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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.

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.

Stress distribution has led to the design of both tough and lightweight materials. Truss structures distribute stress well and are commonly used to design lightweight materials for applications experiencing low strains. In 3D lattices, however, few structures allow high elastic compression and tunable deformation. This is especially true for auxetic material designs, such as the prototypical re-entrant honeycomb with sharp corners, which are particularly susceptible to stress concentrations. There is a pressing need for lightweight lattice designs that are dynamic, as well as resistant to fatigue. Truss designs based on hinged structures exist in nature and delocalize stress rather than concentrating it in small areas. They have inspired us to develop s-hinge shaped elastic unit cell elements from which new classes of architected modular 2D and 3D lattices can be printed or assembled. These lattices feature locally tunable Poisson ratios (auxetic), large elastic deformations without fatigue, as well as mechanical switching between multistable states. We demonstrate 3D printed structures with stress delocalization that enables macroscopic 30% cyclable elastic strains, far exceeding those intrinsic to the materials that constitute them (6%). We also present a simple semi-analytical model of the deformations which is able to predict the mechanical properties of the structures within <5% error of experimental measurements from a few parameters such as dimensions and material properties. Using this model, we discovered and experimentally verified a critical angle of the s-hinge enabling bistable transformations between auxetic and normal materials. The dynamic modeling tools developed here could be used for complex 3D designs from any 3D printable material (metals, ceramics, and polymers). Locally tunable deformation and much higher elastic strains than the parent material would enable the next generation of compact, foldable and expandable structures. Mixing unit cells with different hinge angles, we designed gradient Poisson's ratio materials, as well as ones with multiple stable states where elastic energy can be stored in latching structures, offering prospects for multi-functional designs. Much like the energy efficient Venus flytrap, such structures can store elastic energy and release it on demand when appropriate stimuli are present.

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.