Monte Carlo Methods in Statistical Mechanics Aziz Abdellahi CEDER group Materials Basics Lecture : 08/18/2011 1

What is Monte Carlo ? Famous for its casinos ! Monte Carlo is an administrative area of the principality of Monaco. Monte Carlo is a (large) class of numerical methods used to solve integrals and differential equations using sampling and probabilistic criteria. 2

The simplest Monte Carlo method Finding the value of π (“shooting darts”) π/4 is equal to the area of a circle of diameter Details of the Method Randomly select a large number of points inside the square Integral solved with Monte Carlo (Exact when N_total  ∞) 3

MC : Common features and applications Common features in Monte Carlo methods Uses random numbers and selection criteria Monte Carlo in Statistical Mechanics : Calculating thermodynamic properties of a material from its first-principles Hamiltonian Requires the repetition of a large number of events Monte Carlo method that will be discussed in this talk Example of results obtained from MC : Li x FePO 4 (Li-ion battery cathode) Only consider configurational degrees of freedom (Li-Vacancy) The energies of all Li-Vacancy configurations are known (Hamiltonian) 4

Useful battery properties that can be obtained from Monte Carlo Phase diagram, Voltage profiles Results obtained from Monte Carlo These properties are deduced from the μ(x,T) relation [or alternatively x(μ,T)] Results obtained in the Ceder group (using Monte Carlo) Li x FePO 4 phase diagramVoltage profile (room temperature) 5

How to calculate the partition function ? Key physical quantity : The partition function Finding a numerical approximation to the partition function Monte Carlo strategy : Calculate thermodynamic properties by sampling configurations according to their Boltzmann probability {j} : Set of all possible Li-Vacancy configurations E j : Energy of configuration j N j : Number of Li in configuration j All thermodynamic properties can be computed from the partition function Etc. Control parameters The partition function cannot be calculated directly because the number of configurations scales exponentially with the system size (2 N_sites possible configurations … too hard even for modern computers !). 6

Monte Carlo or “Importance Sampling” Importance sampling : Sample states according to their actual probability Consider the following random variable x : Direct calculation of : Importance sampling : Randomly pick 10 values of x out of a giant hat containing 10% 0’s, 80% 1’s and 10% 2’s. Possible outcome :  The arithmetic average will not always be equal to the average. However, the two become equal in the limit of large “chains”. Importance sampling : Sample states with the correct probability. Works well for very large systems that have heavy probability discrepancies. 7

Monte Carlo or “Importance Sampling” Monte Carlo : Methodology Create a Markov chain of configurations, where each configuration is determined from the previous one using a certain probabilistic criteria C 1  C 2  …  C N_max Choose the probabilistic criteria so that states are asympotically sampled with the equilibrium Boltzmann probability (that is the main difficulty !) Start from an initial configuration C 1 Calculate thermodynamic averages directly through arithmetic averages over the Markov Chain E n : Energy of configuration C n N n : Number of Li in configuration C n 8

Metropolis Algorithm Building the chain : The Metropolis Algorithm Start from an initial configuration C 1 : Change the occupation state of the first Li site :  Calculate E i -μN i (Before the change) and E f -μN f (After the change)  If E f -μN f < E i -μN i, accept the change  If E f -μN f > E i –μN i, accept the change with the probability : Repeat for all other Li sites to get C 2 (Ratio of Boltzmann probabilities…) 9

Metropolis algorithm (3) Why does the Metropolis algorithm work ? The Metropolis algorithm generates a chain Markov consistent with Boltzmann probabilities sampling because the selection criteria has Boltzmann probabilities built into it.  It can be shown that all selection criteria that respect the condition of detailed balance produce correct sampling : Probability of generating configuration j from configuration i Because the most probable configurations are sampled preferentially, good approximations of thermodynamic averages can be obtained by sampling a relatively small number of configurations 10

Conclusion Monte Carlo in Statistical Mechanics Method to approximate thermodynamic properties using clever sampling Other Monte Carlo methods in engineering Good results can be obtained by sampling a relatively small number of configurations (relative to the total number of possible configurations) :  Li x FePO 4 voltage profile : states sampled instead of Kinetic Monte Carlo (to calculate diffusivities) Quantum Monte Carlo (to solve the Schrodinger equation) Monte Carlo in nuclear engineering (to predict the evolution of the neutron population in a nuclear reactor) 11

Questions ? 12