Tool For Material Design Research Articles

Singular topological edge states in locally resonant metamaterials.

Band topology has emerged as a novel tool for material design across various domains, including photonic and phononic systems, and metamaterials. A prominent model for band topology is the Su-Schrieffer-Heeger (SSH) chain, which reveals topological in-gap states within Bragg-type gaps (BG) formed by periodic modification. Apart from classical BGs, another mechanism for bandgap formation in metamaterials involves strong coupling between local resonances and propagating waves, resulting in a local resonance-induced bandgap (LRG). Previous studies have shown the challenge of topological edge state emergence within the LRG. Here, we reveal that topological edge states can emerge within an LRG by achieving both topological phase and bandgap transitions simultaneously. We describe this using a model of inversion-symmetric extended SSH chains for locally resonant metamaterials. Notably, this topological state can lead to highly localized modes, comparable to a subwavelength unit cell, when it emerges within the LRG. We experimentally demonstrate distinct differences in topologically protected modes-highlighted by wave localization-between the BG and the LRG using locally resonant granule-based metamaterials. Our findings suggest the scope of topological metamaterials may be extended via their bandgap nature.

Effect of fly ash on mechanical properties and microstructural behaviour of injected moulded recycled polypropylene composites

The global challenges of plastic waste and fly ash accumulation demand innovative recycling solutions. In Malaysia, the consumption of 11.2 million tonnes of coal annually generates approximately 2 million tonnes of fly ash, while the recycling of Polypropylene (PP) is hindered by thermo-oxidative and shear-induced degradation during mechanical recycling, resulting in reduced mechanical properties. This study addresses these issues by fabricating recycled PP composites incorporating varying fly ash contents, using a single cycle of mechanical recycling with extrusion and injection moulding methods. The composites were comprehensively tested for tensile, flexural, and compressive strengths, as well as microstructural behaviour. The results reveal a complex relationship between fly ash content and mechanical performance: while tensile strength decreases with increasing fly ash, flexural strength and strain peak at 5 % and 9 % fly ash, respectively. Compressive strength also shows a slight increase up to 7 % fly ash, indicating an optimal range for enhancing mechanical performance. Furthermore, mathematical models were proposed to predict the mechanical properties of rPP – fly ash composites, providing a valuable tool for future material design. These findings demonstrate that optimization of fly ash content can mitigate the effects of polymer degradation, offering a sustainable solution to improving the mechanical performance of this composite.

A deep neural network potential model for transition metal diborides

Transition metal diborides (TMB2s) are renowned for their high melting point and exceptional wear, corrosion, and erosion resistance, making them promising candidate materials for applications in extreme environments. As such, there is an urgent need for reliable material design tools for TMB2s to improve efficiency in developing new materials. To address this need, we have developed a domain-specific medium-scale interatomic potential model for TMB2s that encompasses elements Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W, and B. The prediction errors in energy and force of our model are 8.8 meV/atom and 387 meV/Å, respectively. Furthermore, the model demonstrates high accuracy in predicting various material properties, including lattice parameters, elastic constants, equations of states, and melting points, as well as grain boundary segregations. By providing a reliable and efficient tool for material design, this model will play a crucial role in the development of new, high-performance TMB2s for use in extreme environments.

Predicting surface-energy anisotropy of metals with geometric properties of surfaces and atoms

Surface-energy anisotropy of metals is crucial for the stability and structure, however, its determining factors and structure-property relationship are still elusive. Herein, we identify three key factors for predicting surface-energy anisotropy of pure metals and alloys: the surface-atom density, coordination numbers and atomic radius. We find that the coupling rules of surface geometric determinants, which determining surface-energy anisotropy of face-centred-cubic (FCC), hexagonal-close-packed (HCP) and body-centred-cubic (BCC) metals, are essentially controlled by the crystal structures instead of chemical bonds, alloying or electronic structures. Furthermore, BCC metals exhibit material-dependent surface-energy anisotropy depending on the atomic radius, unlike FCC and HCP metals. The underlying mechanism can be understood from the bonding properties in the framework of the tight-binding model. Our scheme provides not only a new physical picture of surface stability but also a useful tool for material design.

Meso-scale finite element modelling of biaxial non-crimp-fabric composites under compression

Non-crimp-fabric (NCF) reinforced composites have been receiving attention in the composite market due to their cost-effectiveness and excellent mechanical performance. However, compared to traditional unidirectional laminates (UD), NCF composites have different mechanical behaviours due to their heterogeneous internal structures. The influence of the internal structure of biaxial NCF composites on compressive behaviour was therefore investigated in this study. The representative volume element (RVE) method was adopted to establish finite element (FE) models for biaxial NCF composites at the micro- and meso‑scale. Micro-scale models developed at fibre/matrix level could provide homogenised compressive properties of 0° and 90° tows, while meso‑scale repeated unit cell (RUC) models developed at tow/resin-rich area level could predict homogenised compressive behaviours of biaxial NCF composites. Finally, the homogenised meso‑RUC results were employed in the macro-scale FE model to validate the test result. The novelty lies in developing RUC models to comprehensively investigate key parameters at the micro- and meso‑scale controlling the overall compressive strength, including matrix strength, layup sequence, tow waviness, tow placement, interfacial strength, and out-of-plane shear stress. Another novelty is to explain the differences in compressive strengths between the RUC model and the test result. These models form a multiscale framework for biaxial NCF composites, which could contribute to an analysis tool for NCF material design. The FE results by this multiscale framework indicated that the misaligned resin-rich areas could contribute to the higher compressive strength. Increasing tow waviness could change the failure mode from fibre crushing to fibre kinking failure, while matrix properties, interfacial strength, and fibre waviness were the key factors influencing compressive strength. The conclusion can further guide the development of NCF composites with optimal compressive strength.

Towards implementation of alloy-specific thermo-fluid modelling for laser powder-bed fusion of Mg alloys

Multi-physics thermo-fluid modeling has been extensively used as an approach to understand melt pool dynamics and defect formation as well as optimizing the process-related parameters of laser powder-bed fusion (L-PBF). However, its capabilities for being implemented as a reliable tool for material design, where minor changes in material-related parameters must be accurately captured, is still in question. In the present research, first, a thermo-fluid computational fluid dynamics (CFD) model is developed and validated against experimental data. Considering the predicted material properties of the pure Mg and commercial ZK60 and WE43 Mg alloys, parametric studies are done attempting to elucidate how the difference in some of the material properties, i.e., saturated vapor pressure, viscosity, and solidification range, can influence the melt pool dynamics. It is found that a higher saturated vapor pressure, associated with the ZK60 alloy, leads to a deeper unstable keyhole, increasing the keyhole-induced porosity and evaporation mass loss. Higher viscosity and wider solidification range can increase the non-uniformity of temperature and velocity distribution on the keyhole walls, resulting in increased keyhole instability and formation of defects. Finally, the WE43 alloy showed the best behavior in terms of defect formation and evaporation mass loss, providing theoretical support to the extensive use of this alloy in L-PBF. In summary, this study suggests an approach to investigate the effect of materials-related parameters on L-PBF melting and solidification, which can be extremely helpful for future design of new alloys suitable for L-PBF.

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

Lattice Design and Advanced Modeling to Guide the Design of High-Performance Lightweight Structural Materials

Lightweight structural materials are required to increase the mobility of fission batteries. The materials must feature a robust combination of mechanical properties to demonstrate structural resilience. The primary objective of this project is to produce lightweight structural materials whose strength-to-weight ratios exceed those of the current widely used structural materials such as 316L stainless steels (316L SS). To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structures models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The preliminary results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison with the solid material and that the unit cell size of the lattice had a minimal effect. The novelty here is to apply up-front modeling to determine the best structure for the application before actually producing the sample. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for material development.

A neural network potential based on pairwise resolved atomic forces and energies.

Molecular simulations have become a key tool in molecular and materials design. Machine learning (ML)-based potential energy functions offer the prospect of simulating complex molecular systems efficiently at quantum chemical accuracy. In previous work, we have introduced the ML-based PairF-Net approach to neural network potentials, that adopts a pairwise interatomic scheme to predicting forces within a molecular system. Here, we further develop the PairF-Net model to intrinsically incorporate energy conservation and couple the model to a molecular mechanical (MM) environment within the OpenMM package. The updated PairF-Net model yields energy and force predictions and dynamical distributions in good agreement with the rMD17 dataset of ten small organic molecules in the gas-phase. We further show that these in vacuo ML models of small molecules can be applied to force predictions in aqueous solution via hybrid ML/MM simulations. We present a new benchmark dataset for these ten molecules in solution, obtained from QM/MM simulations, which we denote as rMD17-aq (https://zenodo.org/records/10048644); and assess the ability of PairF-Net to reproduce the molecular energy, atomic forces and dynamical distributions of these solution conformations via ML/MM simulations.

Artificial neural networks for predicting optical conversion efficiency in luminescent solar concentrators

Developing light-harvesting materials able to shape the sunlight to cope with the absorption region of photovoltaic (PV) cells presents an opportunity for the utilization of spectral converters like the luminescent solar concentrators (LSCs). This study explores the use of artificial neural networks (ANNs) to predict the optical conversion efficiency of spectral converters, based on the material properties employed in their production, without the need for expensive and time-consuming experimental testing. To predict efficiency as a function of materials and manufacturing processes, ANNs were trained using data from previously documented physical implementations. The findings indicate that ANNs, having 97 and 19 neurons in the hidden layers, provide accurate efficiency predictions, making them a valuable tool for designing and optimizing spectral converting systems. The proposed model was validated and got a mean square error in the order of 10−5 for the optical conversion efficiency. The trained ANN introduced a novel methodology for predicting the optical efficiency of spectral converters, opening the door to the application of machine learning as a decision-making tool for material design, and eliminating the necessity for physical device implementations.

Defects go green: using defects in nanomaterials for renewable energy and environmental sustainability

Induction of point defects in nanomaterials can bestow upon them entirely new physics or augment their pre-existing physical properties, thereby expanding their potential use in green energy technology. Predicting structure-property relationships for defects a priori is challenging, and developing methods for precise control of defect type, density, or structural distribution during synthesis is an even more formidable task. Hence, tuning the defect structure to tailor nanomaterials for enhanced device performance remains an underutilized tool in materials design. We review here the state of nanomaterial design through the lens of computational prediction of defect properties for green energy technology, and synthesis methods to control defect formation for optimal performance. We illustrate the efficacy of defect-focused approaches for refining nanomaterial physics by describing several specific applications where these techniques hold potential. Most notably, we focus on quantum dots for reabsorption-free solar windows and net-zero emission buildings, oxide cathodes for high energy density lithium-ion batteries and electric vehicles, and transition metal dichalcogenides for electrocatalytic green hydrogen production and carbon-free fuels.

Rough colloids at fluid interfaces: from fundamental science to applications

Colloidal particles pinned to fluid interfaces have applications ranging from Pickering emulsions and foams to the development of 2D materials via Langmuir-Blodgett deposition. While colloids come in virtually any size, shape, and chemistry, particle surface topography, or roughness, has recently found renewed interest as a design parameter for controlling interfacial pinning, capillary interactions, assembly, and mechanics of particulate monolayers. In this review, we highlight the fundamental science regarding rough colloidal particles at fluid interfaces and how manipulating roughness can be a tool for material design, rather than merely a characteristic needing to be dealt with. While existing work reveals the importance of roughness, the field is still rather nascent and therefore this review highlights both challenges and opportunities for future research.

Investigating elastic constants across diverse strain-matrix sets

Elastic constants and mechanical properties play a pivotal role across multiple disciplines and engineering applications. We introduced the optimized high-efficient strain-matrix set (OHESS) that determines the second-order elastic constants of materials using the stress–strain method. Herein, we systematically investigate the computational efficiency of OHESS across a broad range of crystal systems and compare it with other notable stress–strain approaches, such as the single-element strain-matrix sets and the universal linear-independent coupling strains. Notably, our data affirm the superior efficacy of OHESS among the strain-matrix sets under consideration. We believe OHESS will markedly improve computational efficiency in determining the elastic constants and mechanical properties, becoming an indispensable tool for material research, design, and high-throughput screening.

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. The results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

Ultrafast and facile construction of programmable, multidimensional wrinkled-patterned polyacrylamide/sodium alginate hydrogels for human skin-like tactile perception

Customizable structures and patterns are becoming powerful tools for biomimetic design and application of soft materials. The construction of long-range ordered self-wrinkled structures on multi-dimensional and complex-shaped surfaces with facile, fast and efficient strategies still faces serious challenges. During the stretch-recovery process, the carboxyl groups in the polyacrylamide/sodium alginate dual network gel form robust coordination with Fe3+ to achieve a hard shell layer, resulting in a modulus mismatch between the inner soft layer and the outer hard layer, thereby forming a wrinkled surface. This flexible strategy allows simultaneous construction of complex topologies from 1D to 3D wits well-organized microstructure and controllable dimensions. The mechanism of the influence of ion treating time and pre-stretching ratio on wrinkle wavelength was explored in detail. The finite element simulations matched well with the experimental results. Due to the unique surface and dual crosslinking network, the self-wrinkled hydrogel maintains a high sensitivity of up to 67.47 kPa−1 in 1000 compression cycles. As a high-sensitivity pressure sensor integrated into the detection system, it can be efficiently applied to the contact dynamic tactile perception and monitoring of various movement behaviors of the human body.

Kawin: An open source Kampmann–Wagner Numerical (KWN) phase precipitation and coarsening model

Kawin, a new open-source implementation of the Kampmann–Wagner Numerical model of precipitation (concomitant nucleation, growth, and coarsening), is presented. An overview of the organization and capabilities of the program is provided, along with an outline of the constituent physics. Kawin is shown to be able to reproduce the results of state-of-the-art commercial software and experimental data for a variety of alloy systems under multiple precipitation conditions. Kawin is capable of simulating the bulk precipitation behavior of multiphase, multicomponent systems in response to complex heat treatments, and contains numerous innovative features to enhance model stability, improve flexibility and usability, and minimize computational expense. Kawin also incorporates sophisticated elastic energy calculations, traditionally ignored in this type of simulation but shown here to significantly impact the precipitation behavior of some systems. The inclusion of native strain calculations enables Kawin to predict the influence of internal or external stress fields on precipitation, as well as track the evolution of precipitate geometry throughout the course of a heat treatment. It is the hope of these authors that this software will facilitate the advancement of precipitation modeling as a tool for materials design.

An improved composition design method for high-performance copper alloys based on various machine learning models

The preparation of high-performance copper alloys generally considers alloying approaches to solve the conflicting problems of high strength and high electrical conductivity. The traditional “trial and error” research model is complicated and time-consuming. With the continuous accumulation of material databases and the advent of the “big data” era, machine learning has rapidly become a powerful tool for material design and development. In this paper, a total of 407 copper alloy data were collected. In the multi-objective prediction problem, the many-to-many prediction using back propagation neural network alone is improved to a many-to-one prediction. This improvement is based on back propagation neural network, tree model and support vector machine model. Through comparative analysis, an improved composition to property model was developed to predict the tensile strength and electrical conductivity of copper alloys, and the overall coefficient of determination reached 0.98; an improved property to composition model was developed to predict the composition of copper alloys, and the overall coefficient of determination reached 0.78. By combining these two models and the particle swarm optimization algorithm, an improved machine learning design system (MLDS) model was developed to achieve the composition prediction of copper alloy. The overall coefficient of determination reached 0.87, the prediction effect was better than the original MLDS model and with stronger stability. This method is of guiding significance for the alloy composition design of high-performance copper alloys. In addition, it also has certain reference value for the alloy composition design of other alloys.

Visualization of deformation-induced changes in carbon nanotube networks in rubber composites using lock-in thermography.

In this study, we used the lock-in thermography technique (LIT) to successfully visualize the single-walled carbon nanotube (CNT) networks during the tensile deformation of CNT/fluoro-rubber (FKM) composites. The LIT images revealed that the CNT network modes in CNT/FKM during strain-loading and unloading can be classified into four sites: (i) disconnection, (ii) recovery after disconnection, (iii) undestroyable, and (iv) no network. Quantitative analysis of the heat intensity of the LIT also indicated that the change in resistance during strain-loading and unloading plays a role in the balance of disconnection and reconstruction of the conductive network. We demonstrated the ability of LIT to effectively visualize and quantify the network state of the composite under deformation, and the LIT results were found to be strongly correlated with the composite properties. These results highlighted the potential of LIT as a valuable tool for composite characterization and material design.

Effects of martensitic phase transformation on fatigue indicator parameters determined by a crystal plasticity model

Fatigue indicator parameters are a powerful tool for materials design. These parameters represent the potency of various microstructures to incubate fatigue cracks in metal alloys and are useful in ranking deleterious microstructural features on which to focus process improvements. These fatigue indicator parameters are generally a function of irreversible plastic strain; however, for alloys with solid-state phase transformations (such as Nitinol), reversible transformation strains occur along with plastic strain. This article addresses the question of whether modeling a reversible transformation will affect the predicted value of a fatigue indicator parameter. For Nitinol, the effects of a reversible phase transformation on the fatigue indicator parameter and fatigue life are studied for a polygranular microstructure with and without an inclusion. The crystal mechanics of both plasticity and phase transformations are modeled using the finite element method. It is shown that the usage of a crystal-mechanics-based phase transformation model affects the values of fatigue indicator parameters, the shape of predicted strain–life curves, the distribution of fatigue indicator parameter values in the microstructure, and also the rank ordering of deleterious microstructural features.

Editorial: Self-assembly as a tool for functional materials design

Editorial: Self-assembly as a tool for functional materials design