Why is Bayesian Optimization Useful in Nanotechnology?
Nanotechnology research often involves complex experiments and simulations that are resource-intensive. Traditional optimization methods can be inefficient and time-consuming in this context. Bayesian optimization provides a more efficient approach by predicting the most promising areas of the search space, thus minimizing the number of required experiments. This leads to faster and more cost-effective discovery processes in nanotechnology.
