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HOOMD-blue is a particle simulation engine designed for nano- and colloidal-scale molecular dynamics and hard particle Monte Carlo simulations. It has been actively developed since ...March 2007 and available open source since August 2008. HOOMD-blue is a Python package with a high performance C++/CUDA backend that we built from the ground up for GPU acceleration. The Python interface allows users to combine HOOMD-blue with other packages in the Python ecosystem to create simulation and analysis workflows. We employ software engineering practices to develop, test, maintain, and expand the code.
Expanding the library of self-assembled superstructures provides insight into the behaviour of atomic crystals and supports the development of materials with mesoscale order. Here we build on recent ...findings of soft matter quasicrystals and report a quasicrystalline binary nanocrystal superlattice that exhibits correlations in the form of partial matching rules reducing tiling disorder. We determine a three-dimensional structure model through electron tomography and direct imaging of surface topography. The 12-fold rotational symmetry of the quasicrystal is broken in sublayers, forming a random tiling of rectangles, large triangles and small triangles with 6-fold symmetry. We analyse the geometry of the experimental tiling and discuss factors relevant for the stabilization of the quasicrystal. Our joint experimental-computational study demonstrates the power of nanocrystal superlattice engineering and further narrows the gap between the richness of crystal structures found with atoms and in soft matter assemblies.
We address the problem of efficient phase diagram sampling by adopting active learning techniques from machine learning and achieve drastic reduction in the sample size (number of sampled state ...points) needed to establish the phase boundary up to a given precision. Although advanced sampling techniques such as Gibbs ensemble and Gibbs–Duhem integration can sample phase equilibria efficiently, they may fail to generalize to many nonequilbrium systems. This forces researchers to resort to grid search simulations when studying many important active matter systems. Grid search suffers from low efficiency by sampling predetermined state points that provide little information about the phase boundaries. We propose an active learning framework to overcome this deficiency by adaptively choosing the next most informative state point(s) every round. This is done by interpolating the sampled state points’ phases by Gaussian process regression and then using an acquisition function to quantify the informativeness of possible next state points. We generalize our approach with asynchronous sampling techniques to better utilize parallel computing resources and extend the algorithm to incorporate uncertainty information from multiple replicas. We demonstrate the usefulness of our approach in two example systems in soft matter physics: a phase-separated steady-state of a mixture of self-propelled and passive Brownian colloids, and an equilibrium phase boundary between two quasicrystals. We achieve 5 times sample efficiency improvement in the case of the former example while the phase diagram in the latter example has not been studied before with comparable precision. Our active learning enhanced phase diagram sampling method greatly accelerates research and opens up opportunities for extra-large-scale exploration of a wide range of phase diagrams by simulation or experiment.
A theory of entropic bonding Vo, Thi; Glotzer, Sharon C
Proceedings of the National Academy of Sciences - PNAS,
01/2022, Letnik:
119, Številka:
4
Journal Article
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Entropy alone can self-assemble hard nanoparticles into colloidal crystals of remarkable complexity whose structures are the same as atomic and molecular crystals, but with larger lattice spacings. ...Molecular simulation is a powerful tool used extensively to study the self-assembly of ordered phases from disordered fluid phases of atoms, molecules, or nanoparticles. However, it is not yet possible to predict colloidal crystal structures a priori from particle shape as we can for atomic crystals from electronic valency. Here, we present such a first-principles theory. By calculating and minimizing excluded volume within the framework of statistical mechanics, we describe the directional entropic forces that collectively emerge between hard shapes, in familiar terms used to describe chemical bonds. We validate our theory by demonstrating that it predicts thermodynamically preferred structures for four families of hard polyhedra that match, in every instance, previous simulation results. The success of this first-principles approach to entropic colloidal crystal structure prediction furthers fundamental understanding of both entropically driven crystallization and conceptual pictures of bonding in matter.
Predicting structure from the attributes of a material's building blocks remains a challenge and central goal for materials science. Isolating the role of building block shape for self-assembly ...provides insight into the ordering of molecules and the crystallization of colloids, nanoparticles, proteins, and viruses. We investigated 145 convex polyhedra whose assembly arises solely from their anisotropic shape. Our results demonstrate a remarkably high propensity for thermodynamic self-assembly and structural diversity. We show that from simple measures of particle shape and local order in the fluid, the assembly of a given shape into a liquid crystal, plastic crystal, or crystal can be predicted.
Efforts to impart elasticity and multifunctionality in nanocomposites focus mainly on integrating polymeric and nanoscale components. Yet owing to the stochastic emergence and distribution of ...strain-concentrating defects and to the stiffening of nanoscale components at high strains, such composites often possess unpredictable strain-property relationships. Here, by taking inspiration from kirigami—the Japanese art of paper cutting—we show that a network of notches made in rigid nanocomposite and other composite sheets by top-down patterning techniques prevents unpredictable local failure and increases the ultimate strain of the sheets from 4 to 370%. We also show that the sheets' tensile behaviour can be accurately predicted through finite-element modelling. Moreover, in marked contrast to other stretchable conductors, the electrical conductance of the stretchable kirigami sheets is maintained over the entire strain regime, and we demonstrate their use to tune plasma-discharge phenomena. The unique properties of kirigami nanocomposites as plasma electrodes open up a wide range of novel technological solutions for stretchable electronics and optoelectronic devices, among other application possibilities.
We describe a highly optimized implementation of MPI domain decomposition in a GPU-enabled, general-purpose molecular dynamics code, HOOMD-blue (Anderson and Glotzer, 2013). Our approach is inspired ...by a traditional CPU-based code, LAMMPS (Plimpton, 1995), but is implemented within a code that was designed for execution on GPUs from the start (Anderson et al., 2008). The software supports short-ranged pair force and bond force fields and achieves optimal GPU performance using an autotuning algorithm. We are able to demonstrate equivalent or superior scaling on up to 3375 GPUs in Lennard-Jones and dissipative particle dynamics (DPD) simulations of up to 108 million particles. GPUDirect RDMA capabilities in recent GPU generations provide better performance in full double precision calculations. For a representative polymer physics application, HOOMD-blue 1.0 provides an effective GPU vs. CPU node speed-up of 12.5×.
Clathrate colloidal crystals Lin, Haixin; Lee, Sangmin; Sun, Lin ...
Science (American Association for the Advancement of Science),
03/2017, Letnik:
355, Številka:
6328
Journal Article
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DNA-programmable assembly has been used to deliberately synthesize hundreds of different colloidal crystals spanning dozens of symmetries, but the complexity of the achieved structures has so far ...been limited to small unit cells. We assembled DNA-modified triangular bipyramids (~250-nanometer long edge, 177-nanometer short edge) into clathrate architectures. Electron microscopy images revealed that at least three different structures form as large single-domain architectures or as multidomain materials. Ordered assemblies, isostructural to clathrates, were identified with the help of molecular simulations and geometric analysis. These structures are the most sophisticated architectures made via programmable assembly, and their formation can be understood based on the shape of the nanoparticle building blocks and mode of DNA functionalization.
Many butterflies, birds, beetles, and chameleons owe their spectacular colors to the microscopic patterns within their wings, feathers, or skin. When these patterns, or photonic crystals, result in ...the omnidirectional reflection of commensurate wavelengths of light, it is due to a complete photonic band gap (PBG). The number of natural crystal structures known to have a PBG is relatively small, and those within the even smaller subset of notoriety, including diamond and inverse opal, have proven difficult to synthesize. Here, we report more than 150,000 photonic band calculations for thousands of natural crystal templates from which we predict 351 photonic crystal templates - including nearly 300 previously-unreported structures - that can potentially be realized for a multitude of applications and length scales, including several in the visible range via colloidal self-assembly. With this large variety of 3D photonic crystals, we also revisit and discuss oft-used primary design heuristics for PBG materials.
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