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

Journal of Physics Condensed Matter: Preface

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The Nature Of The Chemical Bond

Trends in Cell Biology

We investigate a class of “shape allophiles” that fit together like puzzle pieces as a method to access and stabilize desired structures by controlling directional entropic forces. Squares are cut into rectangular halves, which are shaped in an allophilic manner with the goal of re-assembling the squares while self-assembling the square lattice. We examine the assembly characteristics of this system via the potential of mean force and torque, and the fraction of particles that entropically bind. We generalize our findings and apply them to self-assemble triangles into a square lattice via allophilic shaping. Through these studies we show how shape allophiles can be useful for assembling and stabilizing desired phases with appropriate allophilic design.

Shape allophiles improve entropic assembly

The freud Python library analyzes particle data output from molecular dynamics simulations. The library’s design and its variety of highperformance methods make it a powerful tool for many modern applications. In particular, freud can be used as part of the data generation pipeline for machine learning (ML) algorithms for analyzing particle simulations, and it can be easily integrated with various simulation visualization tools for simultaneous visualization and real-time analysis. Here, we present numerous examples both of using freud to analyze nano-scale particle systems by coupling traditional simulational analyses to machine learning libraries and of visualizing per-particle quantities calculated by freud analysis methods. We include code and examples of this visualization, showing that in general the introduction of freud into existing ML and visualization workflows is smooth and unintrusive. We demonstrate that among Python packages used in the computational molecular sciences, freud offers a unique set of analysis methods with efficient computations and seamless coupling into powerful data analysis pipelines.

/pdf/analyzing-particle-systems-for-machine-learning-and-data-1dfphuks2p.pdf

Analyzing Particle Systems for Machine Learning and Data Visualization with freud

Hierarchical, self-assembled structures are ordered on multiple scales, and formed by objects comprised of even smaller elements. Such structures are widely reported for nanoparticles, macromolecules, and peptides, and even in entropy-driven hard particle assembly hierarchical colloidal crystals have been reported. Here we consider the hierarchical self-assembly of a cubic colloidal crystal from congruent hard cube derivatives, and investigate how various ways of slicing and dicing a cube can affect the ability of the pieces to entropically re-assemble the initial colloidal crystal formed from perfect cubes. We present design rules that support heuristics reported for different systems, and present evidence for a previously unreported cubatic phase from 2 : 1 rectangular prisms.

Hierarchical self-assembly of hard cube derivatives

harperic/freud: Zenodo DOI release

Optical metamaterials can achieve optical performance specifications otherwise unattainable compared to their bulk constituents via the use of skillfully engineered nanoscale features. These features function as sub-wavelength optical scatterers, causing incident light to interact with metamaterials in a manner which is crucially dependent on their geometry. To design the next generation of functional materials, scientists and engineers must realize new methods which prudently explore the near-infinite number of possible metamaterial arrangements. We present a general methodology which harnesses machine learning to accelerate the process of metamaterial design and apply it specifically here, as a test case, to a silicon on insulator (SOI) reflective metasurface consisting of an array of nano-pillars. In particular, we implement artificial neural networks (ANNs) to solve the inverse design problem; i.e. we prescribe a desired reflection profile and utilize ANNs to advise what metasurface array of SOI nano-pillars gives the closest result. Utilizing a synthetic data set of devices simulated with rigorous coupled wave analysis (RCWA), we create an ANN-accelerated simulator, achieving a computational speedup of O(106) over the relatively quick RCWA simulation method. We then couple this simulator with an ANN-based predictor, which directly binds the SOI metasurfaces in question to a targeted optical performance. Together, this simulator/predictor ANNs combination provides a general framework in rapidly evaluating and designing potential optical metamaterial devices beyond what is currently possible via straightforward simulation and more standard optimization methods.

Eric S. Harper

Papers

Shape allophiles improve entropic assembly

Analyzing Particle Systems for Machine Learning and Data Visualization with freud

Hierarchical self-assembly of hard cube derivatives

harperic/freud: Zenodo DOI release

Machine Accelerated Nano-Targeted Inhomogeneous Structures