What are the Key Probabilistic Methods Used in Catalysis?
Several probabilistic methods are commonly applied in the field of catalysis:
1. Monte Carlo Simulations: These are used to model the behavior of catalytic systems by randomly sampling from a probability distribution, allowing for the exploration of numerous potential outcomes. 2. Markov Chains: This method helps in modeling the sequence of events in a catalytic process, where the next state depends only on the current state. 3. Bayesian Inference: This approach updates the probability estimates for a catalyst's performance as new data become available, providing a dynamic and iterative method for improving catalytic models. 4. Stochastic Differential Equations: These are used to describe the evolution of catalytic systems with random perturbations, offering insights into how these systems behave over time.
