Nanoscale simulations often involve complex models with numerous variables. Probabilistic optimization can be integrated with molecular dynamics and Monte Carlo simulations to explore potential configurations and predict the behavior of nanoscale systems under various conditions. By sampling a wide range of possibilities, these methods help in identifying optimal configurations that might be missed by deterministic approaches.
