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

The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods based on the principles of quantum mechanics (QM), while offering valuable theoretical guidance at the electronic level, are often too computationally intense for simulations that consider the full dynamic evolution of a system. Alternatively, empirical interatomic potentials that are based on classical principles require significantly fewer computational resources, which enables simulations to better describe dynamic processes over longer timeframes and on larger scales. Such methods, however, typically require a predefined connectivity between atoms, precluding simulations that involve reactive events. The ReaxFF method was developed to help bridge this gap. Approaching the gap from the classical side, ReaxFF casts the empirical interatomic potential within a bond-order formalism, thus implicitly describing chemical bonding without expensive QM calculations. This article provides an overview of the development, application, and future directions of the ReaxFF method.

https://biblio.ugent.be/publication/8514189/file/8514192.pdf

The ReaxFF reactive force-field: development, applications and future directions

The fact that a new text book introducing the essentials of computational chemistry contains more than 500 pages shows impressively the grown and still growing size and importance of this field of chemistry. The author’s objectives of the book, using his own words, are “to provide a survey of computational chemistry its underpinnings, its jargon, its strengths and weaknesses that will be accessible to both the experimental and theoretical communities”. This design as a general introduction into computational chemistry makes it an alternative to Andrew R. Leach’s well-established “Molecular Modeling” (Prentice Hall) and Frank Jensen’s “Introduction to Computational Chemistry” (Wiley), although the latter focuses on the theory of electronic structure methods. Cramer’s “Essentials” covers force field and molecular orbital theory, Monte Carlo and Molecular Dynamics simulations, thermodynamic and electronic (spectroscopic) property calculation, condensed phase treatment and a few more topics. Moreover, the book contains thirteen selected case studies sexamples taken from the literature sto illustrate the application of the just presented theoretical and computational models. This especially makes the text book well suited for both classroom discussion and self-study. Each chapter of “Essentials” covers a main topic of computational chemistry and will be briefly described here; all chapters are ended by a bibliography and suggested additional readings as well as the literature references cited in the text. In chapter 1 the author defines basic terms such as “theory”, “model”, and “computation”, introduces the concept of the potential energy surface and provides some general considerations about hardware and software. Interestingly, the first equation occurring in the text is not Schro ̈dinger’s equation, as is the case for most computational chemistry introductions, but the famous Einstein relation. The second chapter deals with molecular mechanics. It explains the different potential energy contributions, introduces the field of structure optimization, and provides an overview of the variety of modern force fields. Chapter 3 covers the simulation of molecular ensembles. It defines phase space and trajectories and shows the formalism of, and problems and difference between, Monte Carlo and molecular dynamics. In chapter 4 the author introduces the foundations of molecular orbital theory. Basic concepts such as Hamilton operator, LCAO basis set approach, many-electron wave functions, etc. are explained. To illuminate the LCAO variational process, the Hu ̈ckel theory is presented with an example. Chapter 5 deals with semiempirical molecular orbital (MO) theory. Besides the classical approaches (extended Hu ̈ckel, CNDO, INDO, NDDO) and methods (e.g., MNDO, AM1, PM3) and their performance, examples are provided from the ongoing development in that still fascinating area. Ab initio MO theory is presented in chapter 6; the basis set concept is discussed in detail, and, after some considerations from an user’s point of view, the general performance of ab initio methods is explicated. The next chapter covers the problem of electron correlation and gives the most prominent solutions for its treatment: configuration interaction, theory of the multiconfiguration self-consistent field, perturbation, and coupled cluster. Practical issues are also discussed. Chapter 8’s topic is density functional theory (DFT). Its theoretical foundation, methodology, and some functionals as well as its pros and cons compared to MO theory are presented together with a general performance overview. The next two chapters deal with charge distribution, derived and spectroscopic properties (e.g., atomic charges, polarizability, rotational, vibrational, and NMR spectra), and thermodynamic properties (e.g., zero-point vibrational energy, free energy of formation, and reaction). The modeling of condensed phases is addressed in chapters 11 (implicit models) and 12 (explicit models), which closes with a comparison between the two approaches. Chapter 13 familiarizes the reader with hybrid quantum mechanical/molecular mechanical (QM/MM) models. Polarization as well as the problematic implications of unsaturated QM and MM components are discussed, and empirical valence bond methods are also presented. The treatment of excited states is the topic of chapter 14; besides CI and MCSCF as computational methods, transition probabilities and solvatochromism are discussed. The last chapter deals with reaction dynamics, mostly adiabaticskinetics, rate constants, reaction paths, and transition state theory are section topics here sbut also nonadiabatic, introducing curve crossing and Marcus theory in brief. The appendix is divided into four parts: an acronym glossary (which is very helpful), an overview of symmetry and group theory, an introduction to spin algebra, and finally a section about orbital localization. A rather detailed index ends the book. The “Essentials” writing style fits the fascinating topic: one reads on and on andssurprise! sanother chapter has been absorbed. The text is complemented by a large number of black and white figures and clear tables, mostly self-explanatory with descriptive captions. The use of equations and mathematical formulas in general is well-balanced, and the level of math should be understandable for every natural scientist with some basic knowledge of physics. There are only a few minor shortcomings: for example, a literature reference cited in the text (“Beck et al.”, p 142) is missing in the bibliography; “Kronecker” is mistyped with o ̈; and the author completely forgot to reference Leach’s text book when referring to other computational chemistry introductions. However, the author has established a specific errata web page (http://pollux.chem.umn.edu/ ∼cramer/Errors.html) with all known errors. These will be corrected in the next printing or next revised edition, respectively. With its emphasis, on one hand, on the basic concepts and applications rather than pure theory and mathematics, and on the other hand, coverage of quantum mechanical and classical mechanical models including examples from inorganic, organic, and biological chemistry, “Essentials” is a useful tool not only for teaching and learning but also as a quick reference, and thus will most probably become one of the standard text books for computational chemistry.

Essentials Of Computational Chemistry Theories And Models

The tunneling current from a scanning tunneling microscope was used to image and dissociate single O{sub 2} molecules on the Pt(111) surface in the temperature range of 40 to 150K. After dissociation, the two oxygen atoms are found one to three lattice constants apart. The dissociation rate as a function of current was found to vary as I{sup 0.8{plus_minus}0.2} , I{sup 1.8{plus_minus}0.2} , and I{sup 2.9{plus_minus}0.3} for sample biases of 0.4, 0.3, and 0.2V, respectively. These rates are explained using a general model for dissociation induced by intramolecular vibrational excitations via resonant inelastic electron tunneling. {copyright} {ital 1997} {ital The American Physical Society}

Single Molecule Dissociation by Tunneling Electrons

A Guide to Monte Carlo Simulations in Statistical Physics: Reweighting methods

Accurate modeling of high-temperature hypersonic flows in the atmosphere requires consideration of collision-induced dissociation of molecular species and energy transfer between the translational and internal modes of the gas molecules. Here, we describe a study of the N2 + N2⟶N2 + 2N and N2 + N2⟶4N nitrogen dissociation reactions using the quasiclassical trajectory (QCT) method. The simulations used a new potential energy surface for the N4 system; the surface is an improved version of one that was presented previously. In the QCT calculations, initial conditions were determined based on a two-temperature model that approximately separates the translational-rotational temperature from the vibrational temperature of the N2 diatoms. Five values from 8000 K to 30,000 K were considered for each of the two temperatures. Over 2.4 × 10(9) trajectories were calculated. We present results for ensemble-averaged dissociation rate constants as functions of the translational-rotational temperature T and the vibrational temperature T(v). The rate constant depends more strongly on T when T(v) is low, and it depends more strongly on T(v) when T is low. Quasibound reactant states contribute significantly to the rate constants, as do exchange processes at higher temperatures. We discuss two sets of runs in detail: an equilibrium test set in which T = T(v) and a nonequilibrium test set in which T(v) < T. In the equilibrium test set, high-v and moderately-low-j molecules contribute most significantly to the overall dissociation rate, and this state specificity becomes stronger as the temperature decreases. Dissociating trajectories tend to result in a major loss of vibrational energy and a minor loss of rotational energy. In the nonequilibrium test set, as T(v) decreases while T is fixed, higher-j molecules contribute more significantly to the dissociation rate, dissociating trajectories tend to result in a greater rotational energy loss, and the dissociation probability's dependence on v weakens. In this way, as T(v) decreases, rotational energy appears to compensate for the decline in average vibrational energy in promoting dissociation. In both the equilibrium and nonequilibrium test sets, in every case, the average total internal energy loss in the dissociating trajectories is between 10.2 and 11.0 eV, slightly larger than the equilibrium potential energy change of N2 dissociation.

An improved potential energy surface and multi-temperature quasiclassical trajectory calculations of N2 + N2 dissociation reactions

A hybrid particle-continuum method applied to shock waves

A modular particle-continuum numerical method for hypersonic non-equilibrium gas flows

The direct molecular simulation (DMS) approach is used to predict the internal energy relaxation and dissociation dynamics of high-temperature nitrogen. An ab initio potential energy surface (PES) is used to calculate the dynamics of two interacting nitrogen molecules by providing forces between the four atoms. In the near-equilibrium limit, it is shown that DMS reproduces the results obtained from well-established quasiclassical trajectory (QCT) analysis, verifying the validity of the approach. DMS is used to predict the vibrational relaxation time constant for N2–N2 collisions and its temperature dependence, which are in close agreement with existing experiments and theory. Using both QCT and DMS with the same PES, we find that dissociation significantly depletes the upper vibrational energy levels. As a result, across a wide temperature range, the dissociation rate is found to be approximately 4–5 times lower compared to the rates computed using QCT with Boltzmann energy distributions. DMS calculations predict a quasi-steady-state distribution of rotational and vibrational energies in which the rate of depletion of high-energy states due to dissociation is balanced by their rate of repopulation due to collisional processes. The DMS approach simulates the evolution of internal energy distributions and their coupling to dissociation without the need to precompute rates or cross sections for all possible energy transitions. These benchmark results could be used to develop new computational fluid dynamics models for high-enthalpy flow applications.

Direct molecular simulation of nitrogen dissociation based on an ab initio potential energy surface

This current and comprehensive book provides an updated treatment of molecular gas dynamics topics for aerospace engineers, or anyone researching high-temperature gas flows for hypersonic vehicles and propulsion systems. It demonstrates how the areas of quantum mechanics, kinetic theory, and statistical mechanics can combine in order to facilitate the study of nonequilibrium processes of internal energy relaxation and chemistry. All of these theoretical ideas are used to explain the direct simulation Monte Carlo (DSMC) method, a numerical technique based on molecular simulation. Because this text provides comprehensive coverage of the physical models available for use in the DSMC method, in addition to the equations and algorithms required to implement the DSMC numerical method, readers will learn to solve nonequilibrium flow problems and perform computer simulations, and obtain a more complete understanding of various physical modeling options for DSMC than is available in other texts.

Thomas E. Schwartzentruber

Papers

An improved potential energy surface and multi-temperature quasiclassical trajectory calculations of N2 + N2 dissociation reactions

A hybrid particle-continuum method applied to shock waves

A modular particle-continuum numerical method for hypersonic non-equilibrium gas flows

Direct molecular simulation of nitrogen dissociation based on an ab initio potential energy surface

Nonequilibrium Gas Dynamics and Molecular Simulation