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

A new algorithm is presented for obtaining points on a steepest descent path from the transition state of the reactants and products. In mass‐weighted coordinates, this path corresponds to the intrinsic reaction coordinate. Points on the reaction path are found by constrained optimizations involving all internal degrees of freedom of the molecule. The points are optimized so that the segment of the reaction path between any two adjacent points is given by an arc of a circle, and so that the gradient at each point is tangent to the path. Only the transition vector and the energy gradients are needed to construct the path. The resulting path is continuous, differentiable and piecewise quadratic. In the limit of small step size, the present algorithm is shown to take a step with the correct tangent vector and curvature vector; hence, it is a second order algorithm. The method has been tested on the following reactions: HCN→CNH, SiH2+H2→SiH4, CH4+H→CH3+H2, F−+CH3F→FCH3+F−, and C2H5F→C2H4+HF. Reaction paths calculated with a step size of 0.4 a.u. are almost identical to those computed with a step size of 0.1 a.u. or smaller.

An improved algorithm for reaction path following

I. Introduction: Conceptual vs Fundamental andComputational Aspects of DFT1793II. Fundamental and Computational Aspects of DFT 1795A. The Basics of DFT: The Hohenberg−KohnTheorems1795B. DFT as a Tool for Calculating Atomic andMolecular Properties: The Kohn−ShamEquations1796C. Electronic Chemical Potential andElectronegativity: Bridging Computational andConceptual DFT1797III. DFT-Based Concepts and Principles 1798A. General Scheme: Nalewajski’s ChargeSensitivity Analysis1798B. Concepts and Their Calculation 18001. Electronegativity and the ElectronicChemical Potential18002. Global Hardness and Softness 18023. The Electronic Fukui Function, LocalSoftness, and Softness Kernel18074. Local Hardness and Hardness Kernel 18135. The Molecular Shape FunctionsSimilarity 18146. The Nuclear Fukui Function and ItsDerivatives18167. Spin-Polarized Generalizations 18198. Solvent Effects 18209. Time Evolution of Reactivity Indices 1821C. Principles 18221. Sanderson’s Electronegativity EqualizationPrinciple18222. Pearson’s Hard and Soft Acids andBases Principle18253. The Maximum Hardness Principle 1829IV. Applications 1833A. Atoms and Functional Groups 1833B. Molecular Properties 18381. Dipole Moment, Hardness, Softness, andRelated Properties18382. Conformation 18403. Aromaticity 1840C. Reactivity 18421. Introduction 18422. Comparison of Intramolecular ReactivitySequences18443. Comparison of Intermolecular ReactivitySequences18494. Excited States 1857D. Clusters and Catalysis 1858V. Conclusions 1860VI. Glossary of Most Important Symbols andAcronyms1860VII. Acknowledgments 1861VIII. Note Added in Proof 1862IX. References 1865

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Conceptual density functional theory.

A potential function is presented that can be used to model both chemical reactions and intermolecular interactions in condensed-phase hydrocarbon systems such as liquids, graphite, and polymers. This potential is derived from a well-known dissociable hydrocarbon force field, the reactive empirical bond-order potential. The extensions include an adaptive treatment of the nonbonded and dihedral-angle interactions, which still allows for covalent bonding interactions. Torsional potentials are introduced via a novel interaction potential that does not require a fixed hybridization state. The resulting model is intended as a first step towards a transferable, empirical potential capable of simulating chemical reactions in a variety of environments. The current implementation has been validated against structural and energetic properties of both gaseous and liquid hydrocarbons, and is expected to prove useful in simulations of hydrocarbon liquids, thin films, and other saturated hydrocarbon systems.

https://www.usna.edu/Users/chemistry/jah/_files/Papers/AIREBO_JCPversion.pdf

A reactive potential for hydrocarbons with intermolecular interactions

1 Introduction 2 Vibrational/Rotational Energy Levels 3 Potential Energy Surfaces 4 State Preparation and Intramolecular Vibrational Energy Redistribution 5 Experimental Methods in Unimolecular Dissociation Studies 6 Theory of Unimolecular Decomposition - The Statistical Approach 8 Dynamical Approaches 9 Product Energy Distributions 10 The Dissociation fo Small and Large Clusters Appendix

Unimolecular reaction dynamics : theory and experiments

The general chemical dynamics computer program VENUS is used to perform classical trajectory simulations for large polyatomic systems, with many atoms and complicated potential energy functions. To simulate an ensemble of many trajectories requires a large amount of CPU time. Since each trajectory is independent, it is possible to parallel process a large set of trajectories instead of processing the trajectories by the conventional sequential approach. This enhances the vectorizability of the VENUS program, since the integration of Hamilton's equations of motion and the gradient evaluation, which comprise 97.8% of the CPU, can each be parallel processed. In this article, the vectorization and ensuing optimization of VENUS on the CRAY‐YMP and IBM‐3090 are presented in terms of both global strategies and technical details. A switching algorithm is designed to enhance the vector performance and to minimize the memory storage. A performance of 140 MFLOPS and a vector/scalar execution rate ratio of 10.6 are observed when this new version of VENUS is used to study the association of CH3 with the H(Ar)12 cluster on the CRAY‐YMP.

Vectorization of the general Monte Carlo classical trajectory program VENUS

Chemical dynamics trajectory simulations were used to study the atomic-level mechanisms of the OH- + CH3F --> CH3OH + F- SN2 nucleophilic substitution reaction. The reaction dynamics, from the [OH...CH3...F]- central barrier to the reaction products, are simulated by ab initio direct dynamics. The reaction's potential energy surface has a deep minimum in the product exit channel arising from the CH3OH...F- hydrogen-bonded complex. Statistical theories of unimolecular reaction rates assume that the reactive system becomes trapped in this minimum and forms an intermediate, with random redistribution of its vibrational energy, but the majority of the trajectories (90%) avoided this potential energy minimum and instead dissociated directly to products. This finding is discussed in terms of intramolecular vibrational energy redistribution (IVR) and the relation between IVR and molecular structure. The finding of this study may be applicable to other reactive systems where there is a hierarchy of time scales for intramolecular motions and thus inefficient IVR.

https://science.sciencemag.org/content/sci/296/5569/875.full.pdf

A SN2 Reaction That Avoids Its Deep Potential Energy Minimum

Monte Carlo rate constants for model CH3NC isomerization, determined at 200, 100, and 70 kcal/mole, disagree with theoretical predictions. Also, three different approximate methods of generating initial conditions at 200 kcal lead to divergent results. The molecule does not appear to us to obey the random lifetime assumption of conventional unimolecular rate theory at any of these energies. A discussion is given of the systematics of this kind of effect, and comments are made on the relationship between our results and those obtained in the laboratory.

On non‐RRKM unimolecular kinetics: Molecules in general, and CH3NC in particular

Anion-molecule nucleophilic substitution (SN2) reactions are known for their rich reaction dynamics, caused by a complex potential energy surface with a submerged barrier and by weak coupling of the relevant rotational-vibrational quantum states. The dynamics of the SN2 reaction of Cl– + CH3I were uncovered in detail by using crossed molecular beam imaging. As a function of the collision energy, the transition from a complex-mediated reaction mechanism to direct backward scattering of the I– product was observed experimentally. Chemical dynamics calculations were performed that explain the observed energy transfer and reveal an indirect roundabout reaction mechanism involving CH3 rotation.

https://science.sciencemag.org/content/sci/319/5860/183.full.pdf

William L. Hase

Papers

Unimolecular reaction dynamics : theory and experiments

Vectorization of the general Monte Carlo classical trajectory program VENUS

A SN2 Reaction That Avoids Its Deep Potential Energy Minimum

On non‐RRKM unimolecular kinetics: Molecules in general, and CH3NC in particular

Imaging nucleophilic substitution dynamics.