Dimensionality reduction is a data analysis technique that simplifies complex datasets by reducing the number of features or variables while retaining essential information. Chemical systems, like molecules, are inherently high-dimensional due to the multitude of properties, descriptors, and interactions involved. For instance, a molecule can be characterized by features such as bond lengths, angles, dihedrals, and even electronic properties. When handling such high -dimensional data, which may include hundreds or thousands of variables, visualizing and analyzing the information effectively becomes challenging. Dimensionality reduction is valuable in two key ways: it helps with data visualization by transforming high-dimensional data into a more manageable format, and it reduces redundancy by removing correlated or redundant features. Reducing redundant features can enhance the efficiency and performance of subsequent data use. Below, we summarize some of the most common dimensionality reduction methods applied to model chemical systems and available in ULaMDyn.