Over the last few years dense suspensions of hard particles in a simple liquid have become a model system in the soft condensed matter, granular materials, and rheology communities for the investigation of strongly non-Newtonian behavior. A key aspect underlying the recent surge of activity has been the realization that in addition to hydrodynamic interactions direct frictional contact between particles can occur. In fact, friction forces were found to be essential in order to explain some of the most striking phenomena observed in dense suspensions, such as an abrupt, essentially discontinuous onset of shear thickening, whereby the viscosity can jump up by over an order of magnitude as a critical shear rate is exceeded. So far, however, practically all theoretical models and simulations that include friction have treated it as a phenomenological parameter without considering its molecular origin. Furthermore, most models treated a situation in which shear is applied continuously, under steady-state conditions. This prevented these approaches from capturing the remarkable dynamic phenomena observed in dense suspensions, most notably the propagating jamming fronts associated with the transition from a merely shear-thickened to a solid-like jammed state. Thus, despite much recent progress, there remain fundamental questions both at the nano-scale, about the nature of the frictional interactions, and at the macro-scale, about the relation between steady-state and transient dynamic phenomena.

I will discuss recent experiments from our group that address these questions, focusing on the differences between discontinuous shear thickening (DST) and shear jamming (SJ). These experiments show how particle surface chemistry can play a central role in creating conditions that allow for SJ. We find the system’s ability to form interparticle hydrogen bonds when sheared into contact elicits SJ. We demonstrate this with charge stabilized polymer microspheres and non-spherical cornstarch particles, controlling hydrogen bond formation with solvents. The propensity for SJ is quantified by tensile tests and can be linked directly to an enhancement of the effective frictional interactions between particles, as measured by AFM and also observed by mapping out the steady-state rheology as a function of packing fraction.

I provide an overview of my research tracing several ways in which social connection between scientists, engineers and citizens shape the limits of what a population can collectively know. This includes empirical demonstrations of how centralized networks decrease the truth value of collective certainty in biomedicine, how large teams shrink the search space of science and technology, and how flocking correlates investigations and limits the size of future understanding. I then explore how the complex system of science, technology and society generates productive social disconnection to accelerate advance through maintaining crossable boundaries between disciplines, ideologies, and the ways in which recombination are valued. To explore this last point, I model scientific discovery and technological invention as involved in the complex combination of contents including problems, methods and physical entities, which bridge contexts such as journals, subfields and conferences from which scientists and inventors drawn them. We can model the normal growth of ideas in articles and inventions by representing them as complex combinations of scientific and technical contents and contexts with a high-dimensional stochastic block model, which predicts more than 95% of new patents and articles in biomedicine and physics. The inverse probability of published papers and patents under this model--unlikely combinations of contents across contexts--predicts nearly 50% of the likelihood of revolutionary success, measured by outsized citations and major awards. I discuss the implication of these findings for science policy and practice.

Motivated by classic thermodynamic experiments with dilute fluids, we explore the free and constrained expansion of fire-ant aggregations. In the latter case, we confine the ants to 2D vertical columns; hence, as the ants expand, they do work against the gravitational field. Surprisingly, we often observe the spontaneous generation of density waves; these propagate at a speed that depends on both the width and the amplitude of the wave, and occur cyclically. We also perform experiments in horizontal cells and find that the ants exhibit activity cycles, where the density homogeneity and mechanical properties of the aggregation change with activity. We believe that these cycles together with the large ant densities in our vertical columns are responsible for the generation of the observed waves. Finally, since the average ant density is larger at the bottom of the vertical column than a the top, we follow our temptation and attempt at interpreting the results in lieu of sedimentation equilibrium to seek for an equation of state. Despite our results are still highly preliminary, they provide interesting phenomenology that could perhaps be seen in active systems other than fire-ant aggregations.

We present a novel framework for the study of disclinations in two-dimensional active nematic liquid crystals, and topological defects in general. The order tensor formalism is used to calculate exact multi-particle solutions of the linearized static equations inside a planar uniformly aligned state, so that the total charge has to vanish. Topological charge conservation then requires that there is always an equal number of $q=1/2$ and $q=-1/2$ charges. Starting from a set of hydrodynamic equations, we derive a low-dimensional dynamical system for the parameters of the static solutions, which describes the motion of a half-disclination pair, or of several pairs. Within this formalism, we model defect production and annihilation, as observed in experiments. Our dynamics also provide an estimate for the critical density at which production and annihilation rates are balanced.

Several materials have been found in recent years to exhibit "hyperuniformity", their constituent particles being distributed in a disordered but correlated configurations such that density fluctuations are strongly suppressed. Examples are sheared suspensions and emulsions, and random jammed packings of particles. In those systems the hyperuniform structure appears as a static absorbing state. Suppression of density fluctuations appears also in thermodynamic and dynamic systems which sample many spatial configurations. Examples are electrolytes and suspensions of forced colloids. We relate the statistical hyperuniformity in these systems to conservation laws and long-range interactions. We demonstrate in detail the suppression of density and velocity fluctuations in forced suspensions of asymmetric objects. In this case we have identified the basic mechanism leading to hyperuniformity. We argue that for certain object shapes the same mechanism can act in the opposite direction, destabilizing the dynamics.

Carbon nanotubes reside at the boundary of macroscopic mechanics and polymer physics. They can be made into guitar-string resonators and operated in vacuum, yet they are flexible enough that thermal fluctuations completely alter their properties. Here we show that individual carbon nanotubes can be picked up and strained with micron-sized tweezers, allowing us to study these novel fluctuating strings in a variety of new ways. Experiments include recording the “sound” of the thermal vibrations of a nanotube and “seeing” its real-time motion with light. We find many surprising results, including strong coupling between vibrational modes and very long intrinsic dissipation times, as well as many unresolved mysteries. These results open the door to the science of thermally-fluctuating strings in the underdamped limit, with many opportunities for theory, computation, and experiment.

Topologically nontrivial fields and vortices frequently arise in classical and quantum field theories, plasmas, optics, cosmology, hard condensed matter and atomic systems. On the other hand, soft matter systems, such as colloids and liquid crystals, offer the complexity in degrees of freedom and symmetries that allow for probing topologically analogous phenomena on experimentally accessible scales. In my lecture, I will discuss how surfaces of colloidal knots and handlebodies interact with the liquid crystalline molecular alignment fields and how topological knot solitons can emerge as static field configurations within the chiral colloidal ferromagnets [1-5]. I will show how such synergistic combinations of topology and self-assembly paradigms can emerge as an exciting scientific frontier of topological soft matter.

[1] A. Martinez, L. Hermosillo, M. Tasinkevych and I.I. Smalyukh. Proc. Natl. Acad. Sci. U.S.A. 112, 4546-4551 (2015). [2] A. Martinez, M. Ravnik, B. Lucero, R. Visvanathan, S. Žumer and I. I. Smalyukh. Nature Materials 13, 258–263 (2014).[3] B. Senyuk, Q. Liu, S. He, R. D. Kamien, R. B. Kusner, T. C. Lubensky and I. I. Smalyukh. Nature 493, 200-205 (2013). [4] P. J. Ackerman and I. I. Smalyukh. Nature Mater 16, 426-432 (2017). [5] J.-S.B. Tai, P.J. Ackerman and I.I. Smalyukh. PNAS. doi:10.1073/pnas.1716887115 (2018).

Does a rotating bead always spin in place? Not if that bead is near a surface: rolling leads to translational motion, as well as very strong flows around the bead, even quite far away. These flows strongly couple the motion of nearby microrollers (rotating beads), which leads to a rich variety of collective effects. Using experiments in tandem with large-scale 3D simulations, we have shown that driving a compact group of microrollers leads to a new kind of flow instability, whose wavelength is controlled not by the driving torque or the fluid viscosity, but a geometric parameter: the microroller's distance above the container floor. Furthermore, under the right conditions, stable, compact clusters we term "critters" can emerge from the unstable interface. Our simulations and experiments suggest that these critters are a stable state of the system, move much faster than individual rollers, and quickly respond to a changing drive. We believe that critters are unique in that they are clusters which form only with hydrodynamic interactions; no interparticle potentials are needed to create these structures. Furthermore, as compact, self-assembled structures which can easily be remotely guided, critters may offer a promising tool for microscopic transport.

Entropy and free-energy estimation are key in thermodynamic characterization of simulated systems ranging from spin models through polymers, colloids, protein structure, and drug-design. Current techniques suffer from being model specific, requiring abundant computation resources and simulation at conditions far from the studied realization. In this talk, I will present a novel universal scheme to calculate entropy using lossless compression algorithms and validate it on simulated systems of increasing complexity. Our results show accurate entropy values compared to benchmark calculations while being computationally effective. In molecular-dynamics simulations of protein folding, we exhibit unmatched detection capability of the folded states by measuring previously undetectable entropy fluctuations along the simulation timeline. Such entropy evaluation opens a new window onto the dynamics of complex systems and allows efficient free-energy calculations.

Granular materials are common in everyday life but are historically difficult to model. This has direct real-world ramifications owing to the prominent role granular media play in multiple industries and in terrain dynamics. One can attempt to track every grain with discrete particle methods, but realistic systems are often too large for this approach and a continuum model is desired. However, granular media display unusual behaviors that complicate the continuum treatment: they can behave like solid, flow like liquid, or separate into a “gas”, and the rheology of the flowing state displays remarkable subtleties.

To address these challenges, in this talk we develop a family of continuum models and numerical solvers, which permit quantitative modeling capabilities for general problems and certain reduced-order approaches for problems of intrusion, impact, driving, and locomotion in grains. To calculate flows in general cases, a rather significant nonlocal effect is evident, which is well-described with our recent nonlocal model accounting for grain cooperativity within the rheology. On the other hand, to model just intrusion forces on submerged objects, we will show, and explain why, many of the experimentally observed results can be captured from a much simpler tension-free frictional plasticity model. This approach gives way to some surprisingly simple general tools, including the granular Resistive Force Theory, and a broad set of scaling laws inherent to the problem of granular locomotion. These scalings are validated experimentally and in discrete particle simulations suggesting a new down-scaled paradigm for granular locomotive design, on earth and beyond, to be used much like scaling laws in fluid mechanics. We close with ongoing efforts expanding into wet granular flows, multi-scale approaches, and self-optimizing wheels for off-road traction.

Science is filled with toy models: abstractions of complicated systems that ignore microscopic details even when they are known. For a special class of models in physics, the renormalization group rigorously justifies the use of effective theories containing just a small number of relevant parameters. This philosophy seems to apply more broadly, even when the renormalization group cannot be used. But why? In this talk I will discuss an information theory approach to answering this question, or at least towards quantifying it. I will first review that typical models are sloppy, defined by a hierarchy in parameter importance. I will argue that sloppiness is both necessary and sufficient for a microscopic system to be amenable to description by a simpler effective theory. I will then show how renormalizable models become sloppy as their data is coarse-grained. Finally I will discuss our recent efforts to use the structure of these models to choose simpler effective theories automatically.

Many fundamental phenomena in eukaryotic cells -- nuclear migration, spindle positioning, chromosome segregation -- involve the interaction of often transitory structures with boundaries and fluids. I will discuss the interaction of theory and simulation with experimental measurements of active processes within the cell. This includes understanding the force transduction mechanisms underlying nuclear migration, spindle positioning and oscillations, as well as how active displacement domains of chromatin might be forming in the interphase nucleus.

Nature provides amazing examples of high velocity, high acceleration, impulsive movements that can be repeated numerous times over the course of an organism’s lifetime. Synthetic, or engineered, devices, on the other hand, are often challenged to achieve comparable performance across a wide range of size scales. Common to nature’s examples, including mantis shrimp and trap-jaw ants, is the integration of three essential components for elasticity-assisted movement: an actuator, spring, and latch. Elasticity-assisted motion has been utilized for thousands of years to amplify the power of natural or synthetic actuators; however, the scaling physics of these multi-component systems, especially in light of materials design, have not been widely considered. Here, we discuss our group’s efforts, within a multi-university collaborative team, to lay a foundation for understanding the role that materials properties and structure play in the performance of impulsive systems in nature with an eye toward aiding the development of engineered devices that can overcome current limitations. We first discuss the mechanics of elastic recoil and a set of systematic experiments on a resilin-like synthetic material. The results from this study leads to a common framework for describing the roles of geometry and materials properties for controlling duration, velocity, and acceleration. We then introduce recent advances of using mesoscale polymers, which build upon previously introduced concepts from our group, to develop high rate, large strain, microscale actuators. Collectively, these examples highlight the integrative approach of our group and how we use bio-inspired materials mechanics to inspire new technologies and provide fundamental insight.

Ordered sequences of motions govern the morphological transitions of a wide variety of natural and man-made systems, while the ability to interpret time-ordered signals underlies future smart materials that can be (re)programmed and process information. After a short introduction to mechanical metamaterials, we introduce here two novel classes of mechanical metamaterials, that can (1) exhibit sequential output and (2) are sensitive to sequential input. To obtain metamaterials that translate a global uniform compression into a precise multistep pathway of reconfigurations, we combine strongly nonlinear mechanical elements with a multimodal hierarchical structure, and demonstrate multi-step reconfigurations of digitally manufactured metamaterials. To obtain metamaterials that are sensitive to a sequence of mechanical inputs, we introduce the notion of non-commuting metamaterials. Our work aims to establish generic principles for infusing metamaterials with sequential input and output.

Biologists have recently come to appreciate that eukaryotic cells are home to a multiplicity of non-membrane bound compartments, many of which form and dissolve as needed for the cell to function. These dynamical "condensates" enable many central cellular functions – from ribosome assembly, to RNA regulation and storage, to signaling and metabolism. While it is clear that these compartments represent a type of separated phase, what controls their formation, how specific biological components are included or excluded, and how these structures influence physiological and biochemical processes remain largely mysterious. I will discuss recent experiments on phase separated condensates both in vitro and in vivo, and will present theoretical results that highlight a novel “magic number” effect relevant to the formation and control of two-component phase separated condensates.

Tornadoes are among nature's most destructive forces. The most violent, long-lived tornadoes form within supercell thunderstorms. In this seminar, results from simulations of tornado-producing supercell thunderstorms at ultra-high resolution will be presented, as well as the technical challenges involved in conducting, analyzing and visualizing such simulations.

In a control simulation, tornado formation occurs in concert with processes not clearly seen in previous supercell simulations. Visualizations of model fields presented at very high temporal resolution (up to 1/6 of a second, the model time step) will be presented. These animations reveal a fascinating combination of processes that lead to the formation of a long lived very violent tornado that persists for well over an hour. In order to facilitate the saving and visualizing of large amounts of model data, a file system was developed that utilizes the HDF file format and ZFP lossy file compression, and this file system and associated middleware, dubbed LOFS, will be also described.

Cells of all domains of life must coordinate their cell cycle meticulously in order for protein levels, cell size and DNA replication to be appropriately regulated and to be able to prevent stochastic fluctuations from accumulating over time. These control mechanisms will lead to correlations in various cellular traits across the lineage tree (notably, size and generation times). I will present a recent model we developed for understanding cellular homeostasis and characterizing these correlations and fluctuations. I will discuss the implications of these correlated fluctuations on the population growth. In contrast to the dogma, we find that variability may be detrimental to the population growth, suggesting that evolution would tend to suppress it.

Inferring the structure and dynamical interactions of complex systems is critical to understanding and controlling their behavior. Hybrid systems are challenging to identify because the parameters and equation structure may change across multiple dynamical regimes. Key examples include varying transmission rates in epidemiological problems and legged locomotion. Many current methods focus on inferring a model for the system and detecting switching points in a time-centric framework. One can reframe the problem by clustering in data-driven coordinates, such that similar dynamical behavior is close together, and then use the sparse identification of nonlinear dynamics (SINDy) method to identify different dynamical regimes. I will discuss model selection using SINDy and information criteria. I will then demonstrate the success of the method hybrid-SINDy on a spring-mass model and a simple infectious disease model with time-dependent transmission rates. I will also investigate robustness to noise and cluster-size.

The CRISPR (clustered regularly interspaced short palindromic repeats) mechanism allows bacteria to defend adaptively against phages and other invading genomic material. The CRISPR machinery acquires short genomic sequences from the "invaders" and in this way builds up a memory of past infections. With a new encounter of an invading sequence, this memory is accessed, and in a successful outcome, the invader is neutralized. I will introduce a population dynamics model where immunity can be both acquired and lost. I will describe the predictions of this model and suggest experiments.

Adaptation, where a population evolves increasing fitness in a fixed environment is often thought of as a hill climbing process on a fitness landscape. With a fi nite genome, such a process eventually leads the population to a fitness peak, at which point fitness can no longer increase through individual beneficial mutations. Instead, the ruggedness of typical landscapes due to epistasis between genes or DNA sites suggests that the accumulation of multiple mutations can allow the population to continue increasing in fitness. By using a spin-glass type model for the fitness function that takes into account microscopic epistasis, we find that hopping between metastable states can mechanistically and robustly give rise to a slow, logarithmic average fitness trajectory.

Quantum many-body systems exhibit very rich phenomena unexpected from their classical counterparts. In this talk, I will focus on a quantum information theoretical operation -- partial transpose -- which is useful in detecting quantum entanglement. I will describe how partial transpose can be used to detect topology and entanglement in quantum many-body systems, ranging from topological phases of condensed matter to systems which have holographic dual descriptions. In particular, I will describe the constructions of topological invariants using partial transpose, and possible holographic dual objects corresponding to entanglement negativity, which is an quantum entanglement measure constructed by using partial transpose.

Data-driven modeling and machine learning have opened new paradigms and opportunities in the understanding and design of soft and biological materials. Colloidal particles with tunable anisotropic surface interactions are of technological interest in fabricating responsive actuators, biomimetic encapsulants, and photonic crystals with omnidirectional band gaps. In the first part of this talk, I will describe our applications of nonlinear manifold learning to determine low-dimensional assembly landscapes for self-assembling patchy colloids. These landscapes connect colloid architecture and prevailing conditions with emergent assembly behavior, and enable inverse building block design by rational sculpting of the landscape to engineer the stability and accessibility of desired aggregates. Rational engineering of structural and functional polymers and proteins requires an understanding of the underlying free energy landscapes dictating thermodynamic stability and kinetic folding pathways. In the second part of this talk, I will describe an approach integrating ideas from dynamical systems theory and nonlinear manifold learning to reconstruct multidimensional protein folding funnels from the time evolution of single experimentally-measurable observables.

Understanding the self-organization principles and collective dynamics of non-equilibrium matter remains a major challenge despite considerable progress over the last decade. In this talk, I will introduce a hydrodynamic analog system that allows us to investigate simultaneously the wave-mediated self-propulsion and interactions of effective spin degrees of freedom. Millimetric liquid droplets can walk across the surface of a vibrating fluid bath, self-propelled through a resonant interaction with their own guiding wave fields. A walking droplet, or ‘walker', may be trapped by a submerged circular well at the bottom of the fluid bath, leading to a clockwise or counter-clockwise angular motion centered at the well. When a collection of such wells is arranged in a 1D or 2D lattice geometry, a thin fluid layer between wells enables wave-mediated interactions between neighboring walkers. Through experiments and mathematical modeling, we demonstrate the spontaneous emergence of coherent droplet rotation dynamics for different types of lattices. For sufficiently strong pair-coupling, wave interactions between neighboring droplets may induce local spin flips leading to ferromagnetic or antiferromagnetic order. Transitions between these two forms of order can be controlled by tuning the lattice parameters. More generally, our results reveal a number of surprising parallels between the collective spin dynamics of wave-driven droplets and known phases of classical condensed matter systems. This suggests that our hydrodynamic analog system can be used to explore universal aspects of active matter and wave-mediated particle interactions, including spin-wave propagation and topologically protected dynamics far from equilibrium.

Exact many-particle quantum mechanics has a prohibitive cost that grows exponentially with the size of the system. Most modern quantum chemistry is built on approximations that result in more tractable algorithms with polynomial scaling, but which qualitatively fail for many important problems. I will describe an alternative approach that uses stochastic techniques to circumvent this prohibitive cost (i.e. a flavor of quantum Monte Carlo). In particular, this approach is based on a very general and recently-developed framework for stochastic linear algebra called "fast randomized iteration", due to Lim and Weare. I will describe the FRI algorithm, its application to challenging problems in quantum chemistry, and its advantages over similar techniques.

Topology sheds new light on the emergence of unidirectional edge waves in a variety of physical systems, from condensed matter to artificial lattices. Waves observed in geophysical flows are also robust to perturbations, which suggests a role for topology. We show a topological origin for two celebrated equatorially trapped waves known as Kelvin and Yanai modes, due to the Earth's rotation that breaks time-reversal symmetry. The non-trivial structure of the bulk Poincaré wave modes encoded through the first Chern number of value 2 guarantees existence for these waves. The invariant demonstrates that ocean and atmospheric waves share fundamental properties with topological insulators, and that topology plays an unexpected role in the Earth climate system.

The basic units of biological structure and function are cells, which exhibit wide variation in regard to both type and state. We assess such variation by simultaneously profiling the transcriptomes of thousands of single mammalian cells (Drop-seq) or nuclei (DroNc-seq), using high-throughput emulsion microfluidics and DNA barcodes. These are accomplished by (a) encapsulating and lysing one cell/nuclei per emulsion droplet, and (b) barcoding RNA contents from each cell/nuclei using unique DNA-barcoded micro-beads, (c) performing Next-Gen Sequencing.

We are using these droplet-based techniques to profile cell types comprising complex tissues in a variety of tissue-types such as the heart and solid tumors in mouse models and human primary tissue. Besides, we are using Drop-seq and DroNc-seq to profile cell-states, particularly cellular heterogeneity in development and differentiation processes using a combination of cell lines, mouse embryonic tissue, in vitro culture, and human induced pluripotent stem cells.

We also develop custom microfluidic devices to study phenotypic responses of cells to different environmental stimuli including physical, bio-chemical and pathogenic stimuli; I will provide some examples to illustrate some applications.

Graphics, images and figures — visual representations of scientific data and concepts — are critical components of science and engineering research. They communicate in ways that words cannot. They can clarify or strengthen an argument and spur interest into the research process.

But it is important to remember that a visual representation of a scientific concept or data is a re-presentation and not the thing itself –– some interpretation or translation is always involved. Just as writing a journal article, one must carefully plan what to “say,” and in what order to “say it.” The process of making a visual representation requires you to clarify your thinking and improve your ability to communicate with others.

In this talk, I will show my own approach to creating depictions in science and engineering—the successes and failures. Included will be a discussion about how far can we go when “enhancing” science images. I will be showing pages and concepts from my upcoming book: Picturing Science and Engineering.

One of the enduring mysteries of biology is how organs know to stop growing at the correct size and how those sizes are coordinated so that the animal retains the correct proportions. Here, we discuss several studies that in different ways address the precision with which organ size can be controlled. We first show that there are severe limits to the coordination of the sizes of left and right organs (like the left and right wings of a fruit fly) by chemical signals, suggesting that organ size is set primarily autonomously. We then consider the noisy dynamics of the growth of individuals tissues in the presence of various feedback laws. We find that only certain forms of mechanical feedback can specify a unique organ size. We also show that, even in the simplest, homogeneous case, stochastic growth of an elastic tissue has unexpectedly rich behavior: For example, it exhibits power law correlation functions, reminiscent of those seen in cosmological models, and soft modes that allow for diffusive growth of labelled clones of cells.

Nanoelectromechanical systems (NEMS) that operate in the megahertz (MHz) regime allow energy transducibility between different physical domains. For example, they convert optical or electrical signals into mechanical motions and vice versa. This coupling of different physical quantities leads to frequency-tunable NEMS resonators via electromechanical non-linearities. In this talk, I will describe one- and two-dimensional, non-linear, nanoelectromechanical lattices (NEML) with active control of the frequency band dispersion in the radio-frequency domain (10–30 MHz). Our NEMLs consist of a periodic arrangement of mechanically coupled, free-standing nanomembranes with circular clamped boundaries. This design forms a flexural phononic crystal with a wide and well-defined bandgap. The application of a d.c. gate voltage creates voltage-dependent on-site potentials, which can significantly shift the frequency bands of the device. Additionally, I will discuss the experimental realization of topological nanoelectromechanical metamaterials with protected edge states. These on-chip integrated acoustic components could be used in unidirectional waveguides and compact delay lines for high-frequency signal-processing applications.