Design strategies for the self-assembly of polyhedral shells.

The control over the self-assembly of complex structures is a long-standing challenge of material science, especially at the colloidal scale, as the desired assembly pathway is often kinetically derailed by the formation of amorphous aggregates. Here, we investigate in detail the problem of the self-assembly of the three Archimedean shells with five contact points per vertex, i.e., the icosahedron, the snub cube, and the snub dodecahedron. We use patchy particles with five interaction sites (or patches) as model for the building blocks and recast the assembly problem as a Boolean satisfiability problem (SAT) for the patch-patch interactions. This allows us to find effective designs for all targets and to selectively suppress unwanted structures. By tuning the geometrical arrangement and the specific interactions of the patches, we demonstrate that lowering the symmetry of the building blocks reduces the number of competing structures, which in turn can considerably increase the yield of the target structure. These results cement SAT-assembly as an invaluable tool to solve inverse design problems.

Inverse Design of Self-Folding 3D Shells.

Self-folding is an emerging paradigm for the inverse design of three-dimensional structures. While most efforts have concentrated on the shape of the net, our approach introduces a new design dimension-bond specificity between the edges. We transform this design process into a Boolean satisfiability problem to derive solutions for various target structures. This method significantly enhances the yield of the folding process. Furthermore, by linearly combining independent solutions, we achieve designs for shape-shifting nets wherein the dominant structure evolves with varying external conditions. This approach is demonstrated through coarse-grained simulations on two examples of triangular and square nets capable of folding into multiple target shapes.

Vascular KATP channel structural dynamics reveal regulatory mechanism by Mg-nucleotides.

Vascular tone is dependent on smooth muscle KATP channels comprising pore-forming Kir6.1 and regulatory SUR2B subunits, in which mutations cause Cantú syndrome. Unique among KATP isoforms, they lack spontaneous activity and require Mg-nucleotides for activation. Structural mechanisms underlying these properties are unknown. Here, we determined cryogenic electron microscopy structures of vascular KATP channels bound to inhibitory ATP and glibenclamide, which differ informatively from similarly determined pancreatic KATP channel isoform (Kir6.2/SUR1). Unlike SUR1, SUR2B subunits adopt distinct rotational "propeller" and "quatrefoil" geometries surrounding their Kir6.1 core. The glutamate/aspartate-rich linker connecting the two halves of the SUR-ABC core is observed in a quatrefoil-like conformation. Molecular dynamics simulations reveal MgADP-dependent dynamic tripartite interactions between this linker, SUR2B, and Kir6.1. The structures captured implicate a progression of intermediate states between MgADP-free inactivated, and MgADP-bound activated conformations wherein the glutamate/aspartate-rich linker participates as mobile autoinhibitory domain, suggesting a conformational pathway toward KATP channel activation.

Structural Signatures of Ultrastability in a Deposited Glassformer.

Glasses obtained from vapor deposition on a cold substrate have superior thermodynamic and kinetic stability with respect to ordinary glasses. Here we perform molecular dynamics simulations of vapor deposition of a model glassformer and investigate the origin of its high stability compared to that of ordinary glasses. We find that the vapor deposited glass is characterized by locally favored structures (LFSs) whose occurrence correlates with its stability, reaching a maximum at the optimal deposition temperature. The formation of LFSs is enhanced near the free surface, hence supporting the idea that the stability of vapor deposited glasses is connected to the relaxation dynamics at the surface.

Dielectric and Mechanical Properties of 3D Printed Nanocomposites with Varied Particle Diameter.

3D printed nanocomposites provide a method for generating high-performance radio frequency devices. Limited work has been done to investigate the influence the nanoparticle diameter has on the performance of 3D printable nanocomposites. We describe here the development of a family of 3D printable nanocomposite inks formulated from nanoparticles with diameters ranging from 30 to 300 nm. Relative permittivity values for the printed nanocomposites were unaffected by nanoparticle diameter whereas loss tangent, glass transition temperature, and elastic modulus were altered. This work provides a framework for designing 3D printable nanocomposites and highlights the importance that nanoparticle diameter plays in formulation strategy.

Dominating lengthscales of zebrafish collective behaviour.

Collective behaviour in living systems is observed across many scales, from bacteria to insects, to fish shoals. Zebrafish have emerged as a model system amenable to laboratory study. Here we report a three-dimensional study of the collective dynamics of fifty zebrafish. We observed the emergence of collective behaviour changing between ordered to randomised, upon adaptation to new environmental conditions. We quantify the spatial and temporal correlation functions of the fish and identify two length scales, the persistence length and the nearest neighbour distance, that capture the essence of the behavioural changes. The ratio of the two length scales correlates robustly with the polarisation of collective motion that we explain with a reductionist model of self-propelled particles with alignment interactions.

A neural network potential with self-trained atomic fingerprints: A test with the mW water potential.

We present a neural network (NN) potential based on a new set of atomic fingerprints built upon two- and three-body contributions that probe distances and local orientational order, respectively. Compared with the existing NN potentials, the atomic fingerprints depend on a small set of tunable parameters that are trained together with the NN weights. In addition to simplifying the selection of the atomic fingerprints, this strategy can also considerably increase the overall accuracy of the network representation. To tackle the simultaneous training of the atomic fingerprint parameters and NN weights, we adopt an annealing protocol that progressively cycles the learning rate, significantly improving the accuracy of the NN potential. We test the performance of the network potential against the mW model of water, which is a classical three-body potential that well captures the anomalies of the liquid phase. Trained on just three state points, the NN potential is able to reproduce the mW model in a very wide range of densities and temperatures, from negative pressures to several GPa, capturing the transition from an open random tetrahedral network to a dense interpenetrated network. The NN potential also reproduces very well properties for which it was not explicitly trained, such as dynamical properties and the structure of the stable crystalline phases of mW.

From ultra-fast growth to avalanche growth in devitrifying glasses.

During devitrification, pre-existing crystallites grow by adding particles to their surface via a process that is either thermally activated (diffusive mode) or happens without kinetic barriers (fast crystal growth mode). It is yet unclear what factors determine the crystal growth mode and how to predict it. With simulations of repulsive hard-sphere-like (Weeks-Chandler-Andersen) glasses, we show for the first time that the same system at the same volume fraction and temperature can devitrify via both modes depending on the preparation protocol of the glass. We prepare two types of glass: conventional glass (CG) via fast quenching and uniform glass (UG) via density homogenization. First, we bring either glass into contact with a crystal (X) and find the inherent structure (CGX/UGX). During energy minimization, the crystal front grows deep into the CG interface, while the growth is minimal for UG. When thermal noise is added, this behavior is reflected in different crystallization dynamics. CGX exhibits a density drop at the crystal growth front, which correlates with enhanced dynamics at the interface and a fast growth mode. This mechanism may explain the faster crystal growth observed below the glass transition experimentally. In contrast, UGX grows via intermittent avalanche-like dynamics localized at the interface, a combination of localized mechanical defects and the exceptional mechanical stability imposed by the UG glass phase.

Active Brownian particles in random and porous environments.

The transport of active particles may occur in complex environments, in which it emerges from the interplay between the mobility of the active components and the quenched disorder of the environment. Here, we explore the structural and dynamical properties of active Brownian particles (ABPs) in random environments composed of fixed obstacles in three dimensions. We consider different arrangements of the obstacles. In particular, we consider two particular situations corresponding to experimentally realizable settings. First, we model pinning particles in (non-overlapping) random positions and, second, in a percolating gel structure and provide an extensive characterization of the structure and dynamics of ABPs in these complex environments. We find that the confinement increases the heterogeneity of the dynamics, with new populations of absorbed and localized particles appearing close to the obstacles. This heterogeneity has a profound impact on the motility induced phase separation exhibited by the particles at high activity, ranging from nucleation and growth in random disorder to a complex phase separation in porous environments.

Two-step nucleation in a binary mixture of patchy particles.

Nucleation in systems with a metastable liquid-gas critical point is the prototypical example of a two-step nucleation process in which the appearance of the critical nucleus is preceded by the formation of a liquid-like density fluctuation. So far, the majority of studies on colloidal and protein crystallization have focused on one-component systems, and we are lacking a clear description of two-step nucleation processes in multicomponent systems, where critical fluctuations involve coupled density and concentration inhomogeneities. Here, we examine the nucleation process of a binary mixture of patchy particles designed to nucleate into a diamond lattice. By combining Gibbs-ensemble simulations and direct nucleation simulations over a wide range of thermodynamic conditions, we are able to pin down the role of the liquid-gas metastable phase diagram on the nucleation process. In particular, we show that the strongest enhancement of crystallization occurs at an azeotropic point with the same stoichiometric composition of the crystal.

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

The physics of empty liquids: from patchy particles to water.

Empty liquids represent a wide class of materials whose constituents arrange in a random network through reversible bonds. Many key insights on the physical properties of empty liquids have originated almost independently from the study of colloidal patchy particles on one side, and a large body of theoretical and experimental research on water on the other side. Patchy particles represent a family of coarse-grained potentials that allows for a precise control of both the geometric and the energetic aspects of bonding, while water has arguably the most complex phase diagram of any pure substance, and a puzzling amorphous phase behavior. It was only recently that the exchange of ideas from both fields has made it possible to solve long-standing problems and shed new light on the behavior of empty liquids. Here we highlight the connections between patchy particles and water, focusing on the modelling principles that make an empty liquid behave like water, including the factors that control the appearance of thermodynamic and dynamic anomalies, the possibility of liquid-liquid phase transitions, and the crystallization of open crystalline structures.

Temperature- and pressure-dependence of the hydrogen bond network in plastic ice VII.

We model, via classical molecular dynamics simulations, the plastic phase of ice VII across a wide range of the phase diagram of interest for planetary investigations. Although structural and dynamical properties of plastic ice VII are mostly independent on the thermodynamic conditions, the hydrogen bond network (HBN) acquires a diverse spectrum of topologies distinctly different from that of liquid water and of ice VII simulated at the same pressure. We observe that the HBN topology of plastic ice carries some degree of similarity with the crystal phase, stronger at thermodynamic conditions proximal to ice VII, and gradually lessening when approaching the liquid state. Our results enrich our understanding of the properties of water at high pressure and high temperature and may help in rationalizing the geology of water-rich planets.

Towards Glasses with Permanent Stability.

Unlike crystals, glasses age or devitrify over time, reflecting their nonequilibrium nature. This lack of stability is a serious issue in many industrial applications. Here, we show by numerical simulations that the devitrification of quasi-hard-sphere glasses is prevented by suppressing volume-fraction inhomogeneities. A monodisperse glass known to devitrify with "avalanchelike" intermittent dynamics is subjected to small iterative adjustments to particle sizes to make the local volume fractions spatially uniform. We find that this entirely prevents structural relaxation and devitrification over aging time scales, even in the presence of crystallites. There is a dramatic homogenization in the number of load-bearing nearest neighbors each particle has, indicating that ultrastable glasses may be formed via "mechanical homogenization." Our finding provides a physical principle for glass stabilization and opens a novel route to the formation of mechanically stabilized glasses.

Decorated networks of native proteins: nanomaterials with tunable mesoscopic domain size.

Natural and artificial proteins with designer properties and functionalities offer unparalleled opportunity for functional nanoarchitectures formed through self-assembly. However, to exploit this potential we need to design the system such that assembly results in desired architecture forms while avoiding denaturation and therefore retaining protein functionality. Here we address this challenge with a model system of fluorescent proteins. By manipulating self-assembly using techniques inspired by soft matter where interactions between the components are controlled to yield the desired structure, we have developed a methodology to assemble networks of proteins of one species which we can decorate with another, whose coverage we can tune. Consequently, the interfaces between domains of each component can also be tuned, with potential applications for example in energy - or electron - transfer. Our model system of eGFP and mCherry with tuneable interactions reveals control over domain sizes in the resulting networks.

Crystallisation and polymorph selection in active Brownian particles.

We explore crystallisation and polymorph selection in active Brownian particles with numerical simulation. In agreement with previous work (Wysocki et al. in Europhys Lett 105:48004, 2014), we find that crystallisation is suppressed by activity and occurs at higher densities with increasing Péclet number ([Formula: see text]). While the nucleation rate decreases with increasing activity, the crystal growth rate increases due to the accelerated dynamics in the melt. As a result of this competition, we observe the transition from a nucleation and growth regime at high [Formula: see text] to "spinodal nucleation" at low [Formula: see text]. Unlike the case of passive hard spheres, where preference for FCC over HCP polymorphs is weak, activity causes the annealing of HCP stacking faults, thus strongly favouring the FCC symmetry at high [Formula: see text]. When freezing occurs more slowly, in the nucleation and growth regime, this tendency is much reduced and we see a trend towards the passive case of little preference for either polymorph.

Structural and topological changes across the liquid-liquid transition in water.

It has recently been shown that the TIP4P/Ice model of water can be studied numerically in metastable equilibrium at and below its liquid-liquid critical temperature. We report here simulations along a subcritical isotherm, for which two liquid states with the same pressure and temperature but different density can be equilibrated. This allows for a clear visualization of the structural changes taking place across the transition. We specifically focus on how the topological properties of the H-bond network change across the liquid-liquid transition. Our results demonstrate that the structure of the high-density liquid, characterized by the existence of interstitial molecules and commonly explained in terms of the collapse of the second neighbor shell, actually originates from the folding back of long rings, bringing pairs of molecules separated by several hydrogen-bonds close by in space.

Origin of the emergent fragile-to-strong transition in supercooled water.

Liquids can be broadly classified into two categories, fragile and strong ones, depending on how their dynamical properties change with temperature. The dynamics of a strong liquid obey the Arrhenius law, whereas the fragile one displays a super-Arrhenius law, with a much steeper slowing down upon cooling. Recently, however, it was discovered that many materials such as water, oxides, and metals do not obey this simple classification, apparently exhibiting a fragile-to-strong transition far above [Formula: see text] Such a transition is particularly well known for water, and it is now regarded as one of water's most important anomalies. This phenomenon has been attributed to either an unusual glass transition behavior or the crossing of a Widom line emanating from a liquid-liquid critical point. Here by computer simulations of two popular water models and through analyses of experimental data, we show that the emergent fragile-to-strong transition is actually a crossover between two Arrhenius regimes with different activation energies, which can be naturally explained by a two-state description of the dynamics. Our finding provides insight into the fragile-to-strong transition observed in a wide class of materials.

Water-like anomalies as a function of tetrahedrality.

Tetrahedral interactions describe the behavior of the most abundant and technologically important materials on Earth, such as water, silicon, carbon, germanium, and countless others. Despite their differences, these materials share unique common physical behaviors, such as liquid anomalies, open crystalline structures, and extremely poor glass-forming ability at ambient pressure. To reveal the physical origin of these anomalies and their link to the shape of the phase diagram, we systematically study the properties of the Stillinger-Weber potential as a function of the strength of the tetrahedral interaction [Formula: see text] We uncover a unique transition to a reentrant spinodal line at low values of [Formula: see text], accompanied with a change in the dynamical behavior, from non-Arrhenius to Arrhenius. We then show that a two-state model can provide a comprehensive understanding on how the thermodynamic and dynamic anomalies of this important class of materials depend on the strength of the tetrahedral interaction. Our work establishes a deep link between the shape of the phase diagram and the thermodynamic and dynamic properties through local structural ordering in liquids and hints at why water is so special among all substances.

A gentle introduction to the non-equilibrium physics of trajectories: Theory, algorithms, and biomolecular applications.

Despite the importance of non-equilibrium statistical mechanics in modern physics and related fields, the topic is often omitted from undergraduate and core-graduate curricula. Key aspects of non-equilibrium physics, however, can be understood with a minimum of formalism based on a rigorous trajectory picture. The fundamental object is the ensemble of trajectories, a set of independent time-evolving systems, which easily can be visualized or simulated (e.g., for protein folding) and which can be analyzed rigorously in analogy to an ensemble of static system configurations. The trajectory picture provides a straightforward basis for understanding first-passage times, "mechanisms" in complex systems, and fundamental constraints on the apparent reversibility of complex processes. Trajectories make concrete the physics underlying the diffusion and Fokker-Planck partial differential equations. Last but not least, trajectory ensembles underpin some of the most important algorithms that have provided significant advances in biomolecular studies of protein conformational and binding processes.