Reducing sample complexity can save time and resources. Here are a few strategies:
Machine learning: Utilizing machine learning algorithms to predict catalyst performance can drastically reduce the number of physical experiments needed. High-throughput screening: This technique allows for the rapid testing of a large number of catalyst samples, thus reducing the overall sample complexity. Computational modeling: Advanced computational methods can simulate catalytic reactions, providing valuable insights that reduce the need for extensive experimental data.
