Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.
Explores challenges in identifying useful metastable materials and discusses concepts like structure predictions, ensemble probabilities, and mapping algorithms.
Covers the basics of molecular dynamics simulations, ensemble properties, classical mechanics formulations, numerical integration, energy conservation, and constraint algorithms.
Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.
