Several ML techniques are commonly used in catalysis research, including:
Supervised Learning: Involves training a model on a labeled dataset to make predictions or classifications. Unsupervised Learning: Used to identify patterns and relationships in data without labeled outputs. Reinforcement Learning: Involves training a model through trial and error to achieve a specific goal. Neural Networks: A type of deep learning model that can capture complex, non-linear relationships in data.
