The strong dependence of the StS dissociation rates on the energy deficit from the centrifugal barrier has been discussed extensively by Venturi et al. , and it is shown in Fig. . However, this relation has yet to be formulated analytically, and a FNN is adopted here to represent this unknown dependency. By analyzing the problem from a classical perspective, the molecular bond can be broken either because of an excess in energy or because of an excess in bond length. Consequently, we represent these physical dependencies by selecting as input features ∆E CB (i.e., the energy deficit from the centrifugal barrier) together with r max and r o (i.e., the J-dependent location of the centrifugal barrier and the level-specific outer turning point). Aside from the level properties, an additional and obvious input feature is the translation temperature at which the rates are computed. The network architecture is shown in Fig. , and it is composed of three fully connected hidden layers with a final linear transformation. Each hidden layer consists of 32 neurons, all characterized by tangent hyperbolic activation functions. L1 and L2 regularization schemes are applied, with the parameters being (10 -4 , 10 -5 ). Additionally, dropout is employed at a rate of 10 -3 to the output of the hidden layers. Additive Gaussian noise centered at a value of 10 -17 [cm 2 /s] is introduced in the data to facilitate the training of dissociation rate coefficients. This is done because the training QCT dissociation rate coefficients for low-lying states at low temperatures have poor statistical convergence.