Strength through defects: A novel Bayesian approach for the optimization of architected materials

Ultrahigh Specific Strength by Bayesian Optimization of Carbon Nanolattices

Nanoarchitected materials are at the frontier of metamaterial design and have set the benchmark for mechanical performance in several contemporary applications. However, traditional nanoarchitected designs with conventional topologies exhibit poor stress distributions and induce premature nodal failure. Here, using multi-objective Bayesian optimization and two-photon polymerization, optimized carbon nanolattices with an exceptional specific strength of 2.03 MPa m3 kg-1 at low densities <215 kg m-3 are created. Generative design optimization provides experimental improvements in strength and Young's modulus by as much as 118% and 68%, respectively, at equivalent densities with entirely different lattice failure responses. Additionally, the reduction of nanolattice strut diameters to 300 nm produces a unique high-strength carbon with a pyrolysis-induced atomic gradient of 94% sp2 aromatic carbon and low oxygen impurities. Using multi-focus multi-photon polymerization, a millimeter-scalable metamaterial consisting of 18.75 million lattice cells with nanometer dimensions is demonstrated. Combining Bayesian optimized designs and nanoarchitected pyrolyzed carbon, the optimal nanostructures exhibit the strength of carbon steel at the density of Styrofoam offering unparalleled capabilities in light-weighting, fuel reduction, and contemporary design applications.

Multiphoton and Harmonic Imaging of Microarchitected Materials

Microadditive manufacturing has revolutionized the production of complex, nano- to microscale components across various fields. This work investigates two-photon (2P) and three-photon (3P) fluorescence imaging, as well as third-harmonic generation (THG) microscopy, to examine periodic microarchitected lattice structures fabricated using multiphoton lithography (MPL). By immersing the structures in refractive index matching fluids, we demonstrate high-fidelity 3D reconstructions of both fluorescent structures using 2P and 3P microscopy as well as low-fluorescence structures using THG microscopy. These results show that multiphoton fluorescence (MPF) imaging offers reduced signal decay with respect to depth compared to single-photon techniques in the examined structures. We further demonstrate the ability to nondestructively identify intentional internal modifications of the structure that are not immediately visible with scanning electron microscope (SEM) images and compression-induced fractures, highlighting the potential of these techniques for quality control and defect detection in microadditively manufactured components.

The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems

Different disciplines are developing various methods for determining and dealing with uncertainties in complex systems. The constrained disorder principle (CDP) accounts for the randomness, variability, and uncertainty that characterize biological systems and are essential for their proper function. Per the CDP, intrinsic unpredictability is mandatory for the dynamicity of biological systems under continuously changing internal and external perturbations. The present paper describes some of the parameters and challenges associated with uncertainty and randomness in biological systems and presents methods for quantifying them. Modeling biological systems necessitates accounting for the randomness, variability, and underlying uncertainty of systems in health and disease. The CDP provides a scheme for dealing with uncertainty in biological systems and sets the basis for using them. This paper presents the CDP-based second-generation artificial intelligence system that incorporates variability to improve the effectiveness of medical interventions. It describes the use of the digital pill that comprises algorithm-based personalized treatment regimens regulated by closed-loop systems based on personalized signatures of variability. The CDP provides a method for using uncertainties in complex systems in an outcome-based manner.

A rapid-convergent particle swarm optimization approach for multiscale design of high-permeance seawater reverse osmosis systems

Directly solving sophisticated partial differential equation constrained optimization problems is not only extremely time-consuming, but also very hard to find unique optimal solutions. Here, we propose stable and efficient surrogate models for seawater reverse osmosis desalination processes that enable thorough quantitative description of hydrodynamics and local transport characteristics in narrow flow channels. Without iteratively solving complex multi-physics simulation problem taking several hours, the proposed multi-scale design optimization framework significantly reduces the problem complexity by computing the surrogate models in seconds. Moreover, a fast-converging active subspace particle swarm optimization framework is proposed to address the optimal design problem. Compared to the standard particle swarm optimization algorithm, the proposed method enhances the average optimum by 14% and the standard deviation of optimum results for multiple runs is reduced by no less than ten times. The optimized desalination system achieves 9% reduction on energy consumption and 30% improvement on water production efficiency.

Nondestructive Imaging of Manufacturing Defects in Microarchitected Materials

Defects in microarchitected materials exhibit a dual nature, capable of both unlocking innovative functionalities and degrading their performance. Specifically, while intentional defects are strategically introduced to customize and enhance mechanical responses, inadvertent defects stemming from manufacturing errors can disrupt the symmetries and intricate interactions within these materials. In this study, we demonstrate a nondestructive optical imaging technique that can precisely locate defects inside microscale metamaterials, as well as provide detailed insights on the specific type of defect.

Three-Dimensional Optical Imaging of Internal Deformations in Polymeric Microscale Mechanical Metamaterials

Recent advances in two-photon polymerization fabrication processes are paving the way to creating macroscopic metamaterials with microscale architectures, which exhibit mechanical properties superior to their bulk material counterparts. These metamaterials typically feature lightweight, complex patterns such as lattice or minimal surface structures. Conventional tools for investigating these microscale structures, such as scanning electron microscopy, cannot easily probe the internal features of these structures, which are critical for a comprehensive assessment of their mechanical behavior. In turn, we demonstrate an optical confocal microscopy-based approach that allows for high-resolution optical imaging of internal deformations and fracture processes in microscale metamaterials under mechanical load. We validate this technique by investigating an exemplary metamaterial lattice structure of 80 × 80 × 80 μm3 in size. This technique can be extended to other metamaterial systems and holds significant promise to enhance our understanding of their real-world performance under loading conditions.

Design Criteria for Architected Materials with Programmable Mechanical Properties Within Theoretical Limit Ranges

Architected materials comprising periodic arrangements of cells have attracted considerable interest in various fields because of their unconventional properties and versatile functionality. Although some better properties may be exhibited when this homogeneous layout is broken, most such studies rely on a fixed material geometry, which limits the design space for material properties. Here, combining heterogeneous and homogeneous assembly of cells to generate tunable geometries, a hierarchically architected material (HAM) capable of significantly enhancing mechanical properties is proposed. Guided by the theoretical model and 745 752 simulation cases, generic design criteria are introduced, including dual screening for unique mechanical properties and careful assembly of specific spatial layouts, to identify the geometry of materials with extreme properties. Such criteria facilitate the potential for unprecedented properties such as Young's modulus at the theoretical limit and tunable positive and negative Poisson's ratios in an ultra-large range. Therefore, this study opens a new paradigm for materials with extreme mechanical properties.

Auxeticity as a Mechanobiological Tool to Create Meta-Biomaterials

Mechanical and morphological design parameters, such as stiffness or porosity, play important roles in creating orthopedic implants and bone substitutes. However, we have only a limited understanding of how the microarchitecture of porous scaffolds contributes to bone regeneration. Meta-biomaterials are increasingly used to precisely engineer the internal geometry of porous scaffolds and independently tailor their mechanical properties (e.g., stiffness and Poisson's ratio). This is motivated by the rare or unprecedented properties of meta-biomaterials, such as negative Poisson's ratios (i.e., auxeticity). It is, however, not clear how these unusual properties can modulate the interactions of meta-biomaterials with living cells and whether they can facilitate bone tissue engineering under static and dynamic cell culture and mechanical loading conditions. Here, we review the recent studies investigating the effects of the Poisson's ratio on the performance of meta-biomaterials with an emphasis on the relevant mechanobiological aspects. We also highlight the state-of-the-art additive manufacturing techniques employed to create meta-biomaterials, particularly at the micrometer scale. Finally, we provide future perspectives, particularly for the design of the next generation of meta-biomaterials featuring dynamic properties (e.g., those made through 4D printing).

Towards understanding the mechanism of 3D printing using protein: femtosecond laser direct writing of microstructures made from homopeptides

Femtosecond laser direct write (fs-LDW) is a promising technology for three-dimensional (3D) printing due to its high resolution, flexibility, and versatility. A protein solution can be used as a precursor to fabricate 3D proteinaceous microstructures that retain the protein's native function. The large diversity of protein molecules with different native functions allows diverse applications of this technology. However, our limited understanding of the mechanism of the printing process restricts the design and generation of 3D microstructures for biomedical applications. Therefore, we used eight commercially available homopeptides as precursors for fs-LDW of 3D structures. Our experimental results show that tyrosine, histidine, glutamic acid, and lysine contribute more to the fabrication process than do proline, threonine, phenylalanine, and alanine. In particular, we show that tyrosine is highly beneficial in the fabrication process. The beneficial effect of the charged amino acids glutamic acid and lysine suggests that the printing mechanism involves ions in addition to the previously proposed radical mechanism. Our results further suggest that the uneven electron density over larger amino acid molecules is key in aiding fs-LDW. The findings presented here will help generate more desired 3D proteinaceous microstructures by modifying protein precursors with beneficial amino acids. STATEMENT OF SIGNIFICANCE: Femtosecond laser direct write (fs-LDW) offers a three-dimensional (3D) printing capability for creating well-defined micro-and nanostructures. Applying this technology to proteins enables the manufacture of complex biomimetic 3D micro-and nanoarchitectures with retention of their original protein functions. To our knowledge, amino acid homo-polymers themselves have never been used as precursor for fs-LDW so far. Our studygainsseveral new insights into the 3D printing mechanism of pure protein for the first time. We believe that the experimental evidence presented greatly benefits the community of 3D printing of proteinin particular and the biomaterial science community in general. With the gained insight, we aspire toexpand the possibilitiesof biomaterial and biomedical applications of this technique.

Mechanical Properties of Internally Hierarchical Multiphase Lattices Inspired by Precipitation Strengthening Mechanisms

In metal metallurgy, precipitation strengthening is widely used to increase material strength by utilizing the impediment effect of the second-phase particles on dislocation movements. Inspired by this mechanism, in this paper, novel multiphase heterogeneous lattice materials are developed with enhanced mechanical properties utilizing a similar hindering effect of second-phase lattice cells on the shear band propagation. For this purpose, biphase and triphase lattice samples are fabricated using high-speed multi jet fusion (MJF) and digital light processing (DLP) additive manufacturing techniques, and a parametric study is carried out to investigate their mechanical properties. Different from the conventional random distribution, the second-phase and third-phase cells in this work are continuously distributed along the regular pattern of a larger-scale lattice to form internal hierarchical lattice structures. The results show that the triphase lattices possess balanced mechanical properties. Interestingly, this indicates that introducing a relatively weak phase also has the potential to improve the stiffness and plateau stress, which is distinct from the common mixed rule. This work is aimed at providing new references for the heterogeneous lattice design with outstanding mechanical properties through material microstructure inspiration.

Computational Design and Manufacturing of Sustainable Materials through First-Principles and Materiomics

Engineered materials are ubiquitous throughout society and are critical to the development of modern technology, yet many current material systems are inexorably tied to widespread deterioration of ecological processes. Next-generation material systems can address goals of environmental sustainability by providing alternatives to fossil fuel-based materials and by reducing destructive extraction processes, energy costs, and accumulation of solid waste. However, development of sustainable materials faces several key challenges including investigation, processing, and architecting of new feedstocks that are often relatively mechanically weak, complex, and difficult to characterize or standardize. In this review paper, we outline a framework for examining sustainability in material systems and discuss how recent developments in modeling, machine learning, and other computational tools can aid the discovery of novel sustainable materials. We consider these through the lens of materiomics, an approach that considers material systems holistically by incorporating perspectives of all relevant scales, beginning with first-principles approaches and extending through the macroscale to consider sustainable material design from the bottom-up. We follow with an examination of how computational methods are currently applied to select examples of sustainable material development, with particular emphasis on bioinspired and biobased materials, and conclude with perspectives on opportunities and open challenges.

Mechanical metamaterials made of freestanding quasi-BCC nanolattices of gold and copper with ultra-high energy absorption capacity

Nanolattices exhibit attractive mechanical properties such as high strength, high specific strength, and high energy absorption. However, at present, such materials cannot achieve effective fusion of the above properties and scalable production, which hinders their applications in energy conversion and other fields. Herein, we report gold and copper quasi-body centered cubic (quasi-BCC) nanolattices with the diameter of the nanobeams as small as 34 nm. We show that the compressive yield strengths of quasi-BCC nanolattices even exceed those of their bulk counterparts, despite their relative densities below 0.5. Simultaneously, these quasi-BCC nanolattices exhibit ultrahigh energy absorption capacities, i.e., 100 ± 6 MJ m^-3 for gold quasi-BCC nanolattice and 110 ± 10 MJ m^-3 for copper quasi-BCC nanolattice. Finite element simulations and theoretical calculations reveal that the deformation of quasi-BCC nanolattice is dominated by nanobeam bending. And the anomalous energy absorption capacities substantially stem from the synergy of the naturally high mechanical strength and plasticity of metals, the size reduction-induced mechanical enhancement, and the quasi-BCC nanolattice architecture. Since the sample size can be scaled up to macroscale at high efficiency and affordable cost, the quasi-BCC nanolattices with ultrahigh energy absorption capacity reported in this work may find great potentials in heat transfer, electric conduction, catalysis applications.

Predicting stress, strain and deformation fields in materials and structures with graph neural networks

Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm. Although impressive achievements have been reached, efficiently predicting complex physical phenomena in materials and structures remains a challenge. Here, we present an AI-based general framework, implemented through graph neural networks, able to learn complex mechanical behavior of materials from a few hundreds data. Harnessing the natural mesh-to-graph mapping, our deep learning model predicts deformation, stress, and strain fields in various material systems, like fiber and stratified composites, and lattice metamaterials. The model can capture complex nonlinear phenomena, from plasticity to buckling instability, seemingly learning physical relationships between the predicted physical fields. Owing to its flexibility, this graph-based framework aims at connecting materials' microstructure, base materials' properties, and boundary conditions to a physical response, opening new avenues towards graph-AI-based surrogate modeling.