Automated optimization typically involves several steps:
Data Collection: Gathering experimental and theoretical data on various nanomaterials and their properties. Modeling: Developing computational models that predict the behavior of nanomaterials under different conditions. Optimization Algorithms: Using algorithms such as genetic algorithms, particle swarm optimization, and Bayesian optimization to find the best parameters for desired outcomes. Validation: Experimentally verifying the computational predictions to ensure they are accurate.
