cover story in JCTC

Our recent work, which is entitled "Machine learning deciphered molecular mechanistics with accurate kinetic and thermodynamic prediction", has been published in ACS Journal of Chemical Theory and Computation as a cover strory. In this work, an integrated unsupervised dimension reduction model: uniform manifold approximation and projection (UMAP) with hierarchy density-based spatial clustering of applications with noise (HDBSCAN) is implemented, demonstrating robustness in preserving global and local data structures compared to traditional dimension reduction methods in the field of MD analysis area