724 research outputs found
Flaw-tolerance in silk fibrils explains strength, extensibility and toughness of spider silk
Silk is an ancient but remarkably strong, extensible and tough material made from simple protein building blocks. Earlier work has shown that the particular molecular geometry of silk with a composite of semi-amorphous and nanocrystalline beta-sheet protein domains provides the structural basis for its characteristic softening-stiffening behavior and remarkable strength at the nanoscale. Yet, an open question remains as to how these nanoscale properties are upscaled so effectively to create strong, extensible and tough silk fibers. Here we discover that the geometric confinement of fibrils to ≈50-100 nm width and arranged in bundles to form larger-scale silk fibers, is the key to explaining the upscaling of the mechanical properties of silk from the atomistic scale upwards. We find that under this geometric confinement, hundreds of thousands of protein domains unfold simultaneously and thereby act synergistically to resist deformation and failure, providing access to enhanced large-scale strength, extensibility and toughness. Moreover, since the material is in a flaw-tolerant state under this geometric confinement, structural inhomogeneities such as cavities or tears that typically act as stress concentrators do not compromise the material performance. Indeed, experimental work showed that the diameter of silk fibrils that make up larger-scale silk fibers are on the order of 20-100 nm, in agreement with our findings. The exploitation of this mechanism in engineering design enables the synthesis of hierarchical fiber materials for superior performance despite limited and inferior building blocks
Hierarchical coexistence of universality and diversity controls robustness and multi-functionality in intermediate filament protein networks
Proteins constitute the elementary building blocks of a vast variety of biological materials such as cellular protein networks, spider silk or bone, where they create extremely robust, multi-functional materials by self-organization of structures over many length- and time scales, from nano to macro. Some of the structural features are commonly found in a many different tissues, that is, they are highly conserved. Examples of such universal building blocks include alpha-helices, beta-sheets or tropocollagen molecules. In contrast, other features are highly specific to tissue types, such as particular filament assemblies, beta-sheet nanocrystals in spider silk or tendon fascicles. These examples illustrate that the coexistence of universality and diversity – in the following referred to as the universality-diversity paradigm (UDP) – is an overarching feature in protein materials. This paradigm is a paradox: How can a structure be universal and diverse at the same time? In protein materials, the coexistence of universality and diversity is enabled by utilizing hierarchies, which serve as an additional dimension beyond the 3D or 4D physical space. This may be crucial to understand how their structure and properties are linked, and how these materials are capable of combining seemingly disparate properties such as strength and robustness. Here we illustrate how the UDP enables to unify universal building blocks and highly diversified patterns through formation of hierarchical structures that lead to multi-functional, robust yet highly adapted structures. We illustrate these concepts in an analysis of three types of intermediate filament proteins, including vimentin, lamin and keratin
Molecular Modeling and Mechanics of Acrylic Adhesives on a Graphene Substrate with Roughness
Understanding the mechanics of amorphous polymeric adhesives on a solid substrate at the fundamental scale level is critical for designing and optimizing the mechanics of composite materials. Using molecular dynamics simulations, we investigate the interfacial strength between graphene and polyacrylic and discuss how the surface roughness of graphene affects the interfacial strength in different loading directions. Our results show that a single angstrom increase in graphene roughness can lead to almost eight times higher shear strength, and that such result is insensitive to compression. We have also revealed that the graphene roughness has modest effect on tensile strength of the interface. Our simulations elucidate the molecular mechanism of these different effects in different loading conditions and provide insights for composite designs.Henkel Corporatio
Materials by design—A perspective from atoms to structures
Biological materials are effectively synthesized, controlled, and used for a variety of purposes in Nature—in spite of limitations in energy, quality, and quantity of their building blocks. Whereas the chemical composition of materials in the living world plays some role in achieving functional properties, the way components are connected at different length scales defines what material properties can be achieved, how they can be altered to meet functional requirements, and how they fail in disease states and other extreme conditions. Recent work has demonstrated this using large-scale computer simulations to predict materials properties from fundamental molecular principles, combined with experimental work and new mathematical techniques to categorize complex structure-property relationships into a systematic framework. Enabled by such categorization, we discuss opportunities based on the exploitation of concepts from distinct hierarchical systems that share common principles in how function is created, even linking music to materials science.National Science Foundation (U.S.) (CAREER 0642545)United States. Office of Naval Research (PECASE N00014-10-1-0562)United States. Air Force Office of Scientific Research (FA9550-11-1-0199)National Institutes of Health (U.S.) (U01 EB014976
Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design
Transformer neural networks show promising capabilities, in particular for
uses in materials analysis, design and manufacturing, including their capacity
to work effectively with both 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. 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 finetuning endows LLMs
with reasonable understanding of domain knowledge. However, when queried
outside the context of learned matter, LLMs can have difficulty to recall
correct information. We show how this can be addressed using
retrieval-augmented Ontological Knowledge Graph strategies that discern how the
model understands what concepts are important and how they are related.
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. We
discuss nonlinear sampling strategies and agent-based modeling applied to
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
Liquified protein vibrations, classification and cross-paradigm de novo image generation using deep neural networks
In recent work we reported the vibrational spectrum of more than 100,000
known protein structures, and a self-consistent sonification method to render
the spectrum in the audible range of frequencies (Extreme Mechanics Letters,
2019). Here we present a method to transform these molecular vibrations into
materialized vibrations of thin water films using acoustic actuators, leading
to complex patterns of surface waves, and using the resulting macroscopic
images in further processing using deep convolutional neural networks.
Specifically, the patterns of water surface waves for each protein structure is
used to build training sets for neural networks, aimed to classify and further
process the patterns. Once trained, the neural network model is capable of
discerning different proteins solely by analyzing the macroscopic surface wave
patterns in the water film. Not only can the method distinguish different types
of proteins (e.g. alpha-helix vs hybrids of alpha-helices and beta-sheets), but
it is also capable of determining different folding states of the same protein,
or the binding events of proteins to ligands. Using the DeepDream algorithm,
instances of key features of the deep neural network can be made visible in a
range of images, allowing us to explore the inner workings of protein surface
wave patter neural networks, as well as the creation of new images by finding
and highlighting features of protein molecular spectra in a range of
photographic input. The integration of the water-focused realization of
cymatics, combined with neural networks and especially generative methods,
offer a new direction to realize materiomusical "Inceptionism" as a possible
direction in nano-inspired art. The method could have applications for
detecting different protein structures, the effect of mutations, or uses in
medical imaging and diagnostics, with broad impact in nano-to-macro
transitions.Comment: 19 pages, 11 figure
MeLM, a generative pretrained language modeling framework that solves forward and inverse mechanics problems
We report a flexible multi-modal mechanics language model, MeLM, applied to
solve various nonlinear forward and inverse problems, that can deal with a set
of instructions, numbers and microstructure data. The framework is applied to
various examples including bio-inspired hierarchical honeycomb design, carbon
nanotube mechanics, and protein unfolding. In spite of the flexible nature of
the model-which allows us to easily incorporate diverse materials, scales, and
mechanical features-it performs well across disparate forward and inverse
tasks. Based on an autoregressive attention-model, MeLM effectively represents
a large multi-particle system consisting of hundreds of millions of neurons,
where the interaction potentials are discovered through graph-forming
self-attention mechanisms that are then used to identify relationships from
emergent structures, while taking advantage of synergies discovered in the
training data. We show that the model can solve complex degenerate mechanics
design problems and determine novel material architectures across a range of
hierarchical levels, providing an avenue for materials discovery and analysis.
Looking beyond the demonstrations reported in this paper, we discuss other
opportunities in applied mechanics and general considerations about the use of
large language models in modeling, design, and analysis that can span a broad
spectrum of material properties from mechanical, thermal, optical, to
electronic
Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins
We report a flexible language-model based deep learning strategy, applied
here to solve complex forward and inverse problems in protein modeling, based
on an attention neural network that integrates transformer and graph
convolutional architectures in a causal multi-headed graph mechanism, to
realize a generative pretrained model. The model is applied to predict
secondary structure content (per-residue level and overall content), protein
solubility, and sequencing tasks. Further trained on inverse tasks, the model
is rendered capable of designing proteins with these properties as target
features. The model is formulated as a general framework, completely
prompt-based, and can be adapted for a variety of downstream tasks. We find
that adding additional tasks yields emergent synergies that the model exploits
in improving overall performance, beyond what would be possible by training a
model on each dataset alone. Case studies are presented to validate the method,
yielding protein designs specifically focused on structural proteins, but also
exploring the applicability in the design of soluble, antimicrobial
biomaterials. While our model is trained to ultimately perform 8 distinct
tasks, with available datasets it can be extended to solve additional problems.
In a broader sense, this work illustrates a form of multiscale modeling that
relates a set of ultimate building blocks (here, byte-level utf8 characters
that define the nature of the physical system at hand) to complex output. This
materiomic scheme captures complex emergent relationships between universal
building block and resulting properties via a synergizing learning capacity to
express a set of potentialities embedded in the knowledge used in training, via
the interplay of universality and diversity
Hierarchical nanomechanics of collagen microfibrils
Collagen constitutes one third of the human proteome, providing mechanical stability, elasticity and strength to connective tissues. Collagen is also the dominating material in the extracellular matrix (ECM) and is thus crucial for cell differentiation, growth and pathology. However, fundamental questions remain with respect to the origin of the unique mechanical properties of collagenous tissues, and in particular its stiffness, extensibility and nonlinear mechanical response. By using x-ray diffraction data of a collagen fibril reported by Orgel et al. (Proceedings of the National Academy of Sciences USA, 2006) in combination with protein structure identification methods, here we present an experimentally validated model of the nanomechanics of a collagen microfibril that incorporates the full biochemical details of the amino acid sequence of the constituting molecules. We report the analysis of its mechanical properties under different levels of stress and solvent conditions, using a full-atomistic force field including explicit water solvent. Mechanical testing of hydrated collagen microfibrils yields a Young’s modulus of ≈300 MPa at small and ≈1.2 GPa at larger deformation in excess of 10% strain, in excellent agreement with experimental data. Dehydrated, dry collagen microfibrils show a significantly increased Young’s modulus of ≈1.8 to 2.25 GPa (or ≈6.75 times the modulus in the wet state) owing to a much tighter molecular packing, in good agreement with experimental measurements (where an increase of the modulus by ≈9 times was found). Our model demonstrates that the unique mechanical properties of collagen microfibrils can be explained based on their hierarchical structure, where deformation is mediated through mechanisms that operate at different hierarchical levels. Key mechanisms involve straightening of initially disordered and helically twisted molecules at small strains, followed by axial stretching of molecules, and eventual molecular uncoiling at extreme deformation. These mechanisms explain the striking difference of the modulus of collagen fibrils compared with single molecules, which is found in the range of 4.8±2 GPa or ≈10-20 times greater. These findings corroborate the notion that collagen tissue properties are highly scale dependent and nonlinear elastic, an issue that must be considered in the development of models that describe the interaction of cells with collagen in the extracellular matrix. A key impact the atomistic model of collagen microfibril mechanics reported here is that it enables the bottom-up elucidation of structure-property relationships in the broader class of collagen materials such as tendon or bone, including studies in the context of genetic disease where the incorporation of biochemical, genetic details in material models of connective tissue is essential
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