Material Design Framework Research Articles

Identification of an affordable and printable metastable β Ti alloy with outstanding deformation behaviour for use in laser powder bed fusion

Research on metastable β Ti alloys has recently shown the possibility to achieve outstanding ductility and work hardening behaviours by engineering the materials’ unique strain transformable attributes and responses to heat treatment. As quenching is central to the development of the metastable phase in these alloys, it is of interest to understand how these materials could be designed and developed for use in laser additive manufacturing where feedstocks experience numerous rapid thermal cycles. In this context, we first propose a material design framework to identify printable metastable Ti alloys for laser powder bed fusion (L-PBF). We focus on the ternary Ti-Cr-Sn system for both its affordable character and interest in the biomedical field. The design framework combines criteria based on well-established empirical parameters that we use to compare stability of β phase and resistance to cracking during L-PBF, i.e. its printability. The design work is supported by series of microstructural investigations on arc-melted buttons of selected compositions. Using such methodology we identify Ti-7.5Cr-4.5Sn as strain transformable alloy of good printability and we then characterise this alloy in depth in a series of laser powder bed fusion experiments. Printability of this alloy is further demonstrated via printing experiments using a variety of processing conditions while the deformation character is studied using compressive testing. It was found that the identified Ti-7.5Cr-4.5Sn alloy can achieve a high compressive strain exceeding 70 % thanks to deformation mechanisms that combine dislocation slip and {3 3 2}<1 1 3> twinning. This research paves the way towards the identification of novel Ti alloys for L-PBF beyond those used in the aerospace sector, opening the way for a broader field of applications.

Intelligent electroactive material systems with self-adaptive mechanical memory and sequential logic

By synthesizing the requisite functionalities of intelligence in an integrated material system, it may become possible to animate otherwise inanimate matter. A significant challenge in this vision is to continually sense, process, and memorize information in a decentralized way. Here, we introduce an approach that enables all such functionalities in a soft mechanical material system. By integrating nonvolatile memory with continuous processing, we develop a sequential logic-based material design framework. Soft, conductive networks interconnect with embedded electroactive actuators to enable self-adaptive behavior that facilitates autonomous toggling and counting. The design principles are scaled in processing complexity and memory capacity to develop a model 8-bit mechanical material that can solve linear algebraic equations based on analog mechanical inputs. The resulting material system operates continually to monitor the current mechanical configuration and to autonomously search for solutions within a desired error. The methods created in this work are a foundation for future synthetic general intelligence that can empower materials to autonomously react to diverse stimuli in their environment.

Machine learning-assisted high-throughput screening of transparent organic light-emitting diode anode materials

A target-driven material design framework for the rapid work function prediction of AB-type 2D nanomaterials is proposed to accelerate the discovery of transparent OLED anode materials.

Socio-Scientific Issues-Based Electronic Learning Material Design Framework Development for Flexible Learning

The study aims to design a socio-scientific issues (SSIs)-based electronic learning (℮-LM) design framework. It is anchored to the SSI occurrences in teaching Science at a State University in Batangas province, Philippines. This SSI-based ℮-LM design framework will guide the ℮-LM development for teaching Science socio-contextually through flexible learning. The explanatory sequential mixed method design addressed the study’s goal. Initially, the existing SSIs in the science course curriculum were analyzed. Significant insights into the occurrence of existing SSIs, such as the appropriate teaching approaches, suitable teaching strategies, assessments, and ℮-LM design structure, were identified. The resulting least occurring SSI identified and factors affecting their existence comprised the salient features of the proposed e-LM design framework. Findings showed that lifelong learning issues, ethical issues, and sustainability issues were almost “no occurrences at all” SSIs in the analysis. These identified SSIs became the priority SSIs in ℮-LM design framework. Significant insights showed that flexible learning and socio-contextualization are suitable teaching approaches. Meanwhile, the teaching strategies that were found appropriate in the SSI inclusion include brain-netting, discovering scientific ideas, ℮-collaboration, and reflective thinking evaluation. The ℮-LM design framework proposed is called Brain-D℮CoR ℮-LM Framework. Applying the proposed ℮-LM design framework in teaching Science will encourage science facilitators to situate priority SSIs in teaching Science. This results in creating a balance in SSI inclusion. The least occurring SSIs, which are the priority SSIs, are given into focus. Furthermore, the proposed framework will aid students in learning science beyond the content, developing their skills, and enhancing their environmental concerns and social awareness.

Push, snap, pull, and buckle: A material design framework enabled by cooperating active and geometrically nonlinear passive microstructures

Many active materials have properties that make them challenging to design with or limit their utility, including having combinations of nonlinear, viscous, slow, or small responses. In this work, we show that by designing metamaterial unit cells that have separately active and, specifically geometrically nonlinear, passive elements, many of these drawbacks can be mitigated and enhanced performance enabled (specifically, large, rapid, and resettable shape change). This separation between active and passive elements is advantageous, in that the complex active materials can be assigned only where needed, with the simplest geometries and functions. The use of geometric nonlinearity in passive elements is helpful as it provides mechanisms for augmented performance and further simplifies the role of the active elements. While all elements of the microstructural motifs used herein have been previously seen elsewhere, we suggest that the contribution provided here is a conceptual design approach that may be used more broadly for enabling new types of high-performing, stimuli-responsive metamaterials.

Design and development of polymer composite heat exchanger tubes using an integrated thermal-hydraulic and material design framework

In the current study, thermal-hydraulic design of the heat exchanger and composite material design are integrated to develop polymer composite tube materials for heat exchanger applications. For preliminary analysis, the scheme utilizes basic thermal resistance equations, Kern and Bell-Delaware methods for the design of baffled shell and tube heat exchangers, and differential effective medium theory for the design of composite materials. The preliminary analysis clarifies that the thermal conductivity of tubes is a performance-limiting parameter in the case of liquid-liquid applications. The heat exchanger's design imposes that the tubes' thermal conductivity must be enhanced to ≥8.5 W/m.K for achieving heat transfer comparable to those of metal counterparts. To attain the threshold thermal conductivity, the design of polymer composites employing differential effective medium theory requires that high volume fractions (> 0.3) of high effective aspect ratio (> 10 or 0.1) thermally conductive fillers be incorporated in a polymer matrix. Finally, the samples are fabricated of polypropylene matrix and expandable graphite filler with variable volume fractions (0.1–0.4). The highest thermal conductivity of 8.3 W/m.K was achieved in a 40 vol% sample. The comparison of measured and computed thermal conductivity values shows an acceptable agreement supported by electron microscopy and thermal images.

Artificial Intelligence Guided Thermoelectric Materials Design and Discovery

AbstractMaterials discovery from the infinite earth repository is a major bottleneck for revolutionary technological progress. This labor‐intensive and time‐consuming process hinders the discovery of new materials. Although machine learning techniques show an excellent capability for speeding up materials discovery, obtaining effective material feature representations is still challenging, and making a precise prediction of the material properties is still tricky. This work focuses on developing an automatic material design and discovery framework enabled by data‐driven artificial intelligence (AI) models. Multiple types of material descriptors are first developed to promote the representation and encoding of the materials’ uniqueness, resulting in improved performance for different molecular properties predictions. The material's thermoelectric (TE) properties prediction is then utilized as a baseline to demonstrate the investigation logistic. The proposed framework achieves more than 90% accuracy for predicting materials' TE properties. Furthermore, the developed AI models identify 6 promising p‐type TE materials and 8 promising n‐type TE materials. The prediction results are evaluated by density functional theory calculations and agree with the material's TE property provided by experimental results. The proposed framework is expected to accelerate the design and discovery of the new functional materials.

Target-Driven Design of Deep-UV Nonlinear Optical Materials via Interpretable Machine Learning.

The development of a data-driven science paradigm is greatly revolutionizing the process of materials discovery. Particularly, exploring novel nonlinear optical (NLO) materials with the birefringent phase-matching ability to deep-ultraviolet (UV) region is of vital significance for the field of laser technologies. Herein, a target-driven materials design framework combining high-throughput calculations (HTC), crystal structure prediction, and interpretable machine learning (ML) is proposed to accelerate the discovery of deep-UV NLO materials. Using a dataset generated from HTC, an ML regression model for predicting birefringence is developed for the first time, which exhibits a possibility of achieving fast and accurate prediction. Essentially, crystal structures are adopted as the only known input of this model to establish a close structure-property relationship mapping birefringence. Utilizing the ML-predicted birefringence which can affect the shortest phase-matching wavelength, a full list of potential chemical compositions based on an efficient screening strategy is identified. Further, eight structures with good stability are discovered to show potential applications in the deep-UV region, owing to their promising NLO-related properties. This study provides a new insight into the discovery of NLO materials and this design framework can identify desired materials with high performances in the broad chemical space at a low computational cost.

Multi-objective Bayesian optimization for the design of nacre-inspired composites: optimizing and understanding biomimetics through AI.

The hierarchical structures found in biological materials lead to an outstanding balance of multiple material properties, and numerous research studies have been initiated to emulate the key concepts for the designing of engineering materials, the so-called bioinspired composites. However, the optimization of bioinspired composites has long been difficult as it usually falls into the category of 'black-box problem', the objective functions not being available in a functional form. Also, bioinspired composites possess multiple material properties that are in a trade-off relationship, making it impossible to reach a unique optimal design solution. As a breakthrough, we propose a data-driven material design framework which can generate bioinspired composite designs with an optimal balance of material properties. In this study, a nacre-inspired composite is chosen as the subject of study and the optimization framework is applied to determine the designs that have an optimal balance of strength, toughness, and specific volume. Gaussian process regression was adopted for the modeling of a complex input-output relationship, and the model was trained with the data generated from the crack phase-field simulation. Then, multi-objective Bayesian optimization was carried out to determine pareto-optimal composite designs. As a result, the proposed data-driven algorithm could generate a 3D pareto surface of optimal composite design solutions, from which a user can choose a design that suits his/her requirement. To validate the result, several pareto-optimal designs are built using a PolyJet 3D printer, and their tensile test results show that each of the characteristic designs is well optimized for its specific target objective.

Statistical analysis and visualization of data of non-fullerene small molecule acceptors from Harvard organic photovoltaic database. Structural similarity analysis with famous non-fullerene small molecule acceptors to search new building blocks

Data-driven material design has gained the position of “fourth paradigm” with the first three being experiments, theory, and simulation. The statistical analysis and visualization of data can provide many information about hidden trends of data. It also can help to plan future material design in efficient way. Traditional methods need long experimental time and much money. Data driven research has become new hope to revolutionize materials discovery. A detail statistical and visualization analysis of Harvard organic photovoltaic database is performed. Various new information is found. Similarity analysis is also performed. Famous experimentally reported non-fullerene small acceptors are selected as reference molecules. Very less similarity is found that is indicating that acceptors in Harvard organic photovoltaic database are much different from acceptors that are reported in literature. The molecules in database are not suitable acceptors, however, their suitable combination can result efficient acceptors of organic solar cells. New acceptors are designed using some building block from database and their electronic properties are studied using density functional theory. Our data mining assisted material design framework is based on unique approach that is rare in photovoltaics research. This framework will be helpful to find versatile building blocks.

Thermo-mechanical properties prediction of Ni-reinforced Al2O3 composites using micro-mechanics based representative volume elements

For effective cutting tool inserts that absorb thermal shock at varying temperature gradients, improved thermal conductivity and toughness are required. In addition, parameters such as the coefficient of thermal expansion must be kept within a reasonable range. This work presents a novel material design framework based on a multi-scale modeling approach that proposes nickel (Ni)-reinforced alumina (Al2O3) composites to tailor the mechanical and thermal properties required for ceramic cutting tools by considering numerous composite parameters. The representative volume elements (RVEs) are generated using the DREAM.3D software program and the output is imported into a commercial finite element software ABAQUS. The RVEs which contain multiple Ni particles with varying porosity and volume fractions are used to predict the effective thermal and mechanical properties using the computational homogenization methods under appropriate boundary conditions (BCs). The RVE framework is validated by the sintering of Al2O3-Ni composites in various compositions. The predicted numerical results agree well with the measured thermal and structural properties. The properties predicted by the numerical model are comparable with those obtained using the rules of mixtures and SwiftComp, as well as the Fast Fourier Transform (FFT) based computational homogenization method. The results show that the ABAQUS, SwiftComp and FFT results are fairly close to each other. The effects of porosity and Ni volume fraction on the mechanical and thermal properties are also investigated. It is observed that the mechanical properties and thermal conductivities decrease with the porosity, while the thermal expansion remains unaffected. The proposed integrated modeling and empirical approach could facilitate the development of unique Al2O3-metal composites with the desired thermal and mechanical properties for ceramic cutting inserts.

Predictive materials and structures design of perpendicular magnetic anisotropy in magnetic tunnel junctions

The regular mechanism guiding for the selection of heavy metals (HMs) that adapt to different structures is thoroughly investigated. A predictive materials design framework is established based on the element distribution states analysis from high-resolution transmission electron microscopy (HRTEM) experiments to guide the complex design explorations for spin-orbit torque (SOT) devices. Based on surface free energy and formation enthalpy analyses, it is found that not only a critical HM material type but also an appropriate structure selection will contribute to the excellent magnetic properties required for SOT device control and design. Furthermore, the HM layers with high formation enthalpy value are critical for device performance enhancement, and HM layers with a high (low) surface free energy are better choices for the bottom (top) structure. The researches help to establish a predictive materials design framework, and is effective in designing memory cells with critical SOT effect.

On the importance of microstructure information in materials design: PSP vs PP

The focus of goal-oriented materials design is to find the necessary chemistry/processing conditions to achieve the desired properties. In this setting, a material’s microstructure is either only used to carry out multiscale simulations to establish an invertible quantitative process-structure-property (PSP) relationship, or to rationalize a posteriori the underlying microstructural features responsible for the properties achieved. The materials design process itself, however, tends to be microstructure-agnostic: the microstructure only mediates the process-property (PP) connection and is—with some exceptions such as architected materials—seldom used for the optimization itself. While the existence of PSP relationships is the central paradigm of materials science, it would seem that for materials design, one only needs to focus on PP relationships. In this work, we attempt to resolve the issue whether ‘PSP’ is a superior paradigm for materials design in cases where the microstructure itself cannot be (directly) manipulated to optimize materials’ properties. To this end, we formulate a novel microstructure-aware closed-loop multi-fidelity Bayesian optimization framework for materials design and rigorously demonstrate the importance of the microstructure information in the design process. The problem considered here involves finding the right combination of chemistry and processing parameters that maximizes a targeted mechanical property of a model dual-phase steel. Our results clearly show that an explicit incorporation of microstructure knowledge in the materials design framework significantly enhances the materials optimization process. We thus prove, in a computational setting, and for a particular representative problem where microstructure intervenes to influence properties of interest, that ‘PSP’ is superior to ‘PP’ when it comes to materials design.

Global Inverse Design across Multiple Photonic Structure Classes Using Generative Deep Learning (Advanced Optical Materials 20/2021)

In article number 2100548, Christopher Yeung, Aaswath P. Raman, and co-workers propose a global photonics and materials design framework, based on generative adversarial networks, which simultaneously optimizes a photonic system's device class, material properties, and geometric structuring. This framework is demonstrated in the context of metasurface design, where unique combinations of materials and structures are generated that yield significantly more variation in achievable optical responses than conventional deep learning and optimization-based approaches.

Multiscale Analysis of High Damping Composites Using the Finite Cell and the Mortar Method

Metal lattice structures filled with a damping material such as polymer can exhibit high stiffness and good damping properties. Mechanical simulations of parts made from these composites can however require a large modeling and computational effort because relevant features such as complex geometries need to be represented on multiple scales. The finite cell method (FCM) and numerical homogenization are potential remedies for this problem. Moreover, if the microstructures are placed in between the components of assemblies for vibration reduction, a modified mortar technique can further increase the efficiency of the complete simulation process. With this method, it is possible to discretize the components separately and to integrate the viscoelastic behavior of the composite damping layer into their weak coupling. This paper provides a multiscale computational material design framework for such layers, based on FCM and the modified mortar technique. Its efficiency even in the case of complex microstructures is demonstrated in numerical studies. Therein, computational homogenization is first performed on various microstructures before the resulting effective material parameters are used in larger-scale simulation models to investigate their effect and to verify the employed methods.

Effect of viscous shearing stresses on optimal material designs for flow of fluids through porous media

Topology optimization offers optimal material layouts, enabling automation in the design of devices. Given the recent advances in computer technology and additive manufacturing, topology optimization is increasingly being used to design complex porous structures, for example, microfluidic devices. For the flow of fluids in such miniature-sized porous structures, viscous shearing stresses will be significant. However, the Darcy model—the most popular mathematical model describing the flow of a single-phase incompressible fluid in rigid porous media—neglects the internal friction arising from viscous shearing stresses. We, therefore, develop a material design framework under the topology optimization based on the Darcy–Brinkman model—a mathematical model for the flow of fluids through porous media that accounts for internal friction besides the drag considered in the Darcy model. The proposed framework uses the total rate of mechanical dissipation—a physical quantity—for the objective function. To understand the effect of viscous shearing stresses on the design, we compare the optimal material layouts provided by the Darcy and Darcy–Brinkman models under topology optimization. In particular, we show, using analytical solutions corroborated by numerical simulations, that the optimal material layouts are alike for the class of problems exhibiting axisymmetry, for which viscous shearing stresses vanish. These analytical solutions will be valuable to check the veracity of numerical simulators. For those problems with dominant viscous shearing stresses (e.g., flow in a backward-facing step domain), we show, using numerical solutions, the optimal material layouts under the Darcy and Darcy–Brinkman models are very different. Also, the associated solution fields (i.e., velocity and pressure) are qualitatively different under their respective optimal layouts for these two models.

A Bayesian machine learning approach for inverse prediction of high-performance concrete ingredients with targeted performance

High-performance concrete (HPC) plays an important role in improving the sustainability and reliability of buildings and infrastructures. Machine learning predictive models have been used for predicting concrete performance from ingredients, however it remains a challenge to achieve inverse prediction of ingredients from targeted performances. This study proposes an in-house coded informatics-based materials analysis framework to enable computational design of HPC with targeted strength performance. The Gaussian processes (GP) emulator is used to construct the surrogate predictive model based-on 453 experimental measurements. The validity of the GP emulator is assessed using the leave-one-out cross-validation (LOO-CV) and also a separate validation dataset. The variance-based global sensitivity analysis, Sobol indices, is applied to understand the impact of physical ingredients on the HPC performances. The results suggest that the trained GP emulator can provide sufficiently accurate and reliable predictions, as well as reflect the real-world physicochemical nature of HPC materials. The inverse material design is achieved by the Bayesian inference method with a Markov chain Monte Carlo stochastic sampling method, the Metropolis-Hastings (MH) algorithm. Combining with the Bayesian inference method, the proposed design framework can infer a list of potential HPC formulae of a targeted performance, each evaluated by the likelihood of resulting in the targeted strength. The data-driven material analysis and design framework proposed in this study provides a novel approach to achieve performance-based design of HPC, with the potential to maximise resource efficiency and reduce economical cost. The methodology presented in this study can also be extended to be applied to a wide range of construction materials, targeting difference service performances including durability.

The Rigor and Transparency Index Quality Metric for Assessing Biological and Medical Science Methods.

SummaryThe reproducibility crisis is a multifaceted problem involving ingrained practices within the scientific community. Fortunately, some causes are addressed by the author's adherence to rigor and reproducibility criteria, implemented via checklists at various journals. We developed an automated tool (SciScore) that evaluates research articles based on their adherence to key rigor criteria, including NIH criteria and RRIDs, at an unprecedented scale. We show that despite steady improvements, less than half of the scoring criteria, such as blinding or power analysis, are routinely addressed by authors; digging deeper, we examined the influence of specific checklists on average scores. The average score for a journal in a given year was named the Rigor and Transparency Index (RTI), a new journal quality metric. We compared the RTI with the Journal Impact Factor and found there was no correlation. The RTI can potentially serve as a proxy for methodological quality.

Langer\u2013Schwartz\u2013Kampmann\u2013Wagner precipitation simulations: assessment of models and materials design application for Cu precipitation in PH stainless steels

Quantitative modelling of precipitation kinetics can play an important role in a computational material design framework where, for example, optimization of alloying can become more efficient if it is computationally driven. Precipitation hardening (PH) stainless steels is one example where precipitation strengthening is vital to achieve optimum properties. The Langer–Schwartz–Kampmann–Wagner (LSKW) approach for modelling of precipitation has shown good results for different alloy systems, but the specific models and assumptions applied are critical. In the present work, we thus apply two state-of-the-art LSKW tools to evaluate the different treatments of nucleation and growth. The precipitation modelling is assessed with respect to experimental results for Cu precipitation in PH stainless steels. The LSKW modelling is able to predict the precipitation during ageing in good quantitative agreement with experimental results if the nucleation model allows for nucleation of precipitates with a composition far from the equilibrium and if a composition-dependent interfacial energy is considered. The modelling can also accurately predict trends with respect to alloy composition and ageing temperature found in the experimental data. For materials design purposes, it is though proposed that the modelling is calibrated by measurements of precipitate composition and fraction in key experiments prior to application.Graphic abstract

Design of polymeric materials: Experiences and prescriptions

AbstractAs process engineering has matured, research interest has shifted towards polymer product quality. In the past 20 years or so, the shift has progressed even further, as interest in polymer product quality has morphed into polymer product design. Product design is intended to be a targeted pursuit of optimal conditions that will yield polymers with desirable properties for a specific application. This can be achieved by following a systematic design framework that employs sequential, iterative steps informed by prior knowledge and experience. This overview provides some background information regarding the need for design (including some examples from previous experience), especially in terms of structure‐property relationships. When links between kinetics (synthesis conditions), polymer structure, and application properties are well‐understood, it becomes possible to essentially reverse‐engineer the polymeric material; the researcher can start with known application requirements and synthesize polymers with tailor‐made properties using an optimized recipe (according to the polymerization kinetics). A suggested design approach is presented herein, followed by the application of the design approach to two large case studies. The number of applications for polymeric materials is essentially limitless; the current work provides typical examples of a systematic polymeric material design framework (and related case studies).