Computational statistical physics
- Cambridge : Cambridge University Press, 2021
- xiii, 257 p. ; ill., 27 cm
Includes bibliographical reference and index.
Providing a detailed and pedagogical account of the rapidly-growing field of computational statistical physics, this book covers both the theoretical foundations of equilibrium and non-equilibrium statistical physics, and also modern, computational applications such as percolation, random walks, magnetic systems, machine learning dynamics, and spreading processes on complex networks. A detailed discussion of molecular dynamics simulations is also included, a topic of great importance in biophysics and physical chemistry. The accessible and self-contained approach adopted by the authors makes this book suitable for teaching courses at graduate level, and numerous worked examples and end of chapter problems allow students to test their progress and understanding.
9781108841429
Statistical physics Adaptive Particle-Particle-Particle-Mesh(AP3M) method Amadahi's law Born rule Car-Parrinello method Convolution theorem Markov chain Equipartition theorem Flat histogram method Generating function Hooke's law Ising model Karteleyn-Fortuin theorem Lubachevsky method Many- body wave function n-vector model Partition function Reaction-field method Sandbox method Signorini problem TC model Wang-Landau method XOR function