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Nucleation plays a critical role in the birth of crystals and is associated with a vast array of phenomena, such as protein crystallization and ice formation in clouds. Despite numerous experimental and theoretical studies, many aspects of the nucleation process, such as the polymorph selection mechanism in the early stages, are far from being understood. Here, we show that the hitherto unexplained excess of particles in a face-centered-cubic (fcc)-like environment, as compared to those in a hexagonal-close-packed (hcp)-like environment, in a crystal nucleus of hard spheres can be explained by the higher order structure in the fluid phase. We show using both simulations and experiments that in the metastable fluid phase, pentagonal bipyramids, clusters with fivefold symmetry known to be inhibitors of crystal nucleation, transform into a different cluster, Siamese dodecahedra. These clusters are closely similar to an fcc subunit, which explains the higher propensity to grow fcc than hcp in hard spheres. We show that our crystallization and polymorph selection mechanism is generic for crystal nucleation from a dense, strongly correlated fluid phase.

Leveraging the anisotropic shape of DNA-functionalized nanoparticles holds potential for shape-directed crystallization of a wide collection of superlattice structures. Using coarse-grained molecular dynamics simulations, we study the self-assembly of a binary mixture of cubic gold nanoparticles, which are functionalized by complementary DNA strands. We observe the spontaneous self-assembly of simple cubic (SC), plastic body-centered tetragonal (pBCT), and compositionally disordered plastic body-centered tetragonal (d-pBCT) phases due to hybridization of the DNA strands. We systematically investigate the effect of length, grafting density, as well as rigidity of the DNA strands on the self-assembly behavior of cubic nanoparticles. We measure the potential of mean force between DNA-functionalized nanocubes for varying rigidity of the DNA strands and DNA lengths. Using free-energy calculations, we find that longer and flexible DNA strands can lead to a phase transformation from SC to the pBCT phase due to a gain in entropy arising from the orientational degrees of freedom of the nanocubes in the pBCT phase. Our results may serve as a guide for self-assembly experiments on DNA-functionalized cubic nanoparticles.

We introduce a machine-learning approach to predict the complex non-Markovian dynamics of supercooled liquids from static averaged quantities. Compared to techniques based on particle propensity, our method is built upon a theoretical framework that uses as input and output system-averaged quantities, thus being easier to apply in an experimental context where particle resolved information is not available. In this work, we train a deep neural network to predict the self intermediate scattering function of binary mixtures using their static structure factor as input. While its performance is excellent for the temperature range of the training data, the model also retains some transferability in making decent predictions at temperatures lower than the ones it was trained for, or when we use it for similar systems. We also develop an evolutionary strategy that is able to construct a realistic memory function underlying the observed non-Markovian dynamics. This method lets us conclude that the memory function of supercooled liquids can be effectively parameterized as the sum of two stretched exponentials, which physically corresponds to two dominant relaxation modes.

Using a grand-canonical Landau-de Gennes theory for colloidal suspensions of bent (banana-shaped) rods, we investigate how spatial deformations in the nematic director field affect the local density of twist-bend and splay-bend nematic phases. The grand-canonical character of the theory naturally relates the local density to the local nematic order parameter S. In the splay-bend phase, we find S and hence the local density to modulate periodically along one spatial direction. As a consequence the splay-bend phase has the key symmetries of a smectic rather than a nematic phase. By contrast we find that S and hence the local density do not vary in space in the twist-bend phase, which is therefore a proper nematic phase. The theoretically predicted one-dimensional density modulations in splay-bend phases are in agreement with recent simulations.

Combinatorial metamaterials are metamaterials designed by combining fundamental building blocks, unit cells, picked from a discrete set. This discretized design space allows us to explore the limitless structural complexity of metamaterials in a controlled manner. However, analytical and conventional numerical approaches have difficulty in efficiently navigating this large design space, which grows exponentially with system size. Here we employ machine learning techniques to explore combinatorial metamaterial designs. We show that a trained convolutional neural network is able to classify never before seen configurations into those supporting system-wide periodic deformations in one dimension, line modes, and those who do not with over 99% accuracy. This suggests that the network has correctly learned to identify the set of design rules for the presence of a line mode. To study the scalability of this long-correlation classification problem, we investigate the relation between network complexity and configuration size. Our work provides insight into application and scaling of neural networks with regard to complex discrete structure-property maps.

Using computer simulations, we study the isotropic-to-nematic nucleation in a fluid of colloidal hard rods as well as in a mixture of colloidal rods and non-adsorbing polymer. In order to follow the transformation of the system from the isotropic to the nematic phase, we use a new cluster criterion that enables us to distinguish the nematic clusters from the isotropic fluid phase. Applying this criterion in Monte Carlo simulations, we find two different regimes depending on the supersaturation. At low supersaturation we find nucleation and growth, while at higher supersaturation spinodal decomposition is observed. We determine the height of the nucleation barrier, and we study the structure as well as the shape of the nematic clusters. We discuss our simulation results in the light of classical nucleation theory.

Using simulations, we study the diffusion of rod-like guest particles in a smectic environment of rod-like host particles. We find that the dynamics of guest rods across smectic layers changes from a fast nematic-like diffusion to a slow hopping-type dynamics via an intermediate switching regime by varying the length of the guest rods with respect to the smectic layer spacing. We determine the optimal rod length that yields the fastest and the slowest diffusion in a lamellar environment. We show that this behavior can be rationalized by a complex 1D effective periodic potential exhibiting two energy barriers, resulting in a varying preferred mean position of the guest particle in the smectic layer. The interplay of these two barriers controls the dynamics of the guest particles yielding a slow, an intermediate and a fast diffusion regime depending on the particle length., To be published in Physical Review Letters

The influence of geometry on the local and global packing of particles is important to many fundamental and applied research themes, such as the structure and stability of liquids, crystals and glasses. Here we show by experiments and simulations that a binary mixture of hard-sphere-like nanoparticles crystallizing into a MgZn(2)Laves phase in bulk spontaneously forms icosahedral clusters in slowly drying droplets. Using advanced electron tomography, we are able to obtain the real-space coordinates of all the spheres in the icosahedral clusters of up to about 10,000 particles. The local structure of 70-80% of the particles became similar to that of the MgCu(2)Laves phase. These observations are important for photonic applications. In addition, we observed in simulations that the icosahedral clusters nucleated away from the spherical boundary, which is distinctly different from that of the single species clusters. Our findings open the way for particle-level studies of nucleation and growth of icosahedral clusters, and of binary crystallization. The authors investigate out-of-equilibrium crystallization of a binary mixture of sphere-like nanoparticles in small droplets. They observe the spontaneous formation of an icosahedral structure with stable MgCu(2)phases, which are promising for photonic applications.

We studied the formation of supraparticles from nanocrystals confined in slowly evaporating oil droplets in an oil-in-water emulsion. The nanocrystals consist of an FeO core, a CoFe2O4 shell, and oleate capping ligands, with an overall diameter of 12.5 nm. We performed in situ small- and wide-angle X-ray scattering experiments during the entire period of solvent evaporation and colloidal crystallization. We observed a slow increase in the volume fraction of nanocrystals inside the oil droplets up to 20%, at which a sudden crystallization occurs. Our computer simulations show that crystallization at such a low volume fraction is only possible if attractive interactions between colloidal nanocrystals are taken into account in the model as well. The spherical supraparticles have a diameter of about 700 nm and consist of a few crystalline face-centered cubic domains. Nanocrystal supraparticles bear importance for magnetic and optoelectronic applications, such as color tunable biolabels, color tunable phosphors in LEDs, and miniaturized lasers.

Periodic nano- or microscale structures are used to control light, energy and mass transportation. Colloidal organization is the most versatile method used to control nano- and microscale order, and employs either the enthalpy-driven self-assembly of particles at a low concentration or the entropy-driven packing of particles at a high concentration. Nonetheless, it cannot yet provide the spontaneous three-dimensional organization of multicomponent particles at a high concentration. Here we combined these two concepts into a single strategy to achieve hierarchical multicomponent materials. We tuned the electrostatic attraction between polymer and silica nanoparticles to create dynamic supracolloids whose components, on drying, reorganize by entropy into three-dimensional structured materials. Cryogenic electron tomography reveals the kinetic pathways, whereas Monte Carlo simulations combined with a kinetic model provide design rules to form the supracolloids and control the kinetic pathways. This approach may be useful to fabricate hierarchical hybrid materials for distinct technological applications.

Reviews of modern physics 91(3), 030001 (2019). doi:10.1103/RevModPhys.91.030001, Ninety years! As the first spinoff of the Physical Review launched in 1929, Reviews of ModernPhysics (RMP) has tracked the progress of physics during a time of explosive growth of the field.Originally one journal, the Physical Review has not only split into the more specialized journals weknow today, but the scope, breadth, and depth of topics covered could hardly have been imagined byJohn Tate, Sr., the editor in chief who started RMP. Yet RMP is still published quarterly. So whyhasn’t RMP grown at the same prodigious rate? The research that informs these reviews isprogressing faster, but writing a review that is broadly accessible and that will remain relevant fordecades has therefore become more difficult. RMP’s role is to find authors who are up to thischallenge.At the April 2019 editorial board meeting we resolved to reaffirm our mission of providing thecommunity with articles that capture the essence of the field, with introductions and conclusions thatare accessible to students (our most junior colleagues who will carry science into the future) and thatcommunicate how and why the topic has fascinated scientists and provided an arena for newparadigms, theories, and connections between subfields. To this end, we will implement even higherstandards for our submissions: not only will submitted manuscripts be reviewed by experts, they willalso be reviewed by qualified non-experts from nearby subfields to ensure broad accessibility.Though this is, of course, a greater burden on our referees and authors, we believe that it is necessaryto publish “classic” articles and keep RMP vital for many years to come.We look forward to continuing to serve the community., Published by APS, College Park, Md.

Assembling binary mixtures of nanoparticles into crystals, gives rise to collective properties depending on the crystal structure and the individual properties of both species. However, quantitative 3D real-space analysis of binary colloidal crystals with a thickness of more than 10 layers of particles has rarely been performed. Here we demonstrate that an excess of one species in the binary nanoparticle mixture suppresses the formation of icosahedral order in the self-assembly in droplets, allowing the study of bulk-like binary crystal structures with a spherical morphology also called supraparticles. As example of the approach, we show single-particle level analysis of over 50 layers of Laves phase binary crystals of hard-sphere-like nanoparticles using electron tomography. We observe a crystalline lattice composed of a random mixture of the Laves phases. The number ratio of the binary species in the crystal lattice matches that of a perfect Laves crystal. Our methodology can be applied to study the structure of a broad range of binary crystals, giving insights into the structure formation mechanisms and structure-property relations of nanomaterials., 3D real-space analysis of thick nanoparticle crystals is non-trivial. Here, the authors demonstrate the structural analysis of a bulk-like Laves phase by imaging an off-stoichiometric binary mixture of hard-sphere-like nanoparticles in spherical confinement by electron tomography, enabling defect analysis on the single-particle level.

In continuation of our work in [A.P. Gantapara et al., Phys. Rev. Lett. 111, 015501 (2013)], we investigate here the thermodynamic phase behavior of a family of truncated hard cubes, for which the shape evolves smoothly from a cube via a cuboctahedron to an octahedron. We used Monte Carlo simulations and free-energy calculations to establish the full phase diagram. This phase diagram exhibits a remarkable richness in crystal and mesophase structures, depending sensitively on the precise particle shape. In addition, we examined in detail the nature of the plastic crystal (rotator) phases that appear for intermediate densities and levels of truncation. Our results allow us to probe the relation between phase behavior and building-block shape and to further the understanding of rotator phases. Furthermore, the phase diagram presented here should prove instrumental for guiding future experimental studies on similarly-haped nanoparticles and the creation of new materials., Comment: 19 pages, 12 figures