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Motile liquid crystal (LC) colloids show peculiar behavior due to the high sensitivity to external stimuli driven by the LC elastic and surface effects. However, few studies focus on harnessing the LC phase transitions to propel colloidal inclusions by the nematic-isotropic (NI) interface. We engineer a quasi-2D active system consisting of solid micron-sized light-absorbent platelets immersed in a thermotropic nematic LC. The platelets self-propel in the presence of light while self-inducing a localized NI phase transition. The sample's temperature, light intensity, and confinement determine three different regimes: a 2D large regime where the platelet-isotropic phase bubble is static and the NI interface remains stable; a compact motile-2D regime where the NI interface lies closer to the platelet's contour; and a motile-3D-confinement regime characterized by the emergence of multipolar configurations of the LC. We perform continuum-theory simulations that predict stationary platelet-LC states when confined in 3D. Our study in an intrinsically far-from-equilibrium landscape is crucial for designing simple synthetic systems that contribute to our understanding of harnessing liquid crystals' phase transitions to propel colloidal inclusions and trigger tunable topological reconfigurations leading to photonic responses., Comment: 29 pages main text. 5 figures main text. Supporting Information included at the end

Machine-learned interatomic potentials (MILPs) are rapidly gaining interest for molecular modeling, as they provide a balance between quantum-mechanical level descriptions of atomic interactions and reasonable computational efficiency. However, questions remain regarding the stability of simulations using these potentials, as well as the extent to which the learned potential energy function can be extrapolated safely. Past studies have reported challenges encountered when MILPs are applied to classical benchmark systems. In this work, we show that some of these challenges are related to the characteristics of the training datasets, particularly the inclusion of rigid constraints. We demonstrate that long stability in simulations with MILPs can be achieved by generating unconstrained datasets using unbiased classical simulations if the fast modes are correctly sampled. Additionally, we emphasize that in order to achieve precise energy predictions, it is important to resort to enhanced sampling techniques for dataset generation, and we demonstrate that safe extrapolation of MILPs depends on judicious choices related to the system's underlying free energy landscape and the symmetry features embedded within the machine learning models.

The thermodynamics and transport properties of polymeric materials are essential for the design of reactors and for the development of polymer deconstruction processes. Existing property prediction tools such as correlations based on entropy scaling, kinetic gas theory, and free-volume model are inadequate for polymers. In this paper, we introduce a data-driven model for polyolefins based on data from molecular dynamics simulations that can accurately predict the transport properties of polyethylenes and their binary mixtures with higher n-alkanes across a range of temperatures, pressures, concentrations, and oligomer molecular weights.

There is considerable interest in designing new polymeric materials with built-in mechanisms for recycling. In this work, we present a systematic exploration of several polyethylene-based polymers that contain strategically incorporated cleavable bonds that are susceptible to chemical degradation. We consider ten distinct telechelic functionalities across varying chain lengths: ester, aromatic ester, anhydride, carbonate, urethane, siloxane, oxalate, acetal, urea, and amide linkages. Our study is focused on the effects of cleavable bonds on density, diffusion, enthalpy, and melting temperatures. We also examine the crystallization process. These are key properties for polymer processing and, by comparing them to those of linear polyethylene, we establish a platform that will help guide future synthetic efforts towards the design of sustainable plastics with performance comparable to that of traditional, high-performance polyolefin-based materials.

Active nematics are materials composed of mobile, elongated particles that can transform energy from the environment into a mechanical motion. Current experimental realizations of the active nematics are of biological origin and include cell layers, suspensions of elongated bacteria in liquid crystal, and combinations of bio-filaments with molecular motors. Here, we report the realization of a fully synthetic active nematic system comprised of a lyotropic chromonic liquid crystal energized by ultrasonic waves. This artificial active liquid crystal is free from biological degradation and variability, exhibits stable material properties, and enables precise and rapid activity control over a significantly extended range. We demonstrate that the energy of the acoustic field is converted into microscopic extensile stresses disrupting long-range nematic order and giving rise to an undulation instability, development of active turbulence, and proliferation of topological defects. We reveal the emergence of unconventional free-standing persistent vortices in the nematic director field at high activity levels. The results provide a foundation for the design of externally energized active nematic fluids with stable material properties and tunable topological defects dynamics crucial for the realization of reconfigurable microfluidic systems.

The development of synthetic microswimmers has advanced our understanding of the fundamental self-propelled mechanisms of living systems. However, there are scarce studies at the microscale within highly structured anisotropic media, such as bacteria or cellular receptors that swim in concentrated solutions of filamentous proteins or lipids with viscoelastic properties. Synthetic liquid crystals (LCs) have the potential to serve as biomimetic surrogates to study the structure and dynamics of living systems. Nevertheless, studies on thermotropic LCs have mainly focused on electro and magneto-phoretic effects, with a few others on diffusiophoresis or light-driven distortions of the LC nematic director. To the best of our knowledge, here we report self-thermophoretic experiments on thermotropic LCs for the first time. Our system consists of 2D confined Janus particles in 5CB with homeotropic anchoring on the particle and LC cell surfaces. The Janus particles include a conductive titanium coating that, upon exposure to an LED source, is heated and induces a local steady nematic-isotropic phase transition, leading to the self-propulsion of the particles orthogonally to the LC director. The trajectories of the Janus particles were tracked at different intensities of the applied light. A model is developed to describe the mean-squared displacement of a Janus particle suspended in a nematic LC. The model assumes that the Janus particle feels the LC as a continuum with the anisotropic viscosity of the bulk nematic phase. Moreover, the viscoelasticity of the LC is also considered. The model describes the experimental data well, and the fitting parameter related to the magnitude of the swimming force increases with the intensity of the applied light. Moreover, our approach represents an important step for developing a platform for highly structured anisotropic active materials., Comment: 15 pages, 6 figures

We study the rheology of bidisperse non-Brownian suspensions using particle-based simulation, mapping the viscosity as a function of the size ratio of the species, their relative abundance, and the overall solid content. The variation of the viscosity with applied stress exhibits shear thickening phenomenology irrespective of composition, though the stress-dependent limiting solids fraction governing the viscosity and its divergence point are non-monotonic in the mixing ratio. Contact force data demonstrate an asymmetric exchange in dominant stress contribution from large-large to small-small particle contacts as the mixing ratio of the species evolves. Combining a prior model for shear thickening with one for composition-dependent jamming, we obtain a full description of the rheology of bidisperse non-Brownian suspensions capable of predicting effects such as the viscosity reduction observed upon adding small particle fines to a suspension of large particles., Comment: 5 pages, 4 figures

A machine learning approach is presented to accelerate the computation of block polymer morphology evolution for large domains over long timescales. The strategy exploits the separation of characteristic times between coarse-grained particle evolution on the monomer scale and slow morphological evolution over mesoscopic scales. In contrast to empirical continuum models, the proposed approach learns stochastically driven defect annihilation processes directly from particle-based simulations. A UNet architecture that respects different boundary conditions is adopted, thereby allowing periodic and fixed substrate boundary conditions of arbitrary shape. Physical concepts are also introduced via the loss function and symmetries are incorporated via data augmentation. The model is validated using three different use cases. Explainable artificial intelligence methods are applied to visualize the morphology evolution over time. This approach enables the generation of large system sizes and long trajectories to investigate defect densities and their evolution under different types of confinement. As an application, we demonstrate the importance of accessing late-stage morphologies for understanding particle diffusion inside a single block. This work has implications for directed self-assembly and materials design in micro-electronics, battery materials, and membranes., Comment: 51 page, 11 Figures and 5 figures in the SI

When viewed with a cross-polarized optical microscope (POM), liquid crystals display interference colors and complex patterns that depend on the material's microscopic orientation. That orientation can be manipulated by application of external fields, which provides the basis for applications in optical display and sensing technologies. The color patterns themselves have a high information content. Traditionally, however, calculations of the optical appearance of liquid crystals have been performed by assuming that a single-wavelength light source is employed, and reported in a monochromatic scale. In this work, the original Jones matrix method is extended to calculate the colored images that arise when a liquid crystal is exposed to a multi-wavelength source. By accounting for the material properties, the visible light spectrum and the CIE color matching functions, we demonstrate that the proposed approach produces colored POM images that are in quantitative agreement with experimental data. Results are presented for a variety of systems, including radial, bipolar, and cholesteric droplets, where results of simulations are compared to experimental microscopy images. The effects of droplet size, topological defect structure, and droplet orientation are examined systematically. The technique introduced here generates images that can be directly compared to experiments, thereby facilitating machine learning efforts aimed at interpreting LC microscopy images, and paving the way for the inverse design of materials capable of producing specific internal microstructures in response to external stimuli., Comment: 12 pages, 5 figures (main text). 6 pages, 6 figures (appendices)

Electrostatic interactions in polymeric systems are responsible for a wide range of liquid-liquid phase transitions that are of importance for biology and materials science. Such transitions are referred to as complex coacervation, and recent studies have sought to understand the underlying physics and chemistry. Most theoretical and simulation efforts to date have focused on oppositely charged linear polyelectrolytes, which adopt nearly ideal-coil conformations in the condensed phase. However, when one of the coacervate components is a globular protein, a better model of complexation should replace one of the species with a spherical charged particle or colloid. In this work, we perform coarse-grained simulations of colloid-polyelectrolyte coacervation using a spherical model for the colloid. Simulation results indicate that the electroneutral cell of the resulting (hybrid) coacervates consists of a polyelectrolyte layer adsorbed on the colloid. Power laws for the structure and the density of the condensed phase, which are extracted from simulations, are found to be consistent with the adsorption-based scaling theory of coacervation. The coacervates remain amorphous (disordered) at a moderate colloid charge, $Q$, while an intra-coacervate colloidal crystal is formed above a certain threshold, at $Q > Q^{*}$. In the disordered coacervate, if $Q$ is sufficiently low, colloids diffuse as neutral non-sticky nanoparticles in the semidilute polymer solution. For higher $Q$, adsorption is strong and colloids become effectively sticky. Our findings are relevant for the coacervation of polyelectrolytes with proteins, spherical micelles of ionic surfactants, and solid organic or inorganic nanoparticles.

Polyelectrolyte complexation plays an important role in materials science and biology. The internal structure of the resultant polyelectrolyte complex (PEC) phase dictates properties such as physical state, response to external stimuli, and dynamics. Small-angle scattering experiments with X-rays and neutrons have revealed structural similarities between PECs and semidilute solutions of neutral polymers, where the total scattering function exhibits an Ornstein-Zernike form. In spite of consensus among different theoretical predictions, the existence of positional correlations between polyanion and polycation charges has not been confirmed experimentally. Here, we present small-angle neutron scattering profiles where the polycation scattering length density is matched to that of the solvent to extract positional correlations among anionic monomers. The polyanion scattering functions exhibit a peak at the inverse polymer screening radius of Coulomb interactions, $q^{*} \approx 0.2 {\AA}^{-1}$. This peak, attributed to Coulomb repulsions between the fragments of polyanions and their attractions to polycations, is even more pronounced in the calculated charge scattering function that quantifies positional correlations of all polymer charges within the PEC. Screening of electrostatic interactions by adding salt leads to the gradual disappearance of this correlation peak, and the scattering functions regain an Ornstein-Zernike form. Experimental scattering results are consistent with those calculated from the random phase approximation, a scaling analysis, and molecular simulations.

The optical properties of liquid crystals serve as the basis for display, diagnostic, and sensing technologies. Such properties are generally controlled by relying on electric fields. In this work, we investigate the effects of microfluidic flows and acoustic fields on the molecular orientation and the corresponding optical response of nematic liquid crystals. Several previously unknown structures are identified, which are rationalized in terms of a state diagram as a function of the strengths of the flow and the acoustic field. The new structures are interpreted by relying on calculations with a free energy functional expressed in terms of the tensorial order parameter, using continuum theory simulations in the Landau-de Gennes framework. Taken together, the findings presented here offer promise for the development of new systems based on combinations of sound, flow, and confinement.

From flocks of birds to biomolecular assemblies, systems in which many individual components independently consume energy to perform mechanical work exhibit a wide array of striking behaviors. Methods to quantify the dynamics of these so called active systems generally aim to extract important length or time scales from experimental fields. Because such methods focus on extracting scalar values, they do not wring maximal information from experimental data. We introduce a method to overcome these limitations. We extend the framework of correlation functions by taking into account the internal headings of displacement fields. The functions we construct represent the material response to specific types of active perturbation within the system. Utilizing these response functions we query the material response of disparate active systems composed of actin filaments and myosin motors, from model fluids to living cells. We show we can extract critical length scales from the turbulent flows of an active nematic, anticipate contractility in an active gel, distinguish viscous from viscoelastic dissipation, and even differentiate modes of contractility in living cells. These examples underscore the vast utility of this method which measures response functions from experimental observations of complex active systems.

Cells are fundamental building blocks of living organisms displaying an array of shapes, morphologies, and textures that encode specific functions and physical behaviors. Elucidating the rules of this code remains a challenge. In this work, we create biomimetic structural building blocks by coating ellipsoidal droplets of a smectic liquid crystal with a protein-based active cytoskeletal gel, thus obtaining core-shell structures. By exploiting the patterned texture and anisotropic shape of the smectic core, we were able to mold the complex nematodynamics of the interfacial active material and identify new time-dependent states where topological defects periodically oscillate between rotational and translational regimes. Our nemato-hydrodynamic simulations of active nematics demonstrate that, beyond topology and activity, the dynamics of the active material are profoundly influenced by the local curvature and smectic texture of the droplet, as well as by external hydrodynamic forces. These results illustrate how the incorporation of these constraints into active nematic shells orchestrates remarkable spatio-temporal motifs, offering critical new insights into biological processes and providing compelling prospects for designing bio-inspired micro-machines., Comment: 16 pages, 8 figures

Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system's configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this new tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes.

Solitons in nematic liquid crystals offer intriguing opportunities for transport and sensing in microfluidic systems. Little is known about the elementary conditions that are needed to create solitons in nematic materials. In this work, theory, simulations and experiments are used to study the generation and propagation of solitary waves (or "solitons") in nematic liquid crystals upon the application of an alternating current (AC) electric field. We find that these solitary waves exhibit "butterfly"-like or "bullet"-like structures that travel in the direction perpendicular to the applied electric field. Such structures propagate over long distances without losing their initial shape. The theoretical model adopted here serves to identify some of the key requirements that are needed to generate solitons in the absence of electrostatic interactions. These include surface imperfections that introduce a twist in the director, unequal elastic constants, and negative anisotropic dielectric permittivity. The results of simulations are shown to be in good agreement with our own experimental observations, serving to establish the validity of the theoretical concepts advanced in this work.

Solitons in liquid crystals have generated considerable interest. Several hypotheses of varying complexity have been advanced to explain how they emerge, and a consensus has not emerged yet about the underlying forces responsible for their formation or their structure. In this work, we present a minimal model for soliton structures in achiral nematic liquid crystals, which reveals the key requirements needed to generate traveling solitons in the absence of added charges. These include a surface imperfection or inhomogeneity capable of producing a twist, flexoelectricity, dielectric contrast, and an applied AC electric field that can couple to the director's orientation. Our proposed model is based on a tensorial representation of a confined liquid crystal, and it predicts the formation of "butterfly" structures, quadrupolar in character, in regions of a slit channel where the director is twisted by the surface imperfection. As the applied electric field is increased, solitons (or "bullets") become detached from the wings of the butterfly, which then rapidly propagate throughout the system. The main observations that emerge from the model, including the formation and structure of butterflies, bullets, and stripes, as well as the role of surface imperfections and the strength of the applied field, are consistent with our own experimental findings presented here for nematic LCs confined between two chemically treated parallel plates.

The properties of soft electronic materials depend on the coupling of electronic and conformational degrees of freedom over a wide range of spatiotemporal scales. Description of such properties requires multiscale approaches capable of, at the same time, accessing electronic properties and sampling the conformational space of soft materials. This could in principle be realized by connecting the coarse-grained (CG) methodologies required for adequate conformational sampling to conformationally-averaged electronic property distributions via backmapping to atomistic-resolution level models and repeated quantum-chemical calculations. Computational demands of such approaches, however, have hindered their application in high-throughput computer-aided soft materials discovery. Here, we present a method that, combining machine learning and CG techniques, can replace traditional backmapping-based approaches without sacrificing accuracy. We illustrate the method for an emerging class of soft electronic materials, namely non-conjugated, radical-containing polymers, promising materials for all-organic energy storage. Supervised machine learning models are trained to learn the dependence of electronic properties on polymer conformation at CG resolutions. We then parametrize CG models that retain electronic structure information, simulate CG condensed phases, and predict the electronic properties of such phases solely from the CG degrees of freedom. We validate our method by comparing it against a full backmapping-based approach, and find good agreement between both methods. This work demonstrates the potential of the proposed method to accelerate multiscale workflows, and provides a framework for the development of CG models that retain electronic structure information.

Bipolar nanochannels comprising two domains of positively and negatively charged walls along the pore axis are known to rectify current when exposed to an electric potential bias. We find that addition of charged nanoparticles can increase rectification considerably, by approximately one order of magnitude. Two bipolar channel geometries are considered here; their behavior is examined at rest and under the influence of a negative bias and a positive bias, respectively. We do so by relying on a molecular-level model of the electrolyte solution in the channels. The large increase in current rectification can be explained by the inherent electric field that charged nanoparticles generate within the channel. This effect is found to be largely dependent on the pore's geometry, its charge distribution, and the sign of the nanoparticles' charge, thereby offering new opportunities for design of engineered nanopore membrane-nanoparticle systems for energy storage., Comment: 30 pages, 4 figures

Advances in manufacturing and characterization of complex molecular systems have created a need for new methods for design at molecular length scales. Emerging approaches are increasingly relying on the use of Artificial Intelligence (AI), and the training of AI models on large data libraries. This paradigm shift has led to successful applications, but shortcomings related to interpretability and generalizability continue to pose challenges. Here, we explore an alternative paradigm in which AI is combined with physics-based considerations for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning (ML) model trained on data gathered from a single system. Through the ML-constructed collective variables, it becomes possible to identify critical molecular interactions in the system of interest, the modulation of which enables a systematic tailoring of the system's free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation, and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity, and points to its potential for design of complex molecular systems.

Active materials are those in which individual, uncoordinated local stresses drive the material out of equilibrium on a global scale. Examples of such assemblies can be seen across scales from schools of fish to the cellular cytoskeleton and underpin many important biological processes. Synthetic experiments that recapitulate the essential features of such active systems have been the object of study for decades as their simple rules allow us to elucidate the physical underpinnings of collective motion. One system of particular interest has been active nematic liquid crystals (LCs). Because of their well understood passive physics, LCs provide a rich platform to interrogate the effects of active stress. The flows and steady state structures that emerge in an active LCs have been understood to result from a competition between nematic elasticity and the local activity. However most investigations of such phenomena consider only the magnitude of the elastic resistance and not its peculiarities. Here we investigate a nematic liquid crystal and selectively change the ratio of the material's splay and bend elasticities. We show that increases in the nematic's bend elasticity specifically drives the material into an exotic steady state where elongated regions of acute bend distortion or "elasticity bands" dominate the structure and dynamics. We show that these bands strongly influence defect dynamics, including the rapid motion or "catapulting" along the disintegration of one of these bands thus converting bend distortion into defect transport. Thus, we report a novel dynamical state resultant from the competition between nematic elasticity and active stress.

A family of fast sampling methods is introduced here for molecular simulations of systems having rugged free energy landscapes. The methods represent a generalization of a strategy consisting of adjusting a model for the free energy as a function of one- or more collective variables as a simulation proceeds. Such a model is gradually built as a system evolves through phase space from both the frequency of visits to distinct states and generalized force estimates corresponding to such states. A common feature of the methods is that the underlying functional models and their gradients are easily expressed in terms of the same parameters, thereby providing faster and more effective fitting of the model from simulation data than other available sampling techniques. They also eliminate the need to train simultaneously separate neural networks, while retaining the advantage of generating smooth and continuous functional estimates that enable biasing outside the support grid. Implementation of the methods is relatively simple and, more importantly, they are found to provide gains of up to several orders of magnitude in computational efficiency over existing approaches.

Emulsions comprising isotropic fluid drops within a nematic host are of interest for applications ranging from biodetection to smart windows, which rely on changes of molecular alignment structures around the drops in response to chemical, thermal, electric and other stimuli. We show that absorption or desorption of trace amounts of common surfactants can drive continuous transformations of elastic multipoles induced by the droplets within the uniformly aligned nematic host. Out-of-equilibrium dynamics of director structures emerge from a controlled self-assembly or desorption of different surfactants at the drop-nematic interfaces, with ensuing forward and reverse transformations between elastic dipoles, quadrupoles, octupoles and hexadecapoles. We characterize inter-transformations of droplet-induced surface and bulk defects, probe elastic pair interactions and discuss emergent prospects for fundamental science and applications of the reconfigurable nematic emulsions.

Spin defects in wide-bandgap semiconductors provide a promising platform to create qubits for quantum technologies. Their synthesis, however, presents considerable challenges, and the mechanisms responsible for their generation or annihilation are poorly understood. Here, we elucidate spin defect formation processes in a binary crystal for a key qubit candidate--the divacancy complex (VV) in silicon carbide (SiC). Using atomistic models, enhanced sampling simulations, and density functional theory calculations, we find that VV formation is a thermally activated process that competes with the conversion of silicon ($V_{Si}$) to carbon monovacancies ($V_{C}$), and that VV reorientation can occur without dissociation. We also find that increasing the concentration of $V_{Si}$ relative to $V_{C}$ favors the formation of divacancies. Moreover, we identify pathways to create spin defects consisting of antisite-double vacancy complexes and determine their electronic properties. The detailed view of the mechanisms that underpin the formation and dynamics of spin defects presented here may facilitate the realization of qubits in an industrially relevant material., Comment: 23 pages (main), 16 pages (supporting info), 6 main figures

Janus --or two-sided, charged membranes offer promise as ionic current rectifiers. In such systems, pores consisting of two regions of opposite charge can be used to generate a current from a gradient in salinity. The efficiency of \textcolor{black}{nanoscale} Janus pores increases dramatically as their diameter becomes smaller. However, little is known about the underlying transport processes\textcolor{black}{, particularly under experimentally accessible conditions}. In this work, we examine the molecular basis for rectification in Janus nanopores using an applied electric field. Molecular simulations with explicit water and ions are used to examine the structure and dynamics of all molecular species in aqueous electrolyte solutions. \textcolor{black}{For several macroscopic observables, the results of such simulations are consistent with experimental observations on asymmetric membranes. Our analysis reveals a number of previously unknown features, including a pronounced local re-orientation of water molecules in the pores, and a segregation of ionic species that has not been anticipated by previously reported continuum analyses of Janus pores. Using these insights, a model is proposed for ionic current rectification in which electric leakage at pore entrance controls net transport.

We introduce a method for elucidating and modifying the functionality of systems dominated by rare events that relies on the automated tuning of their underlying free energy surface. The proposed approach seeks to construct collective variables (CVs) that encode the essential information regarding the rare events of the system of interest. The appropriate CVs are identified using Harmonic Linear Discriminant Analysis (HLDA), a machine-learning based method that is trained solely on data collected from short ordinary simulations in the relevant metastable states of the system. Utilizing the interpretable form of the resulting CVs, the critical interaction potentials that determine the system's rare transitions are identified and purposely modified to tailor the free energy surface in manner that alters functionality as desired. The applicability of the method is illustrated in the context of three different systems, thereby demonstrating that thermodynamic and kinetic properties can be tractably modified with little to no prior knowledge or intuition.

Dispersing small particles in a liquid can produce surprising behaviors when the solids fraction becomes large: rapid shearing drives these systems out of equilibrium and can lead to dramatic increases in viscosity (shear-thickening) or even solidification (shear jamming). These phenomena occur above a characteristic onset stress when particles are forced into frictional contact. Here we show via simulations how this can be understood within a framework that abstracts details of the forces acting at particle-particle contacts into general stress-activated constraints on relative particle movement. We find that focusing on just two constraints, affecting sliding and rolling at contact, can reproduce the experimentally observed shear thickening behavior quantitatively, despite widely different particle properties, surface chemistries, and suspending fluids. Within this framework parameters such as coefficients of sliding and rolling friction can each be viewed as a proxy for one or more forces of different physical or chemical origin, while the parameter magnitudes indicate the relative importance of the associated constraint. In this way, a new link is established that connects features observable in macroscale rheological measurements to classes of constraints arising from micro- or nano-scale properties., Comment: 7 Pages, 4 Figures

Topological defects in active liquid crystals can be confined by introducing gradients of activity. Here, we examine the dynamical behavior of two defects confined by a sharp gradient of activity that separates an active circular region and a surrounding passive nematic material. Continuum simulations are used to explain how the interplay among energy injection into the system, hydrodynamic interactions, and frictional forces governs the dynamics of topologically required self-propelling $+1/2$ defects. Our findings are rationalized in terms of a phase diagram for the dynamical response of defects in terms of activity and frictional damping strength. Different regions of the underlying phase diagram correspond to distinct dynamical modes, namely immobile defects (ID), steady rotation of defects (SR), bouncing defects (TB), bouncing-cruising defects (BC), dancing defects (DA), and multiple defects with irregular dynamics (MD). These dynamic states raise the prospect of generating synchronized defect arrays for microfluidic applications.

The Poisson's ratio of a spring network system has been shown to depend not only on the geometry but also on the relative strength of angle-bending forces in comparison to the bond-compression forces in the system. Here we derive the very simple analytic result that in systems where the spring interaction strength is equal to the bond-reorientation interaction, the Poisson's ratio identically goes to zero and is independent of the network geometry., Comment: 3 pages, 2 figures

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such hydrodynamic parameters are difficult to derive from microscopics. Seldom is this challenge more apparent than in active matter where the energy cascade mechanisms responsible for autonomous large-scale dynamics are poorly understood. Here, we use active nematics to demonstrate that neural networks can extract the spatio-temporal variation of hydrodynamic parameters directly from experiments. Our algorithms analyze microtubule-kinesin and actin-myosin experiments as computer vision problems. Unlike existing methods, neural networks can determine how multiple parameters such as activity and elastic constants vary with ATP and motor concentration. In addition, we can forecast the evolution of these chaotic many-body systems solely from image-sequences of their past by combining autoencoder and recurrent networks with residual architecture. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems even when no knowledge of the underlying dynamics exists., Comment: SI Movie 1: https://www.youtube.com/watch?v=9WzIT7OG9pY SI Movie 2: https://youtu.be/Trc4RyU7-dw SI Movie 3: https://youtu.be/Epm_P_EakH8

The Coronavirus Disease (COVID-19) pandemic caused by the SARS-coronavirus 2 (SARS-CoV-2) urgently calls for the design of drugs directed against this new virus. Given its essential role in proteolytic processing, the main protease Mpro has been identified as an attractive candidate for drugs against SARS-CoV-2 and similar coronaviruses. Recent high-throughput screening studies have identified a set of existing, small-molecule drugs as potent Mpro inhibitors. Amongst these, Ebselen (2-Phenyl-1,2-benzoselenazol-3-one), a glutathione peroxidase mimetic seleno-organic compound, is particularly attractive. Recent experiments suggest that its effectiveness is higher than that of other molecules that also act at the enzyme catalytic site. By relying on extensive simulations with all-atom models, in this study we examine at a molecular level the potential of Ebselen to decrease Mpro catalytic activity. Our results indicate that Ebselen exhibits a distinct affinity for the catalytic site cavity of Mpro. In addition, our molecular models reveal a second, previously unkown binding site for Ebselen in the dimerization region localized between the II and III domains of the protein. A detailed analysis of the free energy of binding indicates that the affinity of Ebselen to this second binding site is in fact significantly larger than that to the catalytic site. A strain analysis indicates that Ebselen bound between the II-III domains exerts a pronounced allosteric effect that regulates catalytic site access through surface loop interactions, and induces a displacement and reconfiguration of water hotspots, including the catalytic water, which could interfere with normal enzymatic function. Taken together, these findings provide a framework for the future design of more potent and specific Mpro inhibitors, based on the Ebselen scaffold, that could lead to new therapeutic strategies for COVID-19., Comment: 10 pages, 5 figures

Particle-based simulations of discontinuous shear thickening (DST) and shear jamming (SJ) suspensions are used to study the role of stress-activated constraints, with an emphasis on resistance to gear-like rolling. Rolling friction decreases the volume fraction required for DST and SJ, in quantitative agreement with real-life suspensions with adhesive surface chemistries and "rough" particle shapes. It sets a distinct structure of the frictional force network compared to only sliding friction, and from a dynamical perspective leads to an increase in the velocity correlation length, in part responsible for the increased viscosity. The physics of rolling friction is thus a key element in achieving a comprehensive understanding of strongly shear-thickening materials., Comment: 5 pages, 4 figures. +Supplemental Material--- --- Supplementary Material also consists of the following movies: ---https://youtu.be/wgrfN7jiu1g (Force network in 3D at volume fraction 0.45, at high stress limit, with and without rolling friction) --- https://youtu.be/SMzTB7CsRsY (Force network in 2D at packing fraction 0.7, at high stress limit, with and without rolling friction)

The structure and dynamics of confined suspensions of particles of arbitrary shape is of interest in multiple disciplines, from biology to engineering. Theoretical studies are often limited by the complexity of long-range particle-particle and particle-wall forces, including many-body fluctuating hydrodynamic interactions. Here, we report a computational study on the diffusion of spherical and cylindrical particles confined in a spherical cavity. We rely on an Immersed-Boundary General geometry Ewald-like method to capture lubrication and long-range hydrodynamics, and include appropriate non-slip conditions at the confining walls. A Chebyshev polynomial approximation is used to satisfy the fluctuation-dissipation theorem for the Brownian suspension. We explore how lubrication, long-range hydrodynamics, particle volume fraction and shape affect the equilibrium structure and the diffusion of the particles. It is found that once the particle volume fraction is greater than $10\%$, the particles start to form layered aggregates that greatly influence particle dynamics. Hydrodynamic interactions strongly influence the particle diffusion by inducing spatially dependent short-time diffusion coefficients, stronger wall effects on the particle diffusion towards the walls, and a sub-diffusive regime --caused by crowding-- in the long-time particle mobility. The level of asymmetry of the cylindrical particles considered here is enough to induce an orientational order in the layered structure, decreasing the diffusion rate and facilitating a transition to the crowded mobility regime at low particle concentrations. Our results offer fundamental insights into the diffusion and distribution of globular and fibrillar proteins inside cells., Comment: 10 pages, 7 Figures+SI

Colloid or nanoparticle mobility under confinement is of central importance to a wide range of physical and biological processes. Here, we introduce a minimal model of particles in a hydrodynamic continuum to examine how particle shape and concentration affect the transport of particles in spherical confinement. Specifically, an immersed boundary-General geometry Ewald-like approach is adopted to simulate the dynamics of spheres and cylinders under the influence of short-and long-range fluctuating hydrodynamic interactions with appropriate non-slip conditions at the confining walls. An efficient $\it{O(N)}$ parallel finite element algorithm is used, thereby allowing simulations at high concentrations, while a Chebyshev polynomial approximation is implemented in order to satisfy the fluctuation-dissipation theorem. A concentration-dependent anomalous diffusion is observed for suspended particles. It is found that introducing cylinders in a background of spheres, i.e. particles with a simple degree of anisotropy, has a pronounced influence on the structure and dynamics of the particles. First, increasing the fraction of cylinders induces a particle segregation effect, where spheres are pushed towards the wall and cylinders remain near the center of the cavity. This segregation leads to lower mobility for the spheres relative to that encountered in a system of pure spheres at the same volume fraction. Second, the diffusive-to-anomalous transition and the degree of anomaly--quantified by the power-law exponent in the mean square displacement vs. time relation-both increase as the fraction of cylinders becomes larger. These findings are of relevance for studies of diffusion in the cytoplasm, where proteins exhibit a distribution of size and shapes that could lead to some of the effects identified in the simulations reported here., Comment: 9 pages, 5 figures

Pure liquids in thermodynamic equilibrium are structurally homogeneous. In liquid crystals, flow and light pulses are used to create reconfigurable domains with polar order. Moreover, through careful engineering of concerted microfluidic flows and localized opto-thermal fields, it is possible to achieve complete control over the nucleation, growth, and shape of such domains. Experiments, theory, and simulations indicate that the resulting structures can be stabilized indefinitely, provided the liquids are maintained in a controlled non-equilibrium state. The resulting sculpted liquids could find applications in microfluidic devices for selective encapsulation of solutes and particles into optically active compartments that interact with external stimuli.

Active materials are capable of converting free energy into mechanical work to produce autonomous motion, and exhibit striking collective dynamics that biology relies on for essential functions. Controlling those dynamics and transport in synthetic systems has been particularly challenging. Here, we introduce the concept of spatially structured activity as a means to control and manipulate transport in active nematic liquid crystals consisting of actin filaments and light-sensitive myosin motors. Simulations and experiments are used to demonstrate that topological defects can be generated at will, and then constrained to move along specified trajectories, by inducing local stresses in an otherwise passive material. These results provide a foundation for design of autonomous and reconfigurable microfluidic systems where transport is controlled by modulating activity with light.

Auxetic materials are characterized by a negative Poisson's ratio, $\mathrm{\nu}$. As the Poisson's ratio becomes negative and approaches the lower isotropic mechanical limit of $\mathrm{\nu = -1}$, materials show enhanced resistance to impact and shear, making them suitable for applications ranging from robotics to impact mitigation. Past experimental efforts aimed at reaching the $\mathrm{\nu = -1}$ limit have resulted in highly anisotropic materials, which show a negative Poisson's ratio only when subjected to deformations along specific directions. Isotropic designs have only attained moderately auxetic behavior, or have led to structures that cannot be manufactured in 3D. Here, we present a design strategy to create isotropic structures from disordered networks that leads to Poisson's ratios as low as $\mathrm{\nu = -0.98}$. The materials conceived through this approach are successfully fabricated in the laboratory and behave as predicted. The Poisson's ratio $\mathrm{\nu}$ is found to depend on network structure and bond strengths; this sheds light on the structural motifs that lead to auxetic behavior. The ideas introduced here can be generalized to 3D, a wide range of materials, and a spectrum of length scales, thereby providing a general platform that could impact technology., Comment: 16 pages, 6 figures

Active materials are capable of converting free energy into directional motion, giving rise to striking dynamical phenomena. Developing a general understanding of their structure in relation to the underlying non-equilibrium physics would provide a route towards control of their dynamic behavior and pave the way for potential applications. The active system considered here consists of a quasi-two-dimensional sheet of short ($\approx$ 1 $\mu$m) actin filaments driven by myosin-II motors. By adopting a concerted theoretical and experimental strategy, new insights are gained into the non-equilibrium properties of active nematics over a wide range of internal activity levels. In particular, it is shown that topological defect interactions can be led to transition from attractive to repulsive as a function of initial defect separation and relative orientation. Furthermore, by examining the +1/2 defect morphology as a function of activity, it is found that the apparent elastic properties of the system (the ratio of bend-to-splay elastic moduli) are altered considerably by increased activity, leading to an effectively lower bend elasticity. At high levels of activity, the topological defects that decorate the material exhibit a liquid-like structure, and adopt preferred orientations depending on their topological charge. Taken together, these results suggest that it should be possible to tune internal stresses in active nematic systems with the goal of designing out-of-equilibrium structures with engineered dynamic responses.

In this work simulations are presented of low viscosity ethanol and high viscosity silicone oil droplets impacting on a dry solid surface at atmospheric and reduced ambient pressure. The simulations are able to capture both the effect of the ambient gas pressure and liquid viscosity on droplet impact and breakup. The results suggests that the early time droplet impact and gas film behavior for both low and high viscosity liquids share the same physics. However, for later time liquid sheet formation and breakup high and low viscosity liquids behave differently. These results explain why for both kinds of liquids the pressure effect can be observed, while at the same time different high and low viscosity splashing regimes have been identified experimentally.

We report a computational strategy to obtain the charges of individual dielectric particles from experimental observation of their interactions as a function of time. This strategy uses evolutionary optimization to minimize the difference between trajectories extracted from experiment and simulated trajectories based on many-particle force fields. The force fields include both Coulombic interactions and dielectric polarization effects that arise due to particle-particle charge mismatch and particle-environment dielectric contrast. The strategy was applied to systems of free falling charged granular particles in vacuum, where electrostatic interactions are the only driving forces that influence the particles' motion. We show that when the particles' initial positions and velocities are known, the optimizer requires only an initial and final particle configuration of a short trajectory in order to accurately infer the particles' charges; when the initial velocities are unknown and only the initial positions are given, the optimizer can learn from multiple frames along the trajectory to determine the particles' initial velocities and charges. While the results presented here offer a proof-of-concept demonstration of the proposed ideas, the proposed strategy could be extended to more complex systems of electrostatically charged granular matter.