Several strategies can be employed to achieve data minimization in catalysis:
Feature Selection: Identify and use only the most relevant features or variables that significantly impact the catalytic process. Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) can be used to reduce the number of variables while retaining most of the original information. Experimental Design: Employ statistical methods such as Design of Experiments (DoE) to systematically plan experiments and reduce the number of trials needed. Machine Learning Models: Use predictive models to identify the most influential factors and eliminate redundant data.
