What are the common Machine Learning techniques used in Catalysis?
Several machine learning techniques are commonly applied in catalysis research:
- Regression Models: Used to predict continuous outcomes such as reaction rates and activation energies. - Classification Models: Employed to categorize catalysts based on their performance or other properties. - Clustering Algorithms: Help group similar catalysts, which can reveal underlying patterns in catalyst performance. - Neural Networks: Particularly deep learning models, are used for more complex predictions involving non-linear relationships. - Reinforcement Learning: Applied for optimizing reaction conditions and catalyst formulations through iterative learning processes.
