To estimate the thermodynamic and kinetic properties (P) of the obtained CRN blueprint, a fast energy regressor algorithm is needed. In CARE, this task is done by GAME-Net-UQ, a graph neural network (GNN) targeting the DFT energy of adsorbed species S and the transitions states linking them. Originally, GAME-Net was designed to predict the energy of closed-shell molecules in gas phase and adsorbed on the closed-packed surface of 14 transition metal surfaces. The upgraded GAME-Net-UQ model includes three crucial advancements: (i) the extension to open-shell species, (ii) the estimation of transition state (TS) energy and (iii) the implementation of uncertainty quantification (UQ) for the predictions. Including these features keeps GAME-Net-UQ lightweight (558k parameters, roughly twice the original GAME-Net), accurate, and robust, enabling extensive direct energy evaluations at minimal computational cost compared to state-ofthe-art ML potentials containing up to 10 7-8 parameters .