An ideal set of CVs should effectively capture the collective, slow motions of proteins over extended timescales . As a result, CVs purely based on geometric variances, such as root mean square distances (RMSD) , principal component analysis (PCA) , or isometric feature mapping (ISOMAP) , are often inaccurate for studying the slow dynamics, as geometric variance does not always align with slow dynamical processes. In contrast, CV-identification methods based on the variational principles of conformational dynamics , including time-lagged Independent Component Analysis (tICA) and kernel-tICA , have gained popularity, as they can identify CVs by directly maximizing the timescales of slow motions. These methods use spectral decomposition to project input features onto the dominant components of slow dynamical modes, which are efficient and numerically stable. However, tICA is limited to linear combinations of input features, which often fail to accurately capture the slow dynamics of large biomolecules where large conformational changes are collective motions of atoms and usually involve nonlinear combinations of features like pairwise distances. Recently, deep-learning-based algorithms have been developed to identify CVs of biomolecular dynamics, such as deep-tICA , the Variational Approach for Markov Processes using neural networks (VAMPnets) , State-free Reversible VAMPnets (SRVs) , and Invariant Subspaces of Koopman Operators learned by A Neural Network (ISOKANN) . These deep-learning-based algorithms use encoders and neural networks to embed input features into a reduced-dimensional CV space, which can involve highly nonlinear combinations of input features. These deep-learning methods can accurately identify CVs for slow dynamical modes, but they often require lengthy training process and are limited by insufficient training datasets.