Several ML algorithms are particularly useful in the field of catalysis. Some of the most commonly used include:
- Linear Regression: Used for predicting reaction outcomes based on a set of input variables. - Support Vector Machines (SVM): Effective for classification and regression tasks in catalytic data. - Neural Networks: Particularly useful for handling complex, non-linear relationships in large datasets. - Random Forests: Excellent for classification and regression, providing insights into feature importance. - K-Nearest Neighbors (KNN): Useful for classification and regression based on similarity measures.
