Specifically, we extracted the output from the last residual block of the model as the molecular embedding. Each molecule is represented as a vector of length 158. Using principal component analysis (PCA), the molecular embeddings were projected into 2D space for visualization (Figure ). In the PCA plot, panel A is colored by ring size, and panel B is colored by RSE. Along principal component 1 (PCA 1), the molecules generally show decreasing RSE from left to right; along PCA Utilizing only 2D information allows the GNN to make predictions within milliseconds, making it a practical tool for screening large molecular libraries. Both the ML model and AIMNet2 RSE workflow are publicly available at , and the RSE Atlas is publicly available as an interactive web app at . These methods complement the existing toolbox for computing RSE, enabling high-accuracy and lowcomputational-cost RSE calculations.