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

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

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

Colloidal particles may be considered as building blocks for materials, just like atoms are the bricks of molecules, macromolecules, and crystals. Periodic arrays of colloids (colloidal crystals) have attracted much interest over the last two decades, largely because of their unique photonic properties. The archetype opal structures are based on close-packed arrays of spheres of submicrometer diameter. Interest in structuring materials at this length scale, but with more complex features and ideally by self-assembly processes, has led to much progress in controlling features of both building blocks and assemblies. The necessary ingredients include colloids, colloidal clusters, and colloidal "molecules" which have special shapes and the ability to bind directionally, the control over short-range and long-range interactions, and the capability to place and orientate these bricks. This Review highlights recent experimental and theoretical progress in the assembly of colloids larger than 50 nm.

Colloidal Assembly: The Road from Particles to Colloidal Molecules and Crystals

Reconfigurability of two-dimensional colloidal crystal structures assembled by anisometric particles capable of changing their shape were studied by molecular dynamics computer simulation. We show that when particles change shape on cue, the assembled structures reconfigure into different ordered structures, structures with improved order, or more densely packed disordered structures, on faster time scales than can be achieved via self-assembly from an initially disordered arrangement. These results suggest that reconfigurable building blocks can be used to assemble reconfigurable materials, as well as to assemble structures not possible otherwise, and that shape shifting could be a promising mechanism to engineer assembly pathways to ordered and disordered structures.

Self-assembly and reconfigurability of shape-shifting particles.

http://glotzerlab.engin.umich.edu/home/publications-pdfs/2013/JCPhys_254.pdf

Massively parallel Monte Carlo for many-particle simulations on GPUs

gdb is intended for C, C++ and FORTRAN programs. To use gdb effectively, you should compile all programs using the “-g” option, to include program symbolic names in the compiler output. When this is done, gdb will be able to list lines in your source program, find the start of each procedure or method in your program, and find and print program variables. gdb will also know the declared type of each variable, and can print the variables in a useful format, based on this knowledge.

Debugging with gdb

The efficiency of bulk heterojunction solar cells depends strongly on the morphology of the electron donors (conjugated polymers) and electron acceptors (fullerene derivatives) in the active layer. The features of the donor–acceptor morphology (e.g., domain shapes, crystalline/amorphous domains, and donor–acceptor interface) can in principle be tuned by choosing the chemistry and architecture of the conjugated polymer, fullerene functional group, solvents, and processing conditions (e.g., annealing temperature). Here, we present a high-throughput coarse-grained simulation study that links molecular-level design parameters to features in the assembled morphology in neat polymers and donor–acceptor blends. These models reproduce neat polymer morphologies observed in experiments, such as lamellae, hexagonally packed cylinders, and acceptor intercalation among donor side chains. Furthermore, for blends of conjugated polymers and fullerene derivatives, this study shows how conjugated polymer architecture and a...

Computationally Linking Molecular Features of Conjugated Polymers and Fullerene Derivatives to Bulk Heterojunction Morphology

Self-assembly holds promise for creating new materials and devices because of its inherent parallelism, allowing many building blocks to simultaneously organize using preprogrammed interactions. An important trend in nanoparticle and colloid science is the synthesis of particles with unusual shapes and/or directional (“patchy”) interactions, whose anisotropy allows, in principle, assemblies of unprecedented complexity. However, patchy particles are more prone to long relaxation times during thermodynamically driven assembly, and there is no a priori way of predicting which particles might be good assembly candidates. Here we demonstrate a new conceptual approach to predict this information using sequences of intermediate clusters that appear during assembly. We demonstrate our approach on a family of model building blocks as well as a real system of CdTe/CdS tetrahedra and find design rules for engineering the optimized assembly of target structures.

/pdf/screening-and-designing-patchy-particles-for-optimized-self-3b1pfn4gd6.pdf

Eric Jankowski

Papers

Self-assembly and reconfigurability of shape-shifting particles.

Massively parallel Monte Carlo for many-particle simulations on GPUs

Debugging with gdb

Computationally Linking Molecular Features of Conjugated Polymers and Fullerene Derivatives to Bulk Heterojunction Morphology

Screening and designing patchy particles for optimized self-assembly propensity through assembly pathway engineering