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A key challenge in artificial intelligence (AI) is the creation of systems capable of autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections in vast scientific data. In this work, SciAgents, an approach that leverages three core concepts is presented: (1) large-scale ontological knowledge graphs to organize and interconnect diverse scientific concepts, (2) a suite of large language models (LLMs) and data retrieval tools, and (3) multi-agent systems with in-situ learning capabilities. Applied to biologically inspired materials, SciAgents reveals hidden interdisciplinary relationships that were previously considered unrelated, achieving a scale, precision, and exploratory power that surpasses human research methods. The framework autonomously generates and refines research hypotheses, elucidating underlying mechanisms, design principles, and unexpected material properties. By integrating these capabilities in a modular fashion, the system yields material discoveries, critiques and improves existing hypotheses, retrieves up-to-date data about existing research, and highlights strengths and limitations. This is achieved by harnessing a "swarm of intelligence" similar to biological systems, providing new avenues for discovery. How this model accelerates the development of advanced materials by unlocking Nature's design principles, resulting in a new biocomposite with enhanced mechanical properties and improved sustainability through energy-efficient production is shown., (© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.)

Competing Interests: Competing interests statement:The authors declare no competing interest.

Multicellular self-assembly into functional structures is a dynamic process that is critical in the development and diseases, including embryo development, organ formation, tumor invasion, and others. Being able to infer collective cell migratory dynamics from their static configuration is valuable for both understanding and predicting these complex processes. However, the identification of structural features that can indicate multicellular motion has been difficult, and existing metrics largely rely on physical instincts. Here we show that using a graph neural network (GNN), the motion of multicellular collectives can be inferred from a static snapshot of cell positions, in both experimental and synthetic datasets.

During developmental processes such as embryogenesis, how a group of cells fold into specific structures, is a central question in biology. However, it remains a major challenge to understand and predict the behavior of every cell within the living tissue over time during such intricate processes. Here we present a geometric deep-learning model that can accurately capture the highly convoluted interactions among cells. We demonstrate that multicellular data can be represented with both granular and foam-like physical pictures through a unified graph data structure, considering both cellular interactions and cell junction networks. Using this model, we achieve interpretable 4-D morphological sequence alignment, and predicting cell rearrangements before they occur at single-cell resolution. Furthermore, using neural activation map and ablation studies, we demonstrate cell geometries and cell junction networks together regulate morphogenesis at single-cell precision. This approach offers a pathway toward a unified dynamic atlas for a variety of developmental processes.

Designing de novo proteins beyond those found in nature holds significant promise for advancements in both scientific and engineering applications. Current methodologies for protein design often rely on AI-based models, such as surrogate models that address end-to-end problems by linking protein structure to material properties or vice versa . However, these models frequently focus on specific material objectives or structural properties, limiting their flexibility when incorporating out-of-domain knowledge into the design process or comprehensive data analysis is required. In this study, we introduce ProtAgents, a platform for de novo protein design based on Large Language Models (LLMs), where multiple AI agents with distinct capabilities collaboratively address complex tasks within a dynamic environment. The versatility in agent development allows for expertise in diverse domains, including knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis. The dynamic collaboration between agents, empowered by LLMs, provides a versatile approach to tackling protein design and analysis problems, as demonstrated through diverse examples in this study. The problems of interest encompass designing new proteins, analyzing protein structures and obtaining new first-principles data - natural vibrational frequencies - via physics simulations. The concerted effort of the system allows for powerful automated and synergistic design of de novo proteins with targeted mechanical properties. The flexibility in designing the agents, on one hand, and their capacity in autonomous collaboration through the dynamic LLM-based multi-agent environment on the other hand, unleashes great potentials of LLMs in addressing multi-objective materials problems and opens up new avenues for autonomous materials discovery and design., Competing Interests: The author declares no conflict of interest., (This journal is © The Royal Society of Chemistry.)

Metal-coordination bonds, a highly tunable class of dynamic noncovalent interactions, are pivotal to the function of a variety of protein-based natural materials and have emerged as binding motifs to produce strong, tough, and self-healing bioinspired materials. While natural proteins use clusters of metal-coordination bonds, synthetic materials frequently employ individual bonds, resulting in mechanically weak materials. To overcome this current limitation, we rationally designed a series of elastin-like polypeptide templates with the capability of forming an increasing number of intermolecular histidine-Ni 2+ metal-coordination bonds. Using single-molecule force spectroscopy and steered molecular dynamics simulations, we show that templates with three histidine residues exhibit heterogeneous rupture pathways, including the simultaneous rupture of at least two bonds with more-than-additive rupture forces. The methodology and insights developed improve our understanding of the molecular interactions that stabilize metal-coordinated proteins and provide a general route for the design of new strong, metal-coordinated materials with a broad spectrum of dissipative time scales.

Fungal mycelium, a living network of filamentous threads, thrives on lignocellulosic waste and exhibits rapid growth, hydrophobicity, and intrinsic regeneration, offering a potential means to create next-generation sustainable and functional composites. However, existing hybrid-living mycelium composites (myco-composites) are tremendously constrained by conventional mold-based manufacturing processes, which are only compatible with simple geometries and coarse biomass substrates that enable gas exchange. Here we introduce a class of structural myco-composites manufactured with a novel platform that harnesses high-resolution biocomposite additive manufacturing and robust mycelium colonization with indirect inoculation. We leverage principles of hierarchical composite design and selective nutritional provision to create a robust myco-composite that is scalable, tunable, and compatible with complex geometries. To illustrate the versatility of this platform, we characterize the impact of mycelium colonization on mechanical and surface properties of the composite. We found that our method yields the strongest mycelium composite reported to date with a modulus of 160 MPa and tensile strength of 0.72 MPa, which represents over a 15-fold improvement over typical mycelium composites, and further demonstrate unique applications with fabrication of foldable bio-welded containers and flexible mycelium textiles. This study bridges the gap between biocomposite and hybrid-living materials research, opening the door to advanced structural mycelium applications and demonstrating a novel platform for development of diverse hybrid-living materials.

The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Through evolution, nature has presented a set of remarkable protein materials, including elastins, silks, keratins and collagens with superior mechanical performances that play crucial roles in mechanobiology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. Here, we report a generative model that predicts protein designs to meet complex nonlinear mechanical property-design objectives. Our model leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, as well as the detailed unfolding force-separation curves. Our model offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, using mechanical features as the target to enable the discovery of protein materials with superior mechanical properties.

Transformer neural networks show promising capabilities, in particular for uses in materials analysis, design, and manufacturing, including their capacity to work effectively with human language, symbols, code, and numerical data. Here, we explore the use of large language models (LLMs) as a tool that can support engineering analysis of materials, applied to retrieving key information about subject areas, developing research hypotheses, discovery of mechanistic relationships across disparate areas of knowledge, and writing and executing simulation codes for active knowledge generation based on physical ground truths. Moreover, when used as sets of AI agents with specific features, capabilities, and instructions, LLMs can provide powerful problem-solution strategies for applications in analysis and design problems. Our experiments focus on using a fine-tuned model, MechGPT, developed based on training data in the mechanics of materials domain. We first affirm how fine-tuning endows LLMs with a reasonable understanding of subject area knowledge. However, when queried outside the context of learned matter, LLMs can have difficulty recalling correct information and may hallucinate. We show how this can be addressed using retrieval-augmented Ontological Knowledge Graph strategies. The graph-based strategy helps us not only to discern how the model understands what concepts are important but also how they are related, which significantly improves generative performance and also naturally allows for injection of new and augmented data sources into generative AI algorithms. We find that the additional feature of relatedness provides advantages over regular retrieval augmentation approaches and not only improves LLM performance but also provides mechanistic insights for exploration of a material design process. Illustrated for a use case of relating distinct areas of knowledge, here, music and proteins, such strategies can also provide an interpretable graph structure with rich information at the node, edge, and subgraph level that provides specific insights into mechanisms and relationships. We discuss other approaches to improve generative qualities, including nonlinear sampling strategies and agent-based modeling that offer enhancements over single-shot generations, whereby LLMs are used to both generate content and assess content against an objective target. Examples provided include complex question answering, code generation, and execution in the context of automated force-field development from actively learned density functional theory (DFT) modeling and data analysis., Competing Interests: The author declares no competing financial interest., (© 2024 The Author. Published by American Chemical Society.)

Reversible crosslinkers can enable several desirable mechanical properties, such as improved toughness and self-healing, when incorporated in polymer networks for bioengineering and structural applications. In this work, we performed coarse-grained molecular dynamics to investigate the effect of the energy landscape of reversible crosslinkers on the dynamic mechanical properties of crosslinked polymer network hydrogels. We report that, for an ideal network, the energy potential of the crosslinker interaction drives the viscosity of the network, where a stronger potential results in a higher viscosity. Additional topographical analyses reveal a mechanistic understanding of the structural rearrangement of the network as it deforms and indicate that as the number of defects increases in the network, the viscosity of the network increases. As an important validation for the relationship between the energy landscape of a crosslinker chemistry and the resulting dynamic mechanical properties of a crosslinked ideal network hydrogel, this work enhances our understanding of deformation mechanisms in polymer networks that cannot easily be revealed by experiment and reveals design ideas that can lead to better performance of the polymer network at the macroscale., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Histidine-M 2+ 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 Ni 2+ , Cu 2+ , and Zn 2+ . 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., (© 2023 The Authors. Macromolecular Rapid Communications published by Wiley-VCH GmbH.)

Since the discovery of deep eutectic solvents (DESs) in 2003, significant progress has been made in the field, specifically advancing aspects of their preparation and physicochemical characterization. Their low-cost and unique tailored properties are reasons for their growing importance as a sustainable medium for the resource-efficient processing and synthesis of advanced materials. In this paper, the significance of these designer solvents and their beneficial features, in particular with respect to biomimetic materials chemistry, is discussed. Finally, this article explores the unrealized potential and advantageous aspects of DESs, focusing on the development of biomineralization-inspired hybrid materials. It is anticipated that this article can stimulate new concepts and advances providing a reference for breaking down the multidisciplinary borders in the field of bioinspired materials chemistry, especially at the nexus of computation and experiment, and to develop a rigorous materials-by-design paradigm., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

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.

We report two generative deep learning models that predict amino acid sequences and 3D protein structures based on secondary structure design objectives via either overall content or per-residue structure. Both models are robust regarding imperfect inputs and offer de novo design capacity as they can discover new protein sequences not yet discovered from natural mechanisms or systems. The residue-level secondary structure design model generally yields higher accuracy and more diverse sequences. These findings suggest unexplored opportunities for protein designs and functional outcomes within the vast amino acid sequences beyond known proteins. Our models, based on an attention-based diffusion model and trained on a dataset extracted from experimentally known 3D protein structures, offer numerous downstream applications in conditional generative design of various biological or engineering systems. Future work may include additional conditioning, and an exploration of other functional properties of the generated proteins for various properties beyond structural objectives., Competing Interests: Declaration of interests The author declares no competing interests.

Model verification is a critical aspect of scientific accountability, transparency, and learning. Here, we demonstrate an application of a model verification approach for a molecular dynamics (MD) simulation, where the interactions between silica and silk protein were studied experimentally toward understanding biomineralization. Following the ten rules for credible modeling and simulation of biosciences as developed in Erdemir et al., the authors of the original paper collaborated with an external modeling group to verify the key findings of their original simulation model and to document this verification approach. The process resulted in successful replication of the key findings of the original model. Beyond verification, study of the model from a new perspective generated new insight into the basic assumptions. We discuss key learnings for how model validation processes can be improved more generally, specifically through improved documentation methods. We anticipate that this application of our protocol for model verification can be further replicated and improved to verify and validate other simulations.

Biominerals are organic-mineral composites formed by living organisms. They are the hardest and toughest tissues in those organisms, are often polycrystalline, and their mesostructure (which includes nano- and microscale crystallite size, shape, arrangement, and orientation) can vary dramatically. Marine biominerals may be aragonite, vaterite, or calcite, all calcium carbonate (CaCO 3 ) polymorphs, differing in crystal structure. Unexpectedly, diverse CaCO 3 biominerals such as coral skeletons and nacre share a similar characteristic: Adjacent crystals are slightly misoriented. This observation is documented quantitatively at the micro- and nanoscales, using polarization-dependent imaging contrast mapping (PIC mapping), and the slight misorientations are consistently between 1° and 40°. Nanoindentation shows that both polycrystalline biominerals and abiotic synthetic spherulites are tougher than single-crystalline geologic aragonite. Molecular dynamics (MD) simulations of bicrystals at the molecular scale reveal that aragonite, vaterite, and calcite exhibit toughness maxima when the bicrystals are misoriented by 10°, 20°, and 30°, respectively, demonstrating that slight misorientation alone can increase fracture toughness. Slight-misorientation-toughening can be harnessed for synthesis of bioinspired materials that only require one material, are not limited to specific top-down architecture, and are easily achieved by self-assembly of organic molecules (e.g., aspirin, chocolate), polymers, metals, and ceramics well beyond biominerals., (© 2023 University of Wisconsin-Madison. Advanced Materials published by Wiley-VCH GmbH.)

Solving materials engineering tasks is often hindered by limited information, such as in inverse problems with only boundary data information or design tasks with a simple objective but a vast search space. To address these challenges, multiple deep learning (DL) architectures are leveraged to predict missing mechanical information given limited known data in part of the domain, and further characterize the composite geometries from the recovered mechanical fields for 2D and 3D complex microstructures. In 2D, a conditional generative adversarial network (GAN) is utilized to complete partially masked field maps and predict the composite geometry with convolutional models with great accuracy and generality by making precise predictions on field data with mixed stress/strain components, hierarchical geometries, distinct materials properties and various types of microstructures including ill-posed inverse problems. In 3D, a Transformer-based architecture is implemented to predict complete 3D mechanical fields from input field snapshots. The model manifests excellent performance regardless of microstructural complexity and recovers the entire bulk field even from a single surface field image, allowing internal structural characterization from only boundary measurements. The whole frameworks provide efficient ways for analysis and design with incomplete information and allow the direct inverse translation from properties back to materials structures., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

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 Zn 2+ ions and steered molecular dynamics simulations were used to explore how the initial localization of the Zn 2+ 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.

Dynamic noncovalent interactions are pivotal to the structure and function of biological proteins and have been used in bioinspired materials for similar roles. Metal-coordination bonds, in particular, are especially tunable and enable control over static and dynamic properties when incorporated into synthetic materials. Despite growing efforts to engineer metal-coordination bonds to produce strong, tough, and self-healing materials, the systematic characterization of the exact contribution of these bonds towards mechanical strength and the effect of geometric arrangements is missing, limiting the full design potential of these bonds. In this work, we engineer the cooperative rupture of metal-coordination bonds to increase the rupture strength of metal-coordinated peptide dimers. Utilizing all-atom steered molecular dynamics simulations on idealized bidentate histidine-Ni 2+ coordinated peptides, we show that histidine-Ni 2+ bonds can rupture cooperatively in groups of two to three bonds. We find that there is a strength limit, where adding additional coordination bonds does not contribute to the additional increase in the protein rupture strength, likely due to the highly heterogeneous rupture behavior exhibited by the coordination bonds. Further, we show that this coordination bond limit is also found natural metal-coordinated biological proteins. Using these insights, we quantitatively suggest how other proteins can be rationally designed with dynamic noncovalent interactions to exhibit cooperative bond breaking behavior. Altogether, this work provides a quantitative analysis of the cooperativity and intrinsic strength limit for metal-coordination bonds with the aim of advancing clear guiding molecular principles for the mechanical design of metal-coordinated materials.

Micro-prosthetics requires the fabrication of mechanically robust and personalized components with sub-millimetric feature accuracy. Three-dimensional (3D) printing technologies have had a major impact on manufacturing such miniaturized devices for biomedical applications; however, biocompatibility requirements greatly constrain the choice of usable materials. Hydroxyapatite (HA) and its composites have been widely employed to fabricate bone-like structures, especially at the macroscale. In this work, we investigate the rheology, printability, and prosthetic mechanical properties of HA and HA-silk protein composites, focusing on the roles of composition and water content. We correlate key linear and nonlinear shear rheological parameters to geometric outcomes of printing and explain how silk compensates for the inherent brittleness of printed HA components. By increasing ink ductility, the inclusion of silk improves the quality of printed items through two mechanisms: (1) reducing underextrusion by lowering the required elastic modulus and, (2) reducing slumping by increasing the ink yield stress proportional to the modulus. We demonstrate that the elastic modulus and compressive strength of parts fabricated from silk-HA inks are higher than those for rheologically comparable pure-HA inks. We construct a printing map to guide the manufacturing of HA-based inks with excellent final properties, especially for use in biomedical applications for which sub-millimetric features are required.

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.

Taking inspiration from nature about how to design materials has been a fruitful approach, used by humans for millennia. In this paper we report a method that allows us to discover how patterns in disparate domains can be reversibly related using a computationally rigorous approach, the AttentionCrossTranslation model. The algorithm discovers cycle- and self-consistent relationships and offers a bidirectional translation of information across disparate knowledge domains. The approach is validated with a set of known translation problems, and then used to discover a mapping between musical data-based on the corpus of note sequences in J.S. Bach's Goldberg Variations created in 1741-and protein sequence data-information sampled more recently. Using protein folding algorithms, 3D structures of the predicted protein sequences are generated, and their stability is validated using explicit solvent molecular dynamics. Musical scores generated from protein sequences are sonified and rendered into audible sound., Competing Interests: The author declares no competing interests., (© 2023 The Author(s).)

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 Ni 2+ -nitrogen coordination complexes as a model case, we demonstrate that the quantitative relationship developed from histidine-Ni 2+ and imidazole-Ni 2+ complexes can predict the average relaxation times of other Ni 2+ -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.

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., (© 2022. Springer Nature Limited.)

The prediction of mechanical and dynamical properties of proteins is an important frontier, especially given the greater availability of proteins structures. Here we report a series of models that provide end-to-end predictions of nanodynamical properties of proteins, focused on high-throughput normal mode predictions directly from the amino acid sequence. Using neural network models within the family of Natural Language Processing and graph-based methods, we offer atomistically based mechanistic predictions of key protein mechanical features. The models include an end-to-end long short-term memory (LSTM) model, an end-to-end transformer model, a graph-based transformer model, and an equivariant graph neural network. All four models show exceptional performance, with the graph-based transformer architecture offering the best results but at the cost of requiring a graph structure as input. Conversely, the LSTM and transformer models offer end-to-end sequence-to-property prediction capabilities, providing efficient avenues for protein engineering, analysis, and design. We compare our results against published data based on a Principal Neighborhood Aggregation graph neural network, revealing that the transformer model offers better performance while also being able to predict a large set of the first 64 normal mode frequencies, simultaneously. The use of the end-to-end transformer model may facilitate other downstream applications through the use of transfer learning, and it offers a comprehensive prediction of dynamical properties without any structural knowledge, directly from the amino acid sequence. We demonstrate a potential application in scientific sonification, where the normal mode frequencies are transposed to generate audible signals for a detailed analysis of subtle changes of protein sequences.

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.

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., (Copyright © 2022 by ASME.)

A variety of image generation methods have emerged in recent years, notably DALL-E 2, Imagen and Stable Diffusion. While they have been shown to be capable of producing photorealistic images from text prompts facilitated by generative diffusion models conditioned on language input, their capacity for materials design has not yet been explored. Here, we use a trained Stable Diffusion model and consider it as an experimental system, examining its capacity to generate novel material designs especially in the context of 3D material architectures. We demonstrate that this approach offers a paradigm to generate diverse material patterns and designs, using human-readable language as input, allowing us to explore a vast nature-inspired design portfolio for both novel architectured materials and granular media. We present a series of methods to translate 2D representations into 3D data, including movements through noise spaces via mixtures of text prompts, and image conditioning. We create physical samples using additive manufacturing and assess material properties of materials designed via a coarse-grained particle simulation approach. We present case studies using images as starting point for material generation; exemplified in two applications. First, a design for which we use Haeckel's classic lithographic print of a diatom, which we amalgamate with a spider web. Second, a design that is based on the image of a flame, amalgamating it with a hybrid of a spider web and wood structures. These design approaches result in complex materials forming solids or granular liquid-like media that can ultimately be tuned to meet target demands., (© The Author(s) 2022. Published by Oxford University Press.)

Recently, the potential to create functional materials from various forms of organic matter has received increased interest due to its potential to address environmental concerns. However, the process of creating novel materials from biomass requires extensive experimentation. A promising means of predicting the properties of such materials would be the use of machine-learning models trained on or integrated into self-learned experimental data and methods. We outline an automated system for the discovery and characterization of novel, sustainable, and functional materials from input biomass. Artificial intelligence provides the capacity to examine experimental data, draw connections between composite composition and behavior, and design future experiments to expand the system's understanding of the studied materials. Extensions to the system are described that could further accelerate the discovery of sustainable composites, including the use of interpretable machine-learning methods to expand the insights gleaned from to human-readable materiomic insights about material process-structure-functional relationships., Competing Interests: DECLARATION OF INTERESTS The authors declare no competing interests.

Collagen is one of the most important structural proteins in biology, and its structural hierarchy plays a crucial role in many mechanically important biomaterials. Here, we demonstrate how transformer models can be used to predict, directly from the primary amino acid sequence, the thermal stability of collagen triple helices, measured via the melting temperature T m . We report two distinct transformer architectures to compare performance. First, we train a small transformer model from scratch, using our collagen data set featuring only 633 sequence-to- T m pairings. Second, we use a large pretrained transformer model, ProtBERT, and fine-tune it for a particular downstream task by utilizing sequence-to- T m pairings, using a deep convolutional network to translate natural language processing BERT embeddings into required features. Both the small transformer model and the fine-tuned ProtBERT model have similar R 2 values of test data ( R 2 = 0.84 vs 0.79, respectively), but the ProtBERT is a much larger pretrained model that may not always be applicable for other biological or biomaterials questions. Specifically, we show that the small transformer model requires only 0.026% of the number of parameters compared to the much larger model but reaches almost the same accuracy for the test set. We compare the performance of both models against 71 newly published sequences for which T m has been obtained as a validation set and find reasonable agreement, with ProtBERT outperforming the small transformer model. The results presented here are, to our best knowledge, the first demonstration of the use of transformer models for relatively small data sets and for the prediction of specific biophysical properties of interest. We anticipate that the work presented here serves as a starting point for transformer models to be applied to other biophysical problems.

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 (T m ). Although significant research has been conducted on understanding how collagen primary amino acid sequences correspond to T m values, a robust framework to facilitate the design of collagen sequences with specific T m remains a challenge. Here, we develop a general model using a genetic algorithm within a deep learning framework to design collagen sequences with specific T m 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 T m values using both experimental and computational methods. We find that the model accurately predicts T m 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 T m values for intended materials manufacturing methods and biomedical applications, realizing a mechanistic materials by design paradigm.

3D graphene assemblies are proposed as solutions to meet the goal toward efficient utilization of 2D graphene sheets, showing excellent performances in applications such as mechanical support, energy storage, and electrochemical catalysis. However, given the diversity and complexity of possible graphene 3D structures, there does not yet exist a systematic approach that can generate target 3D shapes and also, evaluate their performance. Here high-throughput data generation is combined with artificial intelligence approaches to realize rapid structure formation and property quantification of 3D graphene foams with mathematically controlled topologies, driven by molecular dynamics simulations. More than 4000 different foam structures are created, which feature diverse topologies that contain potential pathways for small molecules and auxetic structures with negative Poisson's ratio. Empowered by machine learning (ML) algorithms including graph neural networks, not only global properties such as elastic moduli, but also local behaviors such as atomic stress can be predicted and optimized based on their atomic structure, bypassing expensive atomistic simulations. The key findings of the research reported in this paper include a high-throughput virtual framework of generating diverse 3D graphene assemblies with mechanical performances quantification, and highly efficient methods of evaluating physical properties based on ML., (© 2022 The Authors. Small Methods published by Wiley-VCH GmbH.)

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.

Biomineralization is a common strategy used in Nature to improve the mechanical strength and toughness of biological materials. This strategy, applied in materials like bone or nacre, serves as inspiration for materials scientists and engineers to design new materials for applications in healthcare, soft robotics or the environment. In this regard, composites consisting of silk and hydroxyapatite have been extensively researched for bone regeneration applications, due to their reported cytocompatibility and osteoinduction capacity that supports bone formation in vivo . Thus, it becomes relevant to understand how silk and hydroxyapatite interact at their interface, and how this affects the overall mechanical properties of these composites. This theoretical-experimental work investigates the interfacial dynamic and structural properties of silk in contact with hydroxyapatite, combining molecular dynamics simulations with analytical characterization. Our data indicate that hydroxyapatite decreases the β-sheets in silk, which are a key load-bearing element of silk. The β-sheets content can usually be increased in silk biomaterials via post-processing methods, such as water vapor annealing. However, the presence of hydroxyapatite appears to reduce also for the formation of β-sheets via water vapor annealing. This work sheds light into the interfacial properties of silk-hydroxyapatite composite and their relevance for the design of composite biomaterials for bone regeneration.

Human enamel is an incredibly resilient biological material, withstanding repeated daily stresses for decades. The mechanisms behind this resilience remain an open question, with recent studies demonstrating a crack-deflection mechanism contributing to enamel toughness and other studies detailing the roles of the organic matrix and remineralization. Here, we focus on the mineral and hypothesize that self-healing of cracks in enamel nanocrystals may be an additional mechanism acting to prevent catastrophic failure. To test this hypothesis, we used a molecular dynamics (MD) approach to compare the fracture behavior of hydroxyapatite (HAP) and calcite, the main minerals in human enamel and sea urchin teeth, respectively. We find that cracks heal under pressures typical of mastication by fusion of crystals in HAP but not in calcite, which is consistent with the resilience of HAP enamel that calcite teeth lack. Scanning transmission electron microscopy (STEM) images of structurally intact ("sound") human enamel show dashed-line nanocracks that resemble and therefore might be the cracks healed by fusion of crystals produced in silico . The fast, self-healing mechanism shown here is common in soft materials and ceramics but has not been observed in single crystalline materials at room temperature. The crack self-healing in sound enamel nanocrystals, therefore, is unique in the human body and unique in materials science, with potential applications in designing bioinspired materials.

Deep learning holds great promise for applications in materials science, including the discovery of physical laws and materials design. However, the availability of proper data remains a challenge - often, data lacks labels, or does not contain direct pairing between input and output property of interest. Here we report an approach based on an adversarial neural network model - composed of four individual deep neural nets - to yield atomistic-level prediction of stress fields directly from an input atomic microstructure, illustrated here for defected graphene sheets under tension. The primary question we address is whether it is possible to predict stress fields without any microstructure-to-stress fields pairings, nor the existence of any input-output pairs whatsoever, in the dataset. Using a cycle-consistent adversarial neural net with either U-Net, ResNet and a hybrid U-Net-ResNet architecture, applied to a system of graphene lattices with defects we devise an algorithmic framework that enables us to successfully train and validate a model that reliably predicts atomistic-level field data of unknown microstructures, generalizing to reproduce well-known nano- and micromechanical features such as stress concentrations, size effects, and crack shielding. In a series of validation analyses, we show that the model closely reproduces reactive molecular dynamics simulations but at significant computational efficiency, and without a priori knowledge of any physical laws that govern this complex fracture problem. The model opens an avenue for upscaling where the mechanistic insights, and predictions from the model, can be used to construct analyses of very large systems, based off relatively small and sparse datasets. Since the model is trained to achieve cycle consistency, a trained model features both forward (microstructure to stress) and inverse (stress to microstructure) generators; offering potential applications in materials design to achieve a certain stress field. Another application is the prediction of stress fields based off experimentally acquired structural data, where the knowledge of solely positions of atoms is sufficient to predict physical quantities for augmentation or analysis processes., Competing Interests: The authors declare no conflict of interest., (This journal is © The Royal Society of Chemistry.)

The COVID-19 pandemic has been inflicted upon humanity by the SARS-CoV-2 virus, the latest insidious incarnation of the coronaviruses group. While in its wake intense scientific research has produced breakthrough vaccines and cures, there still exists an immediate need to further understand the origin, mechanobiology and biochemistry, and destiny of this virus so that future pandemics arising from similar coronaviruses may be contained more effectively. In this Perspective, we discuss the various evidential findings of virus propagation and connect them to respective underpinning cellular biomechanical states leading to corresponding manifestations of the viral activity. We further propose avenues to tackle the virus, including from a "musical" vantage point, and contain its relentless strides that are currently afflicting the global populace.

Natural vibrational frequencies of proteins help to correlate functional shifts with sequence or geometric variations that lead to negligible changes in protein structures, such as point mutations related to disease lethality or medication effectiveness. Normal mode analysis is a well-known approach to accurately obtain protein natural frequencies. However, it is not feasible when high-resolution protein structures are not available or time consuming to obtain. Here we provide a machine learning model to directly predict protein frequencies from primary amino acid sequences and low-resolution structural features such as contact or distance maps. We utilize a graph neural network called principal neighborhood aggregation, trained with the structural graphs and normal mode frequencies of more than 34 000 proteins from the protein data bank. combining with existing contact/distance map prediction tools, this approach enables an end-to-end prediction of the frequency spectrum of a protein given its primary sequence., Competing Interests: The authors declare no conflict of interests., (This journal is © The Royal Society of Chemistry.)

Structural proteins are the basis of many biomaterials and key construction and functional components of all life. Further, it is well-known that the diversity of proteins' function relies on their local structures derived from their primary amino acid sequences. Here, we report a deep learning model to predict the secondary structure content of proteins directly from primary sequences, with high computational efficiency. Understanding the secondary structure content of proteins is crucial to designing proteins with targeted material functions, especially mechanical properties. Using convolutional and recurrent architectures and natural language models, our deep learning model predicts the content of two essential types of secondary structures, the α-helix and the β-sheet. The training data are collected from the Protein Data Bank and contain many existing protein geometries. We find that our model can learn the hidden features as patterns of input sequences that can then be directly related to secondary structure content. The α-helix and β-sheet content predictions show excellent agreement with training data and newly deposited protein structures that were recently identified and that were not included in the original training set. We further demonstrate the features of the model by a search for de novo protein sequences that optimize max/min α-helix/β-sheet content and compare the predictions with folded models of these sequences based on AlphaFold2 . Excellent agreement is found, underscoring that our model has predictive potential for rapidly designing proteins with specific secondary structures and could be widely applied to biomedical industries, including protein biomaterial designs and regenerative medicine applications.

Collagen is the most abundant structural protein in humans, with dozens of sequence variants accounting for over 30% of the protein in an animal body. The fibrillar and hierarchical arrangements of collagen are critical in providing mechanical properties with high strength and toughness. Due to this ubiquitous role in human tissues, collagen-based biomaterials are commonly used for tissue repairs and regeneration, requiring chemical and thermal stability over a range of temperatures during materials preparation ex vivo and subsequent utility in vivo. Collagen unfolds from a triple helix to a random coil structure during a temperature interval in which the midpoint or T m is used as a measure to evaluate the thermal stability of the molecules. However, finding a robust framework to facilitate the design of a specific collagen sequence to yield a specific T m remains a challenge, including using conventional molecular dynamics modeling. Here we propose a de novo framework to provide a model that outputs the T m values of input collagen sequences by incorporating deep learning trained on a large data set of collagen sequences and corresponding T m values. By using this framework, we are able to quickly evaluate how mutations and order in the primary sequence affect the stability of collagen triple helices. Specifically, we confirm that mutations to glycines, mutations in the middle of a sequence, and short sequence lengths cause the greatest drop in T m values., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Architected biomaterials, as well as sound and music, are constructed from small building blocks that are assembled across time- and length-scales. Here we present a novel deep learning-enabled integrated algorithmic workflow to merge the two concepts for radical discovery of de novo protein materials, exploiting musical creativity as the foundation, and extrapolating through a recursive method to increase protein complexity by successively injecting protein chemistry into the process. Indeed, music is one of the few universal expressions that can create bridges between cultures, find associations between seemingly unrelated concepts, and can be used as a novel way to generate bio-inspired designs that derive functions from the imaginations of the creative mind. Earlier work has offered a pathway to convert proteins into sound, and sound into proteins. Here we build on this paradigm and translate a piece of classical music into matter. Based on Bach's Goldberg variations, we offer a series of case studies to convert the musical data imagined by the composer into protein design, and folded into a 3D structure using deep learning. The quest we seek to address is to identify semblances, or memories, or information content in such musical creation, that offers new insights into pattern relationships between distinct manifestations of information. Using basic local alignment search tool analysis, we find that several fragments of the new proteins display similarities to existing protein sequences found in proteobacteria among other organisms, especially in regions of low complexity and repetitive motifs. The resulting protein forms the basis for iterative musical composition, and an evolutionary paradigm that defines a variational pathway for melodic development, complementing conventional creative or mathematical methods. This paper broadens the concept of what is understood as bio-inspiration to include a broad array of systems created by humans, animals, or other natural mechanisms., (© 2021 IOP Publishing Ltd.)

Machine learning methods have the potential to transform imaging techniques and analysis for healthcare applications with automation, making diagnostics and treatment more accurate and efficient, as well as to provide mechanistic insights into tissue deformation and fracture in physiological and pathological conditions. Here we report an exploratory investigation for the classification and prediction of mechanical states of cortical and trabecular bone tissue using convolutional neural networks (CNNs), residual neural networks (ResNet), and transfer learning applied to a novel dataset derived from high-resolution synchrotron-radiation micro-computed tomography (SR-microCT) images acquired in uniaxial continuous compression in situ. We present the systematic optimization of CNN architectures for classification of this dataset, visualization of class-defining features detected by the CNNs using gradient class activation maps (Grad-CAMs), comparison of CNN performance with ResNet and transfer learning models, and perhaps most critically, the challenges that arose from applying machine learning methods to an experimentally-derived dataset for the first time. With optimized CNN architectures, we obtained trained models that classified novel images between failed and pristine classes with over 98% accuracy for cortical bone and over 90% accuracy for trabecular bone. Harnessing a pre-trained ResNet with transfer learning, we further achieved over 98% accuracy on the cortical dataset, and 99% on the trabecular dataset. This demonstrates that powerful classifiers for high-resolution SR-microCT images can be developed even with few unique training samples and invites further development through the inclusion of more data and training methods to move towards novel, fundamental, and machine learning-driven insights into microstructural states and properties of bone., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

We propose a simple general framework to predict folding, native states, energy barriers, protein unfolding, as well as mutation induced diseases and other protein structural analyses. The model should not be considered as an alternative to classical approaches (Molecular Dynamics or Monte Carlo) because it neglects low scale details and rather focuses on global features of proteins and structural information. We aim at the description of phenomena that are out of the range of classical molecular modeling approaches due to the large computational cost: multimolecular interactions, cyclic behavior under variable external interactions, and similar. To demonstrate the effectiveness of the approach in a real case, we focus on the folding and unfolding behavior of tropoelastin and its mutations. Specifically, we derive a discrete mechanical model whose structure is deduced based on a coarse graining approach that allows us to group the amino acids sequence in a smaller number of `equivalent' masses. Nearest neighbor energy terms are then introduced to reproduce the interaction of such amino acid groups. Nearest and non-nearest neighbor energy terms, inter and intra functional blocks are phenomenologically added in the form of Morse potentials. As we show, the resulting system reproduces important properties of the folding-unfolding mechanical response, including the monotonic and cyclic force-elongation behavior, representing a physiologically important information for elastin. The comparison with the experimental behavior of mutated tropoelastin confirms the predictivity of the model. STATEMENT OF SIGNIFICANCE: Classical approaches to the study of phenomena at the molecular scale such as Molecular Dynamics (MD) represent an incredible tool to unveil mechanical and conformational properties of macromolecules, in particular for biological and medical applications. On the other hand, due to the computational cost, the time and spatial scales are limited. Focusing of the real case of tropoelastin, we propose a new approach based on a careful coarse graining of the system, able to describe the overall properties of the macromolecule and amenable of extension to larger scale effects (protein bundles, protein-protein interactions, cyclic loading). The comparison with tropoelastin behavior, also for mutations, is very promising., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.)

Understanding and broad screening Li interaction energetics with surfaces are key to the development of materials for a wide range of applications including Li-based electrochemical capacitors, Li sensors, Li separation membranes, and Li-ion batteries. In this work, we build a high-throughput screening scheme to screen Li adsorption energetics on 2D metallic materials. First, density functional theory and graph convolution networks are utilized to calculate the minimum Li adsorption energies for some 2D metallic materials. The data is then used to find a dependence of the minimum Li adsorption energies on the sum of ionization potential, work function of the 2D metal, and coupling energy between Li + and substrate, and the dependence is used to screen all 2D metallic materials. Physics-simplified learning by splitting the property into different contributions and learning or calculating each component is shown to have higher accuracy and transferability for machine learning of complex materials properties., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

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., Competing Interests: The authors declare no competing interest.

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 α v β 3 using ensemble-based computational methodologies. Conformational changes of integrin α v β 3 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 α v β 3 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., (Copyright © 2021 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Fire has fascinated humankind since the prehistoric era. Rooted in the interactions between sound and flames, here we report a method to use fire for a variety of purposes, including sonification, art, and the design and manufacturing nature-inspired materials. We present a method to sonify fire, thereby offering a translation from the silent nature of flames, to represent audible information and to generate de novo flame images. To realize material specimen derived from fire, we use the autoencoder to generate image stacks to yield continuous 3D geometries that are manufactured using 3D printing. This represents the first generation of nature-inspired materials from fire and can be a platform to be used for other natural phenomena in the quest for de novo architectures, geometries, and design ideas, thus creating additional directions in artistic and scientific research through the creative manipulation of data with structural similarities across fields., Competing Interests: The authors declare no competing interests., (© 2021 The Authors.)