Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing.

Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here, we provide a detailed analysis of the heterogeneous graph structures of spider webs and use deep learning as a way to model and then synthesize artificial, bioinspired 3D web structures. The generative models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) an analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation; 2) a discrete diffusion model with full neighbor representation; and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bioinspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose an algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles toward integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties.

Molecular understanding of Ni2+-nitrogen family metal-coordinated hydrogel relaxation times using free energy landscapes.

Incorporating dynamic metal-coordination bonds as cross-links into synthetic materials has become attractive not only to improve self-healing and toughness, but also due to the tunability of metal-coordination bonds. However, a priori determination of bond lifetime of metal-coordination complexes, especially important in the rational design of metal-coordinated materials with prescribed properties, is missing. We report an empirical relationship between the energy landscape of metal-coordination bonds, simulated via metadynamics, and the resulting macroscopic relaxation time in ideal metal-coordinated hydrogels. Importantly, we expand the Arrhenius relationship between the macroscopic hydrogel relaxation time and metal-coordinate bond activation energy to include width and landscape ruggedness identified in the simulated energy landscapes. Using biologically relevant Ni2+-nitrogen coordination complexes as a model case, we demonstrate that the quantitative relationship developed from histidine-Ni2+ and imidazole-Ni2+ complexes can predict the average relaxation times of other Ni2+-nitrogen coordinated networks. We anticipate the quantitative relationship presented here to be a starting point for the development of more sophisticated models that can predict relaxation timescales of materials with programmable viscoelastic properties.

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.

Discovering design principles of collagen molecular stability using a genetic algorithm, deep learning, and experimental validation.

Collagen is the most abundant structural protein in humans, providing crucial mechanical properties, including high strength and toughness, in tissues. Collagen-based biomaterials are, therefore, used for tissue repair and regeneration. Utilizing collagen effectively during materials processing ex vivo and subsequent function in vivo requires stability over wide temperature ranges to avoid denaturation and loss of structure, measured as melting temperature (Tm). Although significant research has been conducted on understanding how collagen primary amino acid sequences correspond to Tm values, a robust framework to facilitate the design of collagen sequences with specific Tm remains a challenge. Here, we develop a general model using a genetic algorithm within a deep learning framework to design collagen sequences with specific Tm values. We report 1,000 de novo collagen sequences, and we show that we can efficiently use this model to generate collagen sequences and verify their Tm values using both experimental and computational methods. We find that the model accurately predicts Tm values within a few degrees centigrade. Further, using this model, we conduct a high-throughput study to identify the most frequently occurring collagen triplets that can be directly incorporated into collagen. We further discovered that the number of hydrogen bonds within collagen calculated with molecular dynamics (MD) is directly correlated to the experimental measurement of triple-helical quality. Ultimately, we see this work as a critical step to helping researchers develop collagen sequences with specific Tm values for intended materials manufacturing methods and biomedical applications, realizing a mechanistic materials by design paradigm.

Hierarchically structured bioinspired nanocomposites.

Next-generation structural materials are expected to be lightweight, high-strength and tough composites with embedded functionalities to sense, adapt, self-repair, morph and restore. This Review highlights recent developments and concepts in bioinspired nanocomposites, emphasizing tailoring of the architecture, interphases and confinement to achieve dynamic and synergetic responses. We highlight cornerstone examples from natural materials with unique mechanical property combinations based on relatively simple building blocks produced in aqueous environments under ambient conditions. A particular focus is on structural hierarchies across multiple length scales to achieve multifunctionality and robustness. We further discuss recent advances, trends and emerging opportunities for combining biological and synthetic components, state-of-the-art characterization and modelling approaches to assess the physical principles underlying nature-inspired design and mechanical responses at multiple length scales. These multidisciplinary approaches promote the synergetic enhancement of individual materials properties and an improved predictive and prescriptive design of the next era of structural materials at multilength scales for a wide range of applications.

In situ three-dimensional spider web construction and mechanics.

Spiders are nature's engineers that build lightweight and high-performance web architectures often several times their size and with very few supports; however, little is known about web mechanics and geometries throughout construction, especially for three-dimensional (3D) spider webs. In this work, we investigate the structure and mechanics for a Tidarren sisyphoides spider web at varying stages of construction. This is accomplished by imaging, modeling, and simulations throughout the web-building process to capture changes in the natural web geometry and the mechanical properties. We show that the foundation of the web geometry, strength, and functionality is created during the first 2 d of construction, after which the spider reinforces the existing network with limited expansion of the structure within the frame. A better understanding of the biological and mechanical performance of the 3D spider web under construction could inspire sustainable robust and resilient fiber networks, complex materials, structures, scaffolding, and self-assembly strategies for hierarchical structures and inspire additive manufacturing methods such as 3D printing as well as inspire artistic and architectural and engineering applications.

Multiscale Modeling at the Interface of Molecular Mechanics and Natural Language through Attention Neural Networks.

Humans are continually bombarded with massive amounts of data. To deal with this influx of information, we use the concept of attention in order to perceive the most relevant input from vision, hearing, touch, and others. Thereby, the complex ensemble of signals is used to generate output by querying the processed data in appropriate ways. Attention is also the hallmark of the development of scientific theories, where we elucidate which parts of a problem are critical, often expressed through differential equations. In this Account we review the emergence of attention-based neural networks as a class of approaches that offer many opportunities to describe materials across scales and modalities, including how universal building blocks interact to yield a set of material properties. In fact, the self-assembly of hierarchical, structurally complex, and multifunctional biomaterials remains a grand challenge in modeling, theory, and experiment. Expanding from the process by which material building blocks physically interact to form a type of material, in this Account we view self-assembly as both the functional emergence of properties from interacting building blocks as well as the physical process by which elementary building blocks interact and yield structure and, thereby, functions. This perspective, integrated through the theory of materiomics, allows us to solve multiscale problems with a first-principles-based computational approach based on attention-based neural networks that transform information to feature to property while providing a flexible modeling approach that can integrate theory, simulation, and experiment. Since these models are based on a natural language framework, they offer various benefits including incorporation of general domain knowledge via general-purpose pretraining, which can be accomplished without labeled data or large amounts of lower-quality data. Pretrained models then offer a general-purpose platform that can be fine-tuned to adapt these models to make specific predictions, often with relatively little labeled data. The transferrable power of the language-based modeling approach realizes a neural olog description, where mathematical categorization is learned by multiheaded attention, without domain knowledge in its formulation. It can hence be applied to a range of complex modeling tasks─such as physical field predictions, molecular properties, or structure predictions, all using an identical formulation. This offers a complementary modeling approach that is already finding numerous applications, with great potential to solve complex assembly problems, enabling us to learn, build, and utilize functional categorization of how building blocks yield a range of material functions. In this Account, we demonstrate the approach in various application areas, including protein secondary structure prediction and prediction of normal-mode frequencies as well as predicting mechanical fields near cracks. Unifying these diverse problem areas is the building block approach, where the models are based on a universally applicable platform that offers benefits ranging from transferability, interpretability, and cross-domain pollination of knowledge as exemplified through a transformer model applied to predict how musical compositions infer de novo protein structures. We discuss future potentialities of this approach for a variety of material phenomena across scales, including the use in multiparadigm modeling schemes.

Localization of Zn2+ ions affects the structural folding and mechanics of Nereis virens Nvjp-1.

Several biological organisms utilize metal-coordination bonds to produce remarkable materials, such as the jaw of the marine worm Nereis virens, where metal-coordination bonds yield remarkable hardness without mineralization. Though the structure of a major component of the jaw, the Nvjp-1 protein, has recently been resolved, a detailed nanostructural understanding of the role of metal ions on the structural and mechanical properties of the protein is missing, especially with respect to the localization of metal ions. In this work, atomistic replica exchange molecular dynamics with explicit water and Zn2+ ions and steered molecular dynamics simulations were used to explore how the initial localization of the Zn2+ ions impacts the structural folding and mechanical properties of Nvjp-1. We found that the initial distribution of metal ions for Nvjp-1, and likely for other proteins with high amounts of metal-coordination, has important effects on the resulting structure, with larger metal ion quantity resulting in a more compact structure. These structural compactness trends, however, are independent from the mechanical tensile strength of the protein, which increases with greater hydrogen bond content and uniform distribution of metal ions. Our results indicate that different physical principles underlie the structure or mechanics of Nvjp-1, with broader implications in the development optimized hardened bioinspired materials and the modeling of proteins with significant metal ion content.

Coordination Stoichiometry Effects on the Binding Hierarchy of Histamine and Imidazole-M2+ Complexes.

Histidine-M2+ coordination bonds are a recognized bond motif in biogenic materials with high hardness and extensibility, which has led to growing interest in their use in soft materials for mechanical function. However, the effect of different metal ions on the stability of the coordination complex remains poorly understood, complicating their implementation in metal-coordinated polymer materials. Herein, rheology experiments and density functional theory calculations are used to characterize the stability of coordination complexes and establish the binding hierarchy of histamine and imidazole with Ni2+ , Cu2+ , and Zn2+ . It is found that the binding hierarchy is driven by the specific affinity of the metal ions to different coordination states, which can be macroscopically tuned by changing the metal-to-ligand stoichiometry of the metal-coordinated network. These findings facilitate the rational selection of metal ions for optimizing the mechanical properties of metal-coordinated materials.

Nanomechanical analysis of SARS-CoV-2 variants and predictions of infectiousness and lethality.

As variants of the pathogen that causes COVID-19 spread around the world, estimates of infectiousness and lethality of newly emerging strains are important. Here we report a predictive model that associates molecular motions and vibrational patterns of the virus spike protein with infectiousness and lethality. The key finding is that most SARS-CoV-2 variants are predicted to be more infectious and less lethal compared to the original spike protein. However, lineage B.1.351 (Beta variant) is predicted to be less infectious and more lethal, and lineage B.1.1.7 (Alpha variant) is predicted to have both higher infectivity and lethality, showing the potential of the virus to mutate towards different performance regimes. The relatively more recent lineage B.1.617.2 (Delta variant), although contains a few key spike mutations other than D614G, behaves quite similar to the single D614G mutation in both vibrational and predicted epidemiological aspects, which might explain its rapid circulation given the prevalence of D614G. This work may provide a tool to estimate the epidemiological effects of new variants, and offer a pathway to screen mutations against high threat levels. Moreover, the nanomechanical approach, as a novel tool to predict virus-cell interactions, may further open up the door towards better understanding other viruses.

Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model.

Dynamic fracture is an important area of materials analysis, assessing the atomic-level mechanisms by which materials fail over time. Here, we focus on brittle materials failure and show that an atomistically derived progressive transformer diffusion machine learning model can effectively describe the dynamics of fracture, capturing important aspects such as crack dynamics, instabilities, and initiation mechanisms. Trained on a small dataset of atomistic simulations, the model generalizes well and offers a rapid assessment of dynamic fracture mechanisms for complex geometries, expanding well beyond the original set of atomistic simulation results. Various validation cases, progressively more distinct from the data used for training, are presented and analyzed. The validation cases feature distinct geometric details, including microstructures generated by a generative neural network used here to identify novel bio-inspired material designs for mechanical performance. For all cases, the model performs well and captures key aspects of material failure.

Fuzzy binding model of molecular interactions between tropoelastin and integrin alphaVbeta3.

Tropoelastin is the highly flexible monomer subunit of elastin, required for the resilience of the extracellular matrix in elastic tissues. To elicit biological signaling, multiple sites on tropoelastin bind to cell surface integrins in a poorly understood multifactorial process. We constructed a full atomistic molecular model of the interactions between tropoelastin and integrin αvß3 using ensemble-based computational methodologies. Conformational changes of integrin αvß3 associated with outside-in signaling were more frequently facilitated in an ensemble in which tropoelastin bound the integrin's α1 helix rather than the upstream canonical binding site. Our findings support a model of fuzzy binding, whereby many tropoelastin conformations and defined sites cooperatively interact with multiple αvß3 regions. This model explains prior experimental binding to distinct tropoelastin regions, domains 17 and 36, and points to the cooperative participation of domain 20. Our study highlights the utility of ensemble-based approaches in helping to understand the interactive mechanisms of functionally significant flexible proteins.

Biological Material Interfaces as Inspiration for Mechanical and Optical Material Designs.

The extraordinary properties of biological materials often result from their sophisticated hierarchical structures. Through multilevel and cross-scale structural designs, biological materials offset the weakness of their individual building blocks and enhance performance at multiple length scales to match the multifunctional needs of organisms. One essential merit of hierarchical structure is that it can optimize the interfacial features of the "building blocks" at different length scales, from the molecular level to the macroscale. Understanding the roles of biological material interfaces (BMIs) on the determination of properties and functions of biological materials has become a growing interdisciplinary research area in recent years. A pivotal aim of these studies is to use BMIs as inspiration for developing bioinspired and biomimetic materials and devices with advanced structures and functions. Given these considerations, this review aims to comprehensively discuss the structure-property-function relationships of BMIs in nature. We particularly focus on the discussion of BMIs and their inspired materials from mechanical and optical perspectives because these two directions are the most well-investigated and closely related. The challenges and directions of design and fabrication of BMI-inspired mechanical and optical materials are also discussed. This review is expected to garner interest from advanced material communities as well as environmental, nanotechnology, food processing, and engineering fields.

Molecular model of human tropoelastin and implications of associated mutations.

Protein folding poses unique challenges for large, disordered proteins due to the low resolution of structural data accessible in experiment and on the basis of short time scales and limited sampling attainable in computation. Such molecules are uniquely suited to accelerated-sampling molecular dynamics algorithms due to a flat-energy landscape. We apply these methods to report here the folded structure in water from a fully extended chain of tropoelastin, a 698-amino acid molecular precursor to elastic fibers that confer elasticity and recoil to tissues, finding good agreement with experimental data. We then study a series of artificial and disease-related mutations, yielding molecular mechanisms to explain structural differences and variation in hierarchical assembly observed in experiment. The present model builds a framework for studying assembly and disease and yields critical insight into molecular mechanisms behind these processes. These results suggest that proteins with disordered regions are suitable candidates for characterization by this approach.

Reversible MoS2 Origami with Spatially Resolved and Reconfigurable Photosensitivity.

Two-dimensional layered materials (2DLMs) have been extensively studied in a variety of planar optoelectronic devices. Three-dimensional (3D) optoelectronic structures offer unique advantages including omnidirectional responses, multipolar detection, and enhanced light-matter interactions. However, there has been limited success in transforming monolayer 2DLMs into reconfigurable 3D optoelectronic devices due to challenges in microfabrication and integration of these materials in truly 3D geometries. Here, we report an origami-inspired self-folding approach to reversibly transform monolayer molybdenum disulfide (MoS2) into functional 3D optoelectronic devices. We pattern and integrate monolayer MoS2 and gold (Au) onto differentially photo-cross-linked thin polymer (SU8) films. The devices reversibly self-fold due to swelling gradients in the SU8 films upon solvent exchange. We fabricate a wide variety of optically active 3D MoS2 microstructures including pyramids, cubes, flowers, dodecahedra, and Miura-oris, and we simulate the self-folding mechanism using a coarse-grained mechanics model. Using finite-difference time-domain (FDTD) simulation and optoelectronic characterization, we demonstrate that the 3D self-folded MoS2 structures show enhanced light interaction and are capable of angle-resolved photodetection. Importantly, the structures are also reversibly reconfigurable upon solvent exchange with high tunability in the optical detection area. Our approach provides a versatile strategy to reversibly configure 2D materials in 3D optoelectronic devices of broad relevance to flexible and wearable electronics, biosensing, and robotics.

Self-Folding Hybrid Graphene Skin for 3D Biosensing.

Biological samples such as cells have complex three-dimensional (3D) spatio-molecular profiles and often feature soft and irregular surfaces. Conventional biosensors are based largely on 2D and rigid substrates, which have limited contact area with the entirety of the surface of biological samples making it challenging to obtain 3D spatially resolved spectroscopic information, especially in a label-free manner. Here, we report an ultrathin, flexible skinlike biosensing platform that is capable of conformally wrapping a soft or irregularly shaped 3D biological sample such as a cancer cell or a pollen grain, and therefore enables 3D label-free spatially resolved molecular spectroscopy via surface-enhanced Raman spectroscopy (SERS). Our platform features an ultrathin thermally responsive poly( N-isopropylacrylamide)-graphene-nanoparticle hybrid skin that can be triggered to self-fold and wrap around 3D micro-objects in a conformal manner due to its superior flexibility. We highlight the utility of this 3D biosensing platform by spatially mapping the 3D molecular signatures of a variety of microparticles including silica microspheres, spiky pollen grains, and human breast cancer cells.

Atomically Sharp Dual Grain Boundaries in 2D WS2 Bilayers.

It is shown that tilt grain boundaries (GBs) in bilayer 2D crystals of the transition metal dichalcogenide WS2 can be atomically sharp, where top and bottom layer GBs are located within sub-nanometer distances of each other. This expands the current knowledge of GBs in 2D bilayer crystals, beyond the established large overlapping GB types typically formed in chemical vapor deposition growth, to now include atomically sharp dual bilayer GBs. By using atomic-resolution annular dark-field scanning transmission electron microscopy (ADF-STEM) imaging, different atomic structures in the dual GBs are distinguished considering bilayers with a 3R (AB stacking)/2H (AA' stacking) interface as well as bilayers with 2H/2H boundaries. An in situ heating holder is used in ADF-STEM and the GBs are stable to at least 800 °C, with negligible thermally induced reconstructions observed. Normal dislocation cores are seen in one WS2 layer, but the second WS2 layer has different dislocation structures not seen in freestanding monolayers, which have metal-rich clusters to accommodate the stacking mismatch of the 2H:3R interface. These results reveal the competition between maintaining van der Waals bilayer stacking uniformity and dislocation cores required to stitch tilted bilayer GBs together.

Sub-nanometre channels embedded in two-dimensional materials.

Two-dimensional (2D) materials are among the most promising candidates for next-generation electronics due to their atomic thinness, allowing for flexible transparent electronics and ultimate length scaling. Thus far, atomically thin p-n junctions, metal-semiconductor contacts, and metal-insulator barriers have been demonstrated. Although 2D materials achieve the thinnest possible devices, precise nanoscale control over the lateral dimensions is also necessary. Here, we report the direct synthesis of sub-nanometre-wide one-dimensional (1D) MoS2 channels embedded within WSe2 monolayers, using a dislocation-catalysed approach. The 1D channels have edges free of misfit dislocations and dangling bonds, forming a coherent interface with the embedding 2D matrix. Periodic dislocation arrays produce 2D superlattices of coherent MoS2 1D channels in WSe2. Using molecular dynamics simulations, we have identified other combinations of 2D materials where 1D channels can also be formed. The electronic band structure of these 1D channels offers the promise of carrier confinement in a direct-gap material and the charge separation needed to access the ultimate length scales necessary for future electronic applications.

Sounds interesting: can sonification help us design new proteins?

Introduction: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music have been made. Previous works have either worked at a low level, mapping amino acid to notes, or at a higher level, using the overall structure as a basis for composition.Areas covered: We report a comprehensive mapping strategy that encompasses the encoding of the geometry of proteins, in addition to the amino acid sequence and secondary structure information. This leads to a piece of music that is both more complete and closely linked to the original protein. By using this mapping, we can invert the process and map music to proteins, retrieving not only the amino acid sequence but also the secondary structure and folding from musical data.Expert opinion: We can train a machine learning model on 'protein music' to generate new music that can be translated to new proteins. By selecting proper datasets and conditioning parameters on the generative model, we could tune de novo proteins with high level parameters to achieve certain protein design features.