What are the Key Machine Learning Techniques Used in Catalysis?
Several advanced machine learning techniques are particularly impactful in the field of catalysis:
1. Neural Networks: Used for modeling complex, non-linear relationships between catalytic properties and performance. Deep learning, a subset of neural networks, can handle large datasets and uncover intricate patterns. 2. Support Vector Machines (SVM): Effective for classification and regression tasks in catalysis, helping in the identification of active sites and prediction of reaction outcomes. 3. Random Forests: An ensemble learning method that creates multiple decision trees to improve predictive accuracy and robustness. 4. Gaussian Processes: Useful for modeling uncertainties and making probabilistic predictions about catalyst behavior. 5. Reinforcement Learning: Helps in optimizing catalytic processes by learning from the environment and making sequential decisions to improve outcomes.
