This book describes the mathematical underpinnings of algorithms used for molecular dynamics simulation, including both deterministic and stochastic numerical methods. Molecular dynamics is one of the most versatile and powerful methods of modern computational science and engineering and is used widely in chemistry, physics, materials science and biology. Understanding the foundations of numerical methods means knowing how to select the best one for a given problem (from the wide range of techniques on offer) and how to create new, efficient methods to address particular challenges as they arise in complex applications. Aimed at a broad audience, this book presents the basic theory of Hamiltonian mechanics and stochastic differential equations, as well as topics including symplectic numerical methods, the handling of constraints and rigid bodies, the efficient treatment of Langevin dynamics, thermostats to control the molecular ensemble, multiple time-stepping, and the dissipative particle dynamics method

Description based on publisher-supplied MARC data.

1.Introduction -- 2.Numerical Integrators -- 3.Analyzing Geometric Integrators -- 4.The Stability Threshold -- 5.Phase Space Distributions and Microcanonical Averages -- 6. The Canonical Distribution and Stochastic Differential Equations -- 7. Numerical Methods for Stochastic Molecular Dynamics -- 8. Extended Variable Methods -- References -- Index