Hierarchical Structure Of Materials Research Articles

Cluster energy prediction based on multiple strategy fusion whale optimization algorithm and light gradient boosting machine

BackgroundClusters, a novel hierarchical material structure that emerges from atoms or molecules, possess unique reactivity and catalytic properties, crucial in catalysis, biomedicine, and optoelectronics. Predicting cluster energy provides insights into electronic structure, magnetism, and stability. However, the structure of clusters and their potential energy surface is exceptionally intricate. Searching for the global optimal structure (the lowest energy) among these isomers poses a significant challenge. Currently, modelling cluster energy predictions with traditional machine learning methods has several issues, including reliance on manual expertise, slow computation, heavy computational resource demands, and less efficient parameter tuning.ResultsThis paper introduces a predictive model for the energy of a gold cluster comprising twenty atoms (referred to as Au20 cluster). The model integrates the Multiple Strategy Fusion Whale Optimization Algorithm (MSFWOA) with the Light Gradient Boosting Machine (LightGBM), resulting in the MSFWOA-LightGBM model. This model employs the Coulomb matrix representation and eigenvalue solution methods for feature extraction. Additionally, it incorporates the Tent chaotic mapping, cosine convergence factor, and inertia weight updating strategy to optimize the Whale Optimization Algorithm (WOA), leading to the development of MSFWOA. Subsequently, MSFWOA is employed to optimize the parameters of LightGBM for supporting the energy prediction of Au20 cluster.ConclusionsThe experimental results show that the most stable Au20 cluster structure is a regular tetrahedron with the lowest energy, displaying tight and uniform atom distribution, high geometric symmetry. Compared to other models, the MSFWOA-LightGBM model excels in accuracy and correlation, with MSE, RMSE, and R2 values of 0.897, 0.947, and 0.879, respectively. Additionally, the MSFWOA-LightGBM model possesses outstanding scalability, offering valuable insights for material design, energy storage, sensing technology, and biomedical imaging, with the potential to drive research and development in these areas.Graphical

Single-shot forward and inverse hierarchical architected materials design for nonlinear mechanical properties using an Attention-Diffusion model

Inspired by natural materials, hierarchical architected materials can achieve enhanced properties including achieving tailored mechanical responses. However, the design space for such materials is often exceedingly large, and both predicting mechanical properties of complex hierarchically organized materials and designing such materials for specific target properties can be extremely difficult. In this paper we report a deep learning approach using an attention-based diffusion model, capable of providing both, forward predictions of nonlinear mechanical properties as a function of the hierarchical material structure as well as solving inverse design problems in order to discover hierarchical microstructure candidates for a specified nonlinear mechanical response. We exemplify the method for a system of compressively loaded four-hierarchy level materials derived from a family of honeycomb structures, where patterns of distributed buckling events are unitary deformation events that control small- and large-scale deformation behavior. Our model offers exquisite single-shot end-to-end performance in both forward and inverse directions across the entire range of deformation regime, and is capable of rapidly discovering multiple solutions that satisfy a design objective in accordance with the known physical behavior elucidated by, and validated with, coarse-grained simulations. The model provides an effective way towards biologically inspired materials design for high-throughput discovery in order to achieve diverse nonlinear constitutive relationships.

Study the hierarchical structure of materials by scanning micro/nano diffraction

Study the hierarchical structure of materials by scanning micro/nano diffraction

Synthesis of activated carbon foams with high specific surface area using polyurethane elastomer templates for effective removal of methylene blue

Abstract Carbon foams have gained significant attention due to their tuneable properties that enable a wide range of applications including catalysis, energy storage and wastewater treatment. Novel synthesis pathways enable novel applications via yielding complex, hierarchical material structure. In this work, activated carbon foams (ACFs) were produced from waste polyurethane elastomer templates using different synthesis pathways, including a novel one-step method. Uniquely, the produced foams exhibited complex structure and contained carbon microspheres. The ACFs were synthesized by impregnating the elastomers in an acidified sucrose solution followed by direct activation using CO2 at 1000 ℃. Different pyrolysis and activation conditions were investigated. The ACFs were characterized by a high specific surface area (SBET) of 2172 m2/g and an enhanced pore volume of 1.08 cm3/g. Computer tomography and morphological studies revealed an inhomogeneous porous structure and the presence of numerous carbon spheres of varying sizes embedded in the porous network of the three-dimensional carbon foam. X-ray diffraction (XRD) and Raman spectroscopy indicated that the obtained carbon foam was amorphous and of turbostratic structure. Moreover, the activation process enhanced the surface of the carbon foam, making it more hydrophilic via altering pore size distribution and introducing oxygen functional groups. In equilibrium, the adsorption of methylene blue on ACF followed the Langmuir isotherm model with a maximum adsorption capacity of 592 mg/g. Based on these results, the produced ACFs have potential applications as adsorbents, catalyst support and electrode material in energy storage systems.

Mechanics of cellulose nanopaper using a scalable coarse-grained modeling scheme

Cellulose, the abundantly available and sustainable biopolymer, exhibits intrinsic mechanical properties superior to many high-performance structural materials. The exceptional mechanical properties of cellulose-based materials inherently hinge upon their bottom-up hierarchical material structure starting from cellulose molecular chains to large scale fibers. However, fully atomistic simulation of such materials at experimental sample dimension becomes computationally prohibitive for the exploration of mechanics involving length scale effects. To address this challenge, here we develop a bottom-up, scalable coarse-grained (CG) modeling scheme of cellulose materials to study the deformation and failure mechanism of cellulose-based materials with insight of the interplay among cellulose building blocks at different length scales, starting from molecular chain, to nanofiber, and finally to microfiber scales. After studying the response of cellulose fibers under different loadings such as shearing and opening, this CG scheme is applied to study the deformation process of a cellulose nanopaper under tension, thus revealing the nanoscale failure mechanism otherwise impossible by atomistic simulations. In addition, the CG model also predicts the strength and stiffness of the nanopaper with respect to varying fiber lengths. Given its scalable nature, such a CG modeling scheme can be readily adapted to study the mechanical behaviors of other cellulose-based materials with mechanistic insight from molecular scale, and thus holds promise to foster the design of cellulose-based high-performance materials.

Gaps and Barriers to Successful Integration and Adoption of Practical Materials Informatics Tools and Workflows

Preparation of the future materials workforce consistent with major imperatives rooted in integrated computational materials engineering (ICME) and the materials genome initiative (MGI) is most effectively pursued within the vision of a materials innovation ecosystem that spans across conventional engineering, science, and computing disciplines. The ICME foundation integrates principles of materials science with computational methods, including increasing reliance on modern data science methods that are savvy to digital information that recognizes hierarchical material structure and the need for correlative relations for process-structure and structure-property relations. We consider gaps in academic research and education programs related to systems engineering, uncertainty quantification of both experiments and computation, and data science methods. Barriers to the introduction of materials data science are discussed, as well as opportunities for innovation in educating the future MGI and ICME workforce.

Highly Elastic Hydrated Cellulosic Materials with Durable Compressibility and Tunable Conductivity.

Anisotropic cellular materials with direction-dependent structure and durable mechanical properties enable various applications (e.g., nanofluidics, biomedical devices, tissue engineering, and water purification), but their widespread use is often hindered by complex and scale-limited fabrication and unsatisfactory mechanical performance. Here, inspired by the anisotropic and hierarchical material structure of tendons, we demonstrate a facile, scalable top-down approach for fabricating a highly elastic, ionically conductive, anisotropic cellulosic material (named elastic wood) directly from natural wood via chemical treatment. The resulting elastic wood demonstrates good elasticity and durable compressibility, showing no sign of fatigue after 10 000 compression cycles. The chemical treatment not only softens the wood cell walls by partially removing lignin and hemicellulose but introduces an interconnected cellulose fibril network into the wood channels. Atomistic and continuum modeling further reveals that the absorbed water can freely and reversibly move inside the elastic wood and therefore helps the elastic wood accommodate large compressive deformation and recover to its original shape upon compression release. In addition, the elastic wood showed a high ionic conductivity of up to 0.5 mS cm-1 at a low KCl concentration of 10-4 M, which can be tuned by changing the compression ratio of the material. The demonstrated elastic, mechanically robust, and ionically conductive cellulosic material combining inherited anisotropic cellular structure from natural wood and a self-formed internal gel may find a variety of potential applications in ionic nanofluidics, sensors, soft robots, artificial muscle, environmental remediation, and energy storage.

Encoding materials for programming a temporal sequence of actions.

Materials are usually synthesized to allow a function that is either independent of time or that can be triggered in a specific environment. However in many cases, the materials experience different environments during their service and therefore they need to be adaptive so that useful functions are achieved during their lifecycle. New generation of materials need to be tailored to allow for a temporal sequence of reversible or irreversible responses in a cascade fashion. We discuss here strategies to encode a dynamic hierarchical structure of materials so that a temporal change of properties is created in different environments. Most of the examples are related to the sequenced release of drugs for combined therapy of cancer.

Revealing and exploiting hierarchical material structure through complex atomic networks

One of the great challenges of modern science is to faithfully model, and understand, matter at a wide range of scales. Starting with atoms, the vastness of the space of possible configurations poses a formidable challenge to any simulation of complex atomic and molecular systems. We introduce a computational method to reduce the complexity of atomic configuration space by systematically recognising hierarchical levels of atomic structure, and identifying the individual components. Given a list of atomic coordinates, a network is generated based on the distances between the atoms. Using the technique of modularity optimisation, the network is decomposed into modules. This procedure can be performed at different resolution levels, leading to a decomposition of the system at different scales, from which hierarchical structure can be identified. By considering the amount of information required to represent a given modular decomposition we can furthermore find the most succinct descriptions of a given atomic ensemble. Our straightforward, automatic and general approach is applied to complex crystal structures. We show that modular decomposition of these structures considerably simplifies configuration space, which in turn can be used in discovery of novel crystal structures, and opens up a pathway towards accelerated molecular dynamics of complex atomic ensembles. The power of this approach is demonstrated by the identification of a possible allotrope of boron containing 56 atoms in the primitive unit cell, which we uncover using an accelerated structure search, based on a modular decomposition of a known dense phase of boron, γ-B28.

An automated functional decomposition method based on morphological changes of material flows

ABSTRACTConceptual design has been paid increasing attention by academia and industry. However, there is no consensus of the concept of ‘function’ in the domain of conceptual design, making automated functional decomposition a difficult problem. The goal of this study is to develop an automated functional decomposition method based on the formal representation to model the morphological changes of material flows. Hierarchical material structure representation and a hierarchical shape graph are used to abstract the morphological properties of a material. Based on the semantics contained in the input and output material flows, together with decomposition principles, an overall function is broken into several sub-functions. Moreover, a sequence-planning algorithm of sub-functions is provided to facilitate achieving all of the morphological changes of a material flow. Finally, the proposed method is implemented, and some analyses are provided.

Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets

Structure quantification is key to successful mining and extraction of core materials knowledge from both multiscale simulations as well as multiscale experiments. The main challenge stems from the need to transform the inherently high dimensional representations demanded by the rich hierarchical material structure into useful, high value, low dimensional representations. In this paper, we develop and demonstrate the merits of a data-driven approach for addressing this challenge at the atomic scale. The approach presented here is built on prior successes demonstrated for mesoscale representations of material internal structure, and involves three main steps: (i) digital representation of the material structure, (ii) extraction of a comprehensive set of structure measures using the framework of n-point spatial correlations, and (iii) identification of data-driven low dimensional measures using principal component analyses. These novel protocols, applied on an ensemble of structure datasets output from molecular dynamics (MD) simulations, have successfully classified the datasets based on several model input parameters such as the interatomic potential and the temperature used in the MD simulations.

XFEM Modelling of Inelastic Material Behaviour and Interface Failure in Textile-Reinforced Composites

This paper addresses the multiscale simulation of heterogeneous materials. The considered composite materials exhibit a hierarchical material structure with three distinct length scales - micro, meso and macro. This feature of the morphology allows for the application of homogenisation techniques based on a representative volume element (RVE) which is entirely typical for the local, periodic material structure.The heterogeneous material structure in an RVE is modelled by the extended finite element method (XFEM) Suitable material models account for the deformation behaviour of the constituents in the generated RVE models. The combination of XFEM and a cohesive zone model is used to represent discrete failure processes in the local material structure. This multiscale approach is applied to predict the effective material behaviour of fibre reinforced polymers. Using periodic displacement boundary conditions, effective stress-strain curves are computed for glass fibre reinforced polypropylene with unidirectional and woven arrangements of the reinforcing fibres.

Poisson's ratio over two centuries: challenging hypotheses

This article explores Poisson's ratio, starting with the controversy concerning its magnitude and uniqueness in the context of the molecular and continuum hypotheses competing in the development of elasticity theory in the nineteenth century, moving on to its place in the development of materials science and engineering in the twentieth century, and concluding with its recent re-emergence as a universal metric for the mechanical performance of materials on any length scale. During these episodes France lost its scientific pre-eminence as paradigms switched from mathematical to observational, and accurate experiments became the prerequisite for scientific advance. The emergence of the engineering of metals followed, and subsequently the invention of composites-both somewhat separated from the discovery of quantum mechanics and crystallography, and illustrating the bifurcation of technology and science. Nowadays disciplines are reconnecting in the face of new scientific demands. During the past two centuries, though, the shape versus volume concept embedded in Poisson's ratio has remained invariant, but its application has exploded from its origins in describing the elastic response of solids and liquids, into areas such as materials with negative Poisson's ratio, brittleness, glass formation, and a re-evaluation of traditional materials. Moreover, the two contentious hypotheses have been reconciled in their complementarity within the hierarchical structure of materials and through computational modelling.

Key computational modeling issues in Integrated Computational Materials Engineering

Designing materials for targeted performance requirements as required in Integrated Computational Materials Engineering (ICME) demands a combined strategy of bottom–up and top–down modeling and simulation which treats various levels of hierarchical material structure as a mathematical representation, with infusion of systems engineering and informatics to deal with differing model degrees of freedom and uncertainty. Moreover, with time, the classical materials selection approach is becoming generalized to address concurrent design of microstructure or mesostructure to satisfy product-level performance requirements. Computational materials science and multiscale mechanics models play key roles in evaluating performance metrics necessary to support materials design. The interplay of systems-based design of materials with multiscale modeling methodologies is at the core of materials design. In high performance alloys and composite materials, maximum performance is often achieved within a relatively narrow window of process path and resulting microstructures.Much of the attention to ICME in the materials community has focused on the role of generating and representing data, including methods for characterization and digital representation of microstructure, as well as databases and model integration. On the other hand, the computational mechanics of materials and multidisciplinary design optimization communities are grappling with many fundamental issues related to stochasticity of processes and uncertainty of data, models, and multiscale modeling chains in decision-based design. This paper explores computational and information aspects of design of materials with hierarchical microstructures and identifies key underdeveloped elements essential to supporting ICME. One of the messages of this overview paper is that ICME is not simply an assemblage of existing tools, for such tools do not have natural interfaces to material structure nor are they framed in a way that quantifies sources of uncertainty and manages uncertainty in representing physical phenomena to support decision-based design.

Multiscale XFEM‐modelling and simulation of the inelastic material behaviour of textile‐reinforced polymers

AbstractThis paper addresses the multiscale simulation of fibre‐reinforced polymers. The considered composite materials exhibit a hierarchical material structure with three distinct length scales—micro, meso and macro. This feature of the morphology allows for the application of homogenization techniques based on a representative volume element (RVE) that is entirely typical for the local, periodic material structure. The effective macroscopic material behaviour of the composite can be predicted from the properties of the individual constituents and the geometric arrangement of the reinforcing fibres based on the simulation of the material behaviour in the RVE.The heterogeneous material structure in an RVE is modelled by the eXtended finite element method (XFEM). To this, two special element types, called X‐element and 2X‐element, are derived. They can represent one or two material interfaces within the element domain. For an efficient modelling process, an automated model generation procedure that determines the required element types, locates the material interfaces and performs the subdivision of the X‐elements into tetrahedral integration domains has been developed. Problems related to a consistent interface approximation and a continuous displacement field are discussed.In the generated RVE models, a viscoplastic material model accounts for the inelastic material behaviour of the polymeric matrix, whereas the glass‐fibres are assumed to have a linear elastic stress–strain behaviour. Using periodic displacement boundary conditions, effective stress–strain curves are computed for glass‐fibre‐reinforced polypropylene with unidirectional and woven arrangements of the reinforcing fibres. Copyright © 2010 John Wiley & Sons, Ltd.

Mesoscopics of the concept of a hierarchical structure of materials

Various aspects of the concept of a hierarchical structure of materials are analyzed with a view to practical applications. It is shown that the principal complications can be removed if the hierarchical structure is represented by a block diagram that contains a block of mesoscopic layers. One way of theoretically describing the structure of mesodisordered media is demonstrated.