Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Chemical methods developed over the past two decades enable preparation of colloidal nanocrystals with uniform size and shape. These Brownian objects readily order into superlattices. Recently, the range of accessible inorganic cores and tunable surface chemistries dramatically increased, expanding the set of nanocrystal arrangements experimentally attainable. In this review, we discuss efforts to create next-generation materials via bottom-up organization of nanocrystals with preprogrammed functionality and self-assembly instructions. This process is often driven by both interparticle interactions and the influence of the assembly environment. The introduction provides the reader with a practical overview of nanocrystal synthesis, self-assembly, and superlattice characterization. We then summarize the theory of nanocrystal interactions and examine fundamental principles governing nanocrystal self-assembly from hard and soft particle perspectives borrowed from the comparatively established fields of micro...

Self-Assembly of Colloidal Nanocrystals: From Intricate Structures to Functional Materials

This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

Machine learning with Python

The use of liquid metals based on gallium for soft and stretchable electronics is discussed. This emerging class of electronics is motivated, in part, by the new opportunities that arise from devices that have mechanical properties similar to those encountered in the human experience, such as skin, tissue, textiles, and clothing. These types of electronics (e.g., wearable or implantable electronics, sensors for soft robotics, e-skin) must operate during deformation. Liquid metals are compelling materials for these applications because, in principle, they are infinitely deformable while retaining metallic conductivity. Liquid metals have been used for stretchable wires and interconnects, reconfigurable antennas, soft sensors, self-healing circuits, and conformal electrodes. In contrast to Hg, liquid metals based on gallium have low toxicity and essentially no vapor pressure and are therefore considered safe to handle. Whereas most liquids bead up to minimize surface energy, the presence of a surface oxide on these metals makes it possible to pattern them into useful shapes using a variety of techniques, including fluidic injection and 3D printing. In addition to forming excellent conductors, these metals can be used actively to form memory devices, sensors, and diodes that are completely built from soft materials. The properties of these materials, their applications within soft and stretchable electronics, and future opportunities and challenges are considered.

Stretchable and Soft Electronics using Liquid Metals.

Mechanical metamaterials exhibit properties and functionalities that cannot be realized in conventional materials. Originally, the field focused on achieving unusual (zero or negative) values for familiar mechanical parameters, such as density, Poisson's ratio or compressibility, but more recently, new classes of metamaterials — including shape-morphing, topological and nonlinear metamaterials — have emerged. These materials exhibit exotic functionalities, such as pattern and shape transformations in response to mechanical forces, unidirectional guiding of motion and waves, and reprogrammable stiffness or dissipation. In this Review, we identify the design principles leading to these properties and discuss, in particular, linear and mechanism-based metamaterials (such as origami-based and kirigami-based metamaterials), metamaterials harnessing instabilities and frustration, and topological metamaterials. We conclude by outlining future challenges for the design, creation and conceptualization of advanced mechanical metamaterials.

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Flexible mechanical metamaterials

Networks of notches in nanocomposite sheets prevent unpredictable local failure and increase the ultimate strain of the sheets from 4% to 370% without affecting their electrical conductance.

/pdf/a-kirigami-approach-to-engineering-elasticity-in-3wks8ho3sx.pdf

A kirigami approach to engineering elasticity in nanocomposites through patterned defects

Simple data and workflow management with the signac framework

We present an integrated theory and simulation study of polydisperse polymer grafted nanoparticles in a polymer matrix to demonstrate the effect of polydispersity in graft length on the potential of mean force between the grafted nanoparticles. In dense polymer solutions, increasing polydispersity in graft length reduces the strength of repulsion at contact and weakens the attractive well at intermediate interparticle distances, completely eliminating the latter at high polydispersity index. The reduction in contact repulsion is attributable to polydispersity relieving monomer crowding near the particle surface, especially at high grafting densities. The elimination of the midrange attractive well is attributable to the longer grafts in the polydisperse graft length distribution that introduce longer range steric repulsion and alter the wetting of the grafted layer by matrix chains. Dispersion of the grafted particles is stabilized by increased penetration or wetting of the polydisperse grafted layer by the matrix chains. This work demonstrates that at high grafting densities, polydispersity in graft length can be used to stabilize dispersions of grafted nanoparticles in a polymer matrix at conditions where monodisperse grafts would cause aggregation.

/pdf/polydispersity-for-tuning-the-potential-of-mean-force-4xgyfhqar1.pdf

Polydispersity for tuning the potential of mean force between polymer grafted nanoparticles in a polymer matrix

Starting with the early alchemists, a holy grail of science has been to make desired materials by modifying the attributes of basic building blocks. Building blocks that show promise for assembling new complex materials can be synthesized at the nanoscale with attributes that would astonish the ancient alchemists in their versatility. However, this versatility means that making a direct connection between building-block attributes and bulk structure is both necessary for rationally engineering materials and difficult because building block attributes can be altered in many ways. Here we show how to exploit the malleability of the valence of colloidal nanoparticle “elements” to directly and quantitatively link building-block attributes to bulk structure through a statistical thermodynamic framework we term “digital alchemy”. We use this framework to optimize building blocks for a given target structure and to determine which building-block attributes are most important to control for self-assembly, through...

Digital Alchemy for Materials Design: Colloids and Beyond.

Since the 1920s, packing arguments have been used to rationalize crystal structures in systems ranging from atomic mixtures to colloidal crystals. Packing arguments have recently been applied to complex nanoparticle structures, where they often, but not always, work. We examine when, if ever, packing is a causal mechanism in hard particle approximations of colloidal crystals. We investigate three crystal structures composed of their ideal packing shapes. We show that, contrary to expectations, the ordering mechanism cannot be packing, even when the thermodynamically self-assembled structure is the same as that of the densest packing. We also show that the best particle shapes for hard particle colloidal crystals at any finite pressure are imperfect versions of the ideal packing shape.

https://www.pnas.org/content/pnas/115/7/1439.full.pdf

Paul M. Dodd

Papers

A kirigami approach to engineering elasticity in nanocomposites through patterned defects

Simple data and workflow management with the signac framework

Polydispersity for tuning the potential of mean force between polymer grafted nanoparticles in a polymer matrix

Digital Alchemy for Materials Design: Colloids and Beyond.

Relevance of packing to colloidal self-assembly.