https://www.sandia.gov/~sjplimp/papers/jcompphys95.pdf

Fast parallel algorithms for short-range molecular dynamics

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.

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

/pdf/scalable-molecular-dynamics-with-namd-3j7xyc70g4.pdf

Scalable Molecular Dynamics with NAMD

http://kth.diva-portal.org/smash/get/diva2:1303357/FULLTEXT01

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

https://cfwebprod.sandia.gov/cfdocs/CompResearch/docs/scc_code.pdf

Finding strongly connected components in distributed graphs

The adhesion between a glassy polymer melt and substrate is studied in the presence of end-grafted chains chemically attached to the substrate surface. Extensive molecular dynamics simulations have been carried out to study the effect of the areal density ∑ of tethered chains and tensile pull velocity v on the adhesive failure mechanisms. The initial configurations are generated using a double-bridging algorithm in which new bonds are formed across a pair of monomers equidistant from their respective free ends. This generates new chain configurations that are substantially different than the original two chains such that the systems can be equilibrated in a reasonable amount of cpu time. At the slowest tensile pull velocity studied, a crossover from chain scission to crazing is observed as the coverage increases, while for very large pull velocity, only chain scission is observed. As the coverage increases, the sections of the tethered chains pulled out from the interface form the fibrils of a craze that are strong enough to suppress chain scission, resulting in cohesive rather than adhesive failure. © 2003 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 42: 199–208, 2004

https://onlinelibrary.wiley.com/doi/pdf/10.1002/polb.10672

Effect of end-tethered polymers on surface adhesion of glassy polymers

Evaporation and condensation at a liquid/vapor interface are ubiquitous interphase mass and energy transfer phenomena that are still not well understood. We have carried out large scale molecular dynamics simulations of Lennard-Jones (LJ) fluids composed of monomers, dimers, or trimers to investigate these processes with molecular detail. For LJ monomers in contact with a vacuum, the evaporation rate is found to be very high with significant evaporative cooling and an accompanying density gradient in the liquid domain near the liquid/vapor interface. Increasing the chain length to just dimers significantly reduces the evaporation rate. We confirm that mechanical equilibrium plays a key role in determining the evaporation rate and the density and temperature profiles across the liquid/vapor interface. The velocity distributions of evaporated molecules and the evaporation and condensation coefficients are measured and compared to the predictions of an existing model based on kinetic theory of gases. Our results indicate that for both monatomic and polyatomic molecules, the evaporation and condensation coefficients are equal when systems are not far from equilibrium and smaller than one, and decrease with increasing temperature. For the same reduced temperature $T/T_c$, where $T_c$ is the critical temperature, these two coefficients are higher for LJ dimers and trimers than for monomers, in contrast to the traditional viewpoint that they are close to unity for monatomic molecules and decrease for polyatomic molecules. Furthermore, data for the two coefficients collapse onto a master curve when plotted against a translational length ratio between the liquid and vapor phase.

/pdf/evaporation-of-lennard-jones-fluids-2xc38gjql9.pdf

Evaporation of Lennard-Jones Fluids

Biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. In addition, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.

/pdf/a-historical-survey-of-algorithms-and-hardware-architectures-49fvl4uzc2.pdf

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

The aggregation of liquid crystal nanodroplets from a homogeneous solution is studied by molecular dynamics simulations. The liquid crystal particles are modeled as elongated ellipsoidal Gay-Berne particles while the solvent is modeled as spherical Lennard-Jones particles. Extending previous studies of Berardi et al. [J. Chem. Phys. 126, 044905 (2007)], we find that liquid crystal nanodroplets are not stable and that after sufficiently long times the nanodroplets always aggregate into a single large droplet. Results describing the droplet shape and orientation for different temperatures and shear rates are presented. The implementation of the Gay-Berne potential for biaxial ellipsoidal particles in a parallel molecular dynamics code is also briefly discussed.

Steven J. Plimpton

Papers

Finding strongly connected components in distributed graphs

Effect of end-tethered polymers on surface adhesion of glassy polymers

Evaporation of Lennard-Jones Fluids

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

Liquid crystal nanodroplets in solution