What Are the Key Machine Learning Techniques Used?
Several ML techniques are particularly useful in catalysis:
Supervised learning: This involves training models on labeled datasets to predict outcomes. In catalysis, supervised learning can be used to predict the activity and selectivity of catalysts based on their properties. Unsupervised learning: Techniques like clustering and dimensionality reduction can help in understanding patterns and correlations in large datasets, useful for exploring relationships between different catalysts and reaction conditions. Reinforcement learning: This can be applied to optimize reaction conditions and catalyst formulations by learning from the outcomes of various experimental setups.
