Monte Carlo Simulation: Lennard-Jones Liquid

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

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

Explores Monte Carlo moves in simulation, including trial moves and biased moves, comparing Monte Carlo with Molecular Dynamics.

Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Covers Monte Carlo integration through correlated sampling and error scaling for grid integration.

Explores the efficiency of sampling in molecular dynamics, focusing on ergodicity and autocorrelation functions.

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

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

Covers ensembles, partition functions, and distributions in statistical thermodynamics.

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