attribution model, to significantly reduce computational costs and to extract property-related structural features. Our method is highly accurate and predictive, and extracts structural features from desired structures to guide materials design. We first test SCCOP on 35 typical compounds to demonstrate its generality. As a case study, we apply our approach to a two-dimensional B-C-N system, which identifies 28 previously undiscovered stable structures out of 82 compositions; our analysis further establishes the structural features that contribute most to energy and bandgap. Compared to conventional approaches, SCCOP is about 10 times faster while maintaining a comparable accuracy. Our framework is generally applicable to all types of systems for precise and efficient structural search, providing insights into the relationship between ML-extracted structural features and physical properties.

Geometric and electronic structures of medium-sized boron clusters doped with plutonium

Doping metal heteroatoms is an effective strategy to regulate the geometric and electronic structure of boron based nanoclusters. However, the exploration of the ground state structures of metal-boron-based nanoclusters is still a challenge duo to the complexity of the bonding interactions between heterogeneous atoms and boron cluster and the number of isomers on the potential energy surface increases exponentially with cluster size. Here, we use the CALYPSO cluster structural search method in combination with density functional theory calculations to study the geometries and electronic properties of anionic boron clusters doped with plutonium (PuB, n = 10–20). Our results show that the medium-sized PuB cluster exhibits excellent stability with highest occupied molecular orbital–lowest unoccupied molecular orbital energy gap of 2.30 eV. The remarkable stability of the anionic PuB cluster is due to the robust interactions between the Pu metal and the B14 skeleton, along with the strong covalent interactions between the B atoms. These findings enrich the geometric structure database of metal doped clusters and provide valuable insights for the future synthesis of boron based nanomaterials.

Quantum chemical exploration of the binding motifs and binding energies of neutral molecules, radicals and ions with small water clusters: characterisation and astrochemical implications

ABSTRACT Accurate binding energies of molecules to water clusters are relevant for understanding intermolecular interactions and various chemical applications. They enter models of interstellar chemical processes, as binding to icy grains influences surface reactions and thus affects calculated gas-phase abundances. Unfortunately, many astrochemical molecules (especially radicals and ions) are incompletely characterised in these models. To address this, we report computational searches for optimal structures and benchmark binding and condensation energies for sets of neutral, radical, cationic, and anionic molecules of astrochemical relevance with clusters of N = 1 − 4 water molecules. These calculations utilised reliable density functionals for geometry optimisation, and coupled cluster (CCSD(T)) single point calculations with large basis sets. Four energetic binding motifs (weak, intermediate, strong or covalently bonded) were observed depending on the chemical nature of the guest molecule. Neutral closed and open-shell molecules with strong dipoles and a greater potential for hydrogen bonding are more tightly bound to water clusters compared to non-polar ones. For closed-shell cationic and anionic species, barrier-less reactions with water clusters occur, which reveals radical-free routes to molecular processing in the gas phase and on amorphous ice surfaces.

Towards efficient long-horizon decision-making using automated structure search method of hierarchical reinforcement learning for edge artificial intelligence

Hierarchical reinforcement learning (HRL) is a promising approach for efficiently solving various long-horizon decision-making tasks in the Internet of Things (IoT) domain. However, HRL algorithms are known to rely on expert knowledge to preset an appropriate hierarchical structure for different IoT tasks, which leads to higher trial costs and limits its wider application. In this paper, we propose a new method called DHRL (Dynamic-Level Hierarchical Reinforcement Learning) and it is able to adaptively search for the optimal hierarchical structure while maintaining the generality of framework design. DHRL incorporates an embedded exploration and exploitation mechanism that effectively solves the challenges caused by dependence between different levels and achieves a balance between maximizing benefits and current evaluation accuracy. Nonetheless, the more exploration processes inevitably has a negative impact on the performance. To mitigate this influences, we propose a synchronous training architecture to support DHRL operating in a distributed and parallel manner, in which the adaptive evolutionary method is also introduced to accelerate the convergence. Extensive experimental evaluations are conducted to demonstrate the effectiveness of our theory and method.

Search for a possible quasi-periodic structure based on data of the SDSS DR12 LOWZ

ABSTRACT We carry out a statistical analysis of the spatial distribution of galaxies at cosmological redshifts 0.16 ≤ z ≤ 0.47 based on the SDSS DR12 LOWZ catalogue. Our aim is to search and study possible large-scale quasi-regular structures embedded in the cosmic web. We calculate projections of the Cartesian galaxy coordinates on different axes (directions), densely covering certain regions in the sky to look for special directions along which 1D distributions of the projections contain significant quasi-periodic components. These components appear as peaks in the power spectra and lie in a narrow range of wave numbers 0.05 < k < 0.07. Particular attention is paid to the evaluation of the significance of the peaks. It is found that the significance of the dominant peaks for some selected directions exceeds (4–5)σ. In order to reduce possible selection effects, we create a mock homogeneous catalogue of spatial distribution of galaxies by adding a random set of artificial objects (points) to the real galaxies under study. The power spectrum of this cumulative model data also demonstrates significant peak corresponding to approximately the same scale. As a result we assume the existence of an anisotropic cosmological quasi-periodic structure with characteristic scale (116 ± 10) h−1 Mpc.

Pressure-induced evolution of stoichiometries and electronic structures of host–guest Na–B compounds

Superionic and electride behaviors in materials, which induce a variety of exotic physical properties of ions and electrons, are of great importance both in fundamental research and for practical applications. However, their coexistence in hot alkali-metal borides has not been observed. In this work, we apply first-principles structure search calculations to identify eight Na–B compounds with host–guest structures, which exhibit a wide range of building blocks and interesting properties linked to the Na/B composition. Among the known borides, Na-rich Na9B stands out as the composition with the highest alkali-metal content, featuring vertex- and face-sharing BNa16 polyhedra. Notably, it exhibits electride characteristics and transforms into a superionic electride at 200 GPa and 2000 K, displaying unusual Na atomic diffusion behavior attributed to the modulation of the interstitial anion electrons. It demonstrates semiconductor behavior in the solid state, and metallic properties associated with Na 3p/3s states in the superionic and liquid regions. On the other hand, B-rich NaB7, consisting of a unique covalent B framework, is predicted to exhibit low-frequency phonon-mediated superconductivity with a Tc of 16.8 K at 55 GPa. Our work advances the understanding of the structures and properties of alkali-metal borides.