Identifying structural signatures of shear banding in model polymer nanopillars

Elasto-plastic effects on shape-shifting electron-beam-patterned gel-based micro-helices

Shape-shifting helical gels have been created by various routes, notably by photolithography. We explore electron-beam lithography as an alternative to prescribe microhelix formation in tethered patterns of pure poly(acrylic acid). Simulations indicate the nanoscale spatial distribution of deposited energy that drives the loss of acid groups and crosslinking. Upon exposure to buffer, a patterned line converts to a 3D helix whose cross section comprises a crosslinked and hydrophobic core surrounded by a high-swelling pH-responsive corona. Through-thickness asymmetries generate out-of-plane bending to drive helix formation. The relative core and corona fractions are determined by the electron dose which in turn controls the helical radius and pitch. Increasing pH substantially raises the swelling stress and the rod elongates plastically. The pitch concurrently changes from minimal to non-minimal. The in-plane asymmetry driving this change can be attributed to shear-band formation in the hydrophobic core. Subsequent pH cycling drives elastic cycling of the helical properties. These findings illustrate the effects of elastoplastic deformation on helical properties and elaborate unique attributes of electron lithography as an alternate means to create shape-shifting structures.

Identifying microscopic factors that influence ductility in disordered solids

There are empirical strategies for tuning the degree of strain localization in disordered solids, but they are system-specific and no theoretical framework explains their effectiveness or limitations. Here, we study three model disordered solids: a simulated atomic glass, an experimental granular packing, and a simulated polymer glass. We tune each system using a different strategy to exhibit two different degrees of strain localization. In tandem, we construct structuro-elastoplastic (StEP) models, which reduce descriptions of the systems to a few microscopic features that control strain localization, using a machine learning-based descriptor, softness, to represent the stability of the disordered local structure. The models are based on calculated correlations of softness and rearrangements. Without additional parameters, the models exhibit semiquantitative agreement with observed stress-strain curves and softness statistics for all systems studied. Moreover, the StEP models reveal that initial structure, the near-field effect of rearrangements on local structure, and rearrangement size, respectively, are responsible for the changes in ductility observed in the three systems. Thus, StEP models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity, and elasticity.

Understanding creep suppression mechanisms in polymer nanocomposites through machine learning

While recent efforts have shown how local structure plays an essential role in the dynamic heterogeneity of homogeneous glass-forming materials, systems containing interfaces such as thin films or composite materials remain poorly understood. It is known that interfaces perturb the molecular packing nearby, however, numerous studies show the dynamics are modified over a much larger range. Here, we examine the dynamics in polymer nanocomposites (PNCs) using a combination of simulations and experiments and quantitatively separate the role of polymer packing from other effects on the dynamics, as a function of distance from the nanoparticle surfaces. After showing good qualitative agreement between the simulations and experiments in glassy structure and creep compliance, we use a machine-learned structure indicator, softness, to decompose polymer dynamics in our simulated PNCs into structure-dependent and structure-independent processes. With this decomposition, the free energy barrier for polymer rearrangement can be described as a combination of packing-dependent and packing-independent barriers. We find both barriers are higher near nanoparticles and decrease with applied stress, quantitatively demonstrating that the slow interfacial dynamics is not solely due to polymer packing differences, but also the change of structure-dynamics relationships. Finally, we present how this decomposition can be used to accurately predict strain-time creep curves for PNCs from their static configuration, providing additional insights into the effects of polymer-nanoparticle interfaces on creep suppression in PNCs.

Role of Local Structure in the Enhanced Dynamics of Deformed Glasses

External stress can accelerate molecular mobility of amorphous solids by several orders of magnitude. The changes in mobility are commonly interpreted through the Eyring model, which invokes an empirical activation volume. Here, we analyze constant-stress molecular dynamics simulations and propose a structure-dependent Eyring model, connecting activation volume to a machine-learned field, softness. We show that stress has a heterogeneous effect on the mobility that depends on local structure through softness. The barrier impeding relaxation reduces more for well-packed particles, which explains the narrower distribution of relaxation time observed under stress.

The initiation of shear band formation in deformed metallic glasses from soft localized domains

It has long been thought that shear band (SB) formation in amorphous solids initiates from relatively "soft" regions in the material in which large-scale non-affine deformations become localized. The test of this hypothesis requires an effective means of identifying "soft" regions and their evolution as the material is deformed to varying degrees, where the metric of "softness" must also account for the effect of temperature on local material stiffness. We show that the mean square atomic displacement on a caging timescale ⟨u²⟩, the "Debye-Waller factor," provides a useful method for estimating the shear modulus of the entire material and, by extension, the material stiffness at an atomic scale. Based on this "softness" metrology, we observe that SB formation indeed occurs through the strain-induced formation of localized soft regions in our deformed metallic glass free-standing films. Unexpectedly, the critical strain condition for SB formation occurs when the softness (⟨u²⟩) distribution within the emerging soft regions approaches that of the interfacial region in its undeformed state, initiating an instability with similarities to the transition to turbulence. Correspondingly, no SBs arise when the material is so thin that the entire material can be approximately described as being "interfacial" in nature. We also quantify relaxation in the glass and the nature and origin of highly non-Gaussian particle displacements in the dynamically heterogeneous SB regions at times longer than the caging time.

Using delaunay triangularization to characterize non-affine displacement fields during athermal, quasistatic deformation of amorphous solids

We investigate the non-affine displacement fields that occur in two-dimensional Lennard-Jones models of metallic glasses subjected to athermal, quasistatic simple shear (AQS). During AQS, the shear stress versus strain displays continuous quasi-elastic segments punctuated by rapid drops in shear stress, which correspond to atomic rearrangement events. We capture all information concerning the atomic motion during the quasi-elastic segments and shear stress drops by performing Delaunay triangularizations and tracking the deformation gradient tensor F_α associated with each triangle α. To understand the spatio-temporal evolution of the displacement fields during shear stress drops, we calculate F_α along minimal energy paths from the mechanically stable configuration immediately before to that after the stress drop. We find that quadrupolar displacement fields form and dissipate both during the quasi-elastic segments and shear stress drops. We then perform local perturbations (rotation, dilation, simple and pure shear) to single triangles and measure the resulting displacement fields. We find that local pure shear deformations of single triangles give rise to mostly quadrupolar displacement fields, and thus pure shear strain is the primary type of local strain that is activated by bulk, athermal quasistatic simple shear. Other local perturbations, e.g. rotations, dilations, and simple shear of single triangles, give rise to vortex-like and dipolar displacement fields that are not frequently activated by bulk AQS. These results provide fundamental insights into the non-affine atomic motion that occurs in driven, glassy materials.

Characterising soft matter using machine learning

Machine learning is making a major impact in materials research. I review current progress across a selection of areas of ubiquitous soft matter. When applied to particle tracking, machine learning using convolution neural networks is providing impressive performance but there remain some significant problems to solve. Characterising ordered arrangements of particles is a huge challenge and machine learning has been deployed to create the description, perform the classification and tease out an interpretation using a wide array of techniques often with good success. In glass research, machine learning has proved decisive in quantifying very subtle correlations between the local structure around a site and the susceptibility towards a rearrangement event at that site. There are also beginning to be some impressive attempts to deploy machine learning in the design of composite soft materials. The discovery aspect of this new materials design meets the current interest in teaching algorithms to learn to extrapolate beyond the training data.

Confinement effects on the mechanical heterogeneity of polymer fiber glasses

Both polymer fiber glasses and bulk polymer glasses exhibit nonlinear mechanical responses under uniaxial deformation. In polymer fibers, however, polymer chains are confined strongly and the surface area is relatively large compared to their volume. The confinement and the surface may lead to the spatially heterogeneous relaxation of chains in polymer fibers. In this work we perform molecular dynamics simulations and investigate the relation between the heterogeneous dynamics and the nonlinear mechanical responses at a molecular level. Our molecular simulations capture successfully not only the nonlinear mechanical response but also the dependence of mechanical properties on the strain rate of typical polymer glasses as in experiments. We find that the local elastic modulus and the nonaffine displacement are spatially heterogeneous in the pre-yield regime, which results in a lower elastic modulus for polymer fibers than bulk polymer glasses. In the post-yield regime, those mechanical properties become relatively homogeneous. Monomers with large nonaffine displacement are localized mainly at the interfacial region in the pre-yield regime while highly nonaffine monomers are distributed throughout the fibers in the post-yield regime. We show that the nonaffine displacement during deformation relates closely to the mechanical response of the polymer fibers. We also find that in the strain-hardening regime there is a significant difference in the energetic contribution to the stress between polymer fibers and bulk polymers, for which the modulus of the strain-hardening regime of the polymer fibers is smaller than that of bulk polymers.

Effect of polymer-nanoparticle interaction on strain localization in polymer nanopillars

Polymer nanocomposites (PNCs), a class of composites consisting of typically inorganic nanoparticles (NPs) embedded in a polymer matrix, have become an emerging class of materials due to their significant potential to combine the functionality of NPs with the toughness of polymers. However, many applications are limited by their mechanical properties, and a fundamental understanding of NPs' effect on the nonlinear mechanical properties is lacking. In this study, we used molecular dynamics simulations to investigate the influence of NPs on the tendency of a polymer nanopillar to form a shear band. Even though we restrict ourselves to sufficiently low NP loadings that the elastic and yield behaviors are unaffected compared to the pure polymer, the polymer-NP interactions have a surprisingly strong effect on the location of a shear band in the sample. Different polymer-NP interactions have been used to explore their effect on the local structure of materials which is described using a recently developed machine-learned quantity, softness. Our calculations reveal a strong correlation between the strain localization pattern and the local structural signatures. Lastly, we show that weak interactions between NP and polymer matrix can form a soft region near the NP, and this leads to an attraction of the shear band to the NP surface.

Strain localization and failure of disordered particle rafts with tunable ductility during tensile deformation

Quasi-static tensile experiments were performed for a model disordered solid consisting of a two-dimensional raft of polydisperse floating granular particles with capillary attractions. The ductility is tuned by controlling the capillary interaction range, which varies with the particle size. During the tensile tests, after an initial period of elastic deformation, strain localization occurs and leads to the formation of a shear band at which the pillar later fails. In this process, small particles with long-ranged interactions can endure large plastic deformation without forming significant voids, while large particles with short-range interactions fail dramatically by fracturing at small deformation. Particle-level structure was measured, and the strain-localized region was found to have higher structural anisotropy than the bulk. Local interactions between anisotropic sites and particle rearrangements were the main mechanisms driving strain localization and the subsequent failure, and significant differences of such interactions exist between ductile and brittle behaviors.

Cavitation in soft matter

Cavitation is the sudden, unstable expansion of a void or bubble within a liquid or solid subjected to a negative hydrostatic stress. Cavitation rheology is a field emerging from the development of a suite of materials characterization, damage quantification, and therapeutic techniques that exploit the physical principles of cavitation. Cavitation rheology is inherently complex and broad in scope with wide-ranging applications in the biology, chemistry, materials, and mechanics communities. This perspective aims to drive collaboration among these communities and guide discussion by defining a common core of high-priority goals while highlighting emerging opportunities in the field of cavitation rheology. A brief overview of the mechanics and dynamics of cavitation in soft matter is presented. This overview is followed by a discussion of the overarching goals of cavitation rheology and an overview of common experimental techniques. The larger unmet needs and challenges of cavitation in soft matter are then presented alongside specific opportunities for researchers from different disciplines to contribute to the field.

Deep learning for automated classification and characterization of amorphous materials

It is difficult to quantify structure-property relationships and to identify structural features of complex materials. The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide a procedure to identify important structural features in materials that could be missed by standard techniques and give unique insight into how these neural networks process data.

Uniaxial Deformation and Crazing in Glassy Polymer-Grafted Nanoparticle Ultrathin Films

The deformation behavior of neat, glassy polymer-grafted nanoparticle (PGN) monolayer films is studied using coarse-grained molecular dynamics simulations and experiments on polystyrene-grafted silica. In both the simulations and experiments, apparent crazing behavior is observed during deformation. The PGN systems show a relatively more uniform, perforated sheet craze structure and significantly higher strain at break than reference homopolymers of the same length. Short chain, unentangled PGN monolayers are also simulated for comparison; these are brittle and break apart without crazing. The entangled PGN simulations are analyzed in detail for systems at both high and moderate graft density. Stress-strain curves show three distinct regions: yielding and strain localization, craze widening, and strain hardening preceding catastrophic failure. The PGN stress-strain behavior appears more similar to that of longer chain, highly entangled homopolymer films than to the reference homopolymer films of the same length as the graft chains, suggesting that the particles effectively add additional entanglement points. The moderate graft density particles have higher strain-to-failure and maximum stress than the high graft density particles. We suggest this increased robustness for lower graft density systems is due to their increased interpenetration of graft chains between neighboring particles, which leads to increased interparticle entanglements per chain.

Distinguishing failure modes in oligomeric polymer nanopillars

Brittle failure is ubiquitous in amorphous materials that are sufficiently cooled below their glass transition temperature, T_g. This catastrophic failure mode is limiting for amorphous materials in many applications, and many fundamental questions surrounding it remain poorly understood. Two challenges that prevent a more fundamental understanding of the transition between a ductile response at temperatures near T_g to brittle failure at lower temperatures are (i) a lack of computationally inexpensive molecular models that capture the failure modes observed in experiments and (ii) the lack of quantitative metrics that can distinguish various failure mechanisms. In this work, we use molecular dynamics simulations to capture ductile-to-brittle transition in glass-forming oligomeric polymer systems where we systematically vary both the temperature relative to T_g and the form of the interaction potential to induce a variety of failure modes. We characterized the effects of this new potential on macroscopic mechanical properties as well as microscopic structural and dynamical response during deformation. Finally, we develop several quantitative metrics to distinguish between different failure modes, and we find that the transition between catastrophic brittle failure, necking, and homogeneous plastic flow is gradual as the temperature is increased.