Several computational techniques are employed for pattern recognition in catalysis:
Machine Learning: Algorithms like support vector machines (SVM), random forests, and k-nearest neighbors (k-NN) are used to classify and predict catalytic performance based on input features. Neural Networks: Deep learning models can capture complex, non-linear relationships in catalytic data, making them suitable for high-dimensional data sets. Principal Component Analysis (PCA): PCA reduces the dimensionality of data, making it easier to visualize and interpret key patterns. Clustering: Techniques like k-means clustering group similar data points, helping to identify clusters of catalysts with similar properties. Regression Analysis: Linear and non-linear regression models are used to quantify relationships between variables and predict outcomes.