Sampling the Canonical Ensemble

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Covers simulating and sampling in atomistic computer modelling of materials.

Introduces the concept of a Markov process and chain in configuration sampling.

Covers the theory and practical aspects of Monte Carlo simulations in molecular dynamics, including ensemble averages and Metropolis algorithm.

Explores molecular dynamics sampling, conservation laws, energy fluctuations, and various thermostats used for simulations.

Explores sampling the canonical ensemble, temperature fluctuations, extended Lagrangian, and molecular dynamics control of temperature.

Explores molecular dynamics, integrators, trajectory generation, and error monitoring in atomistic modeling.

Covers the transition from quantum to classical mechanics, statistical mechanics, Monte Carlo simulations, and molecular dynamics simulations.

Explores molecular dynamics simulations under holonomic constraints, focusing on numerical integration and algorithm formulation.

Covers statistical physics, isolated systems, entropy, and the Boltzmann distribution.

Explores Boltzmann distribution, quantum particles, thermodynamic ensembles, and ensemble sampling methods.