SphereDiff-TS: Sphere Space Diffusion Modeling for Accurate 3D Transition State Geometry Prediction

Abstract

To overcome the high computational expense of conventional quantum chemistry techniques and the limited incorporation of physical constraints in machine learning models, we present SphereDiff-TS: a diffusion-based method for predicting 3D transition state (TS) structures using a spherical coordinate system with flexible boundary and dynamic radius constraints. Evaluated against true transition states, the model achieves chemical accuracy in both geometry (median RMSD: 0.048 Å; median of 0.022 Å on selected cross-system cases) and energy (median absolute error: 0.55 kcal/mol; 0.328 kcal/mol on the same cases). Moreover, comparative analysis with the literature-reported structures confirms that the model accurately reproduces barrier heights, with deviations generally below 1.5 kcal/mol. These results highlight the potential of SphereDiff-TS as a robust computational tool for exploring reaction mechanisms and aiding in computer-driven reaction design.