Covers the efficient computation of heat capacity in metal organic frameworks using quantum classical methods and explores advanced PIMD techniques beyond benchmarks.
Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.
Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.
