induced scission measurements, imaging with AFM and TEM, and analysis of data. A.J.L. performed FRET measurements and analysis of the data. X.Z., T.C.-T., and Y.C. performed solution X-ray scattering, and analysis of the data. A.J.L. and Y.C. conceptualized nanoribbon thread processing and Y.C. and M.G. prepared nanoribbon threads. M.G. performed tensile testing of nanoribbon threads and analysis of the data. T.C.-T. and Y.C. performed X-ray scattering of solid-state nanoribbon threads and analysis of the data. J.

Traditionally, gels have been defined by their covalently cross-linked polymer networks. Supramolecular gels challenge this framework by relying on non-covalent interactions for self-organization into hierarchical structures. This class of materials offers a variety of novel and exciting potential applications. This review draws together recent advances in supramolecular gels with an emphasis on their proposed uses as optoelectronic, energy, biomedical, and biological materials. Additional special topics reviewed include environmental remediation, participation in synthesis procedures, and other industrial uses. The examples presented here demonstrate unique benefits of supramolecular gels, including tunability, processability, and self-healing capability, enabling a new approach to solve engineering challenges.

Understanding fracture is critical to the design of resilient nanomaterials. Molecular dynamics offers a way to study fracture at an atomistic level, but is computationally expensive with limitations of scalability. In this work, we build upon machine-learning approaches for predicting nanoscopic fracture mechanisms including crack instabilities and branching as a function of crystal orientation. We focus on a particular technologically relevant material system, graphene, and apply a deep learning method to the study of such nanomaterials and explore the parameter space necessary for calibrating machine-learning predictions to meaningful results. Our results validate the ability of deep learning methods to quantitatively capture graphene fracture behavior, including its fractal dimension as a function of crystal orientation, and provide promise toward the wider application of deep learning to materials design, opening the potential for other 2D materials.

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Strongly interacting amphiphilic molecules self-assemble in water. The flexibility of the amphiphiles and their head group repulsion mediate their nanostructure geometry.

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 crackdeflection 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.

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 (CaCO3) polymorphs, differing in crystal structure. Unexpectedly, diverse CaCO3 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.

The gestalt of computational methods including physics-based molecular dynamics simulations, data-driven machine learning (ML) models, and biologically-inspired genetic algorithms affords a powerful toolbox for tackling materials mechanism discovery and design problems. Here, we leverage these methods to investigate the complex multidimensional problem of polycrystalline 2D material fracture. We focus first on graphene and in doing so, demonstrate a practical workflow for exploring the structural dependencies of fracture energy. Despite training our ML model on exclusively single crystal fracture in increments of 10° orientations, we can identify a crack branching mechanism responsible for high bicrystal toughness centered at initial crystal orientation angles of 19° and 41°. These high peaks span only a few degrees in range and are completely overlooked by a search with stride 10°. Furthermore, we can discover qualitative physical phenomena such as collective fracture branch termination and extract quantitative trends relating angular dispersion and mis-orientation angles of crystal grains to fracture energy. None of these complex polycrystalline behaviors were presented in the training data, and the predictive power of the model ultimately allows us to expeditiously generate polycrystalline graphene structures with bespoke fracture paths, a task with great implications in industrial design applications and mechanism discovery. Furthermore, the approach is not limited to graphene specifically, as we demonstrate by retraining the model for another more complex 2D material—MoS2—and achieve polycrystalline fracture predictions of comparable accuracy.