Automated experimentation typically involves several interconnected components:
- Robotic Systems: These are used to handle materials, perform synthesis, and execute experimental protocols with high precision. - AI and ML Algorithms: These algorithms analyze data in real-time, optimize experimental conditions, and even predict outcomes. - High-Throughput Screening: This technique allows for the rapid testing of a large number of samples, helping researchers identify promising nanomaterials more efficiently. - Data Management Systems: These systems collect, store, and analyze vast amounts of data generated during experiments.
