where f is a neural network applied to each reaction feature list, and the transformed inputs are summed, producing a representation that is invariant to the reaction order. The scorer net σ processes the aggregated reaction encoding as well as the route features cost c, volume v and complexity e. In summary, as shown in Fig. , the DeepSets-based network architecture consists of two primary modules: the reaction-level encoder ϕ and route-level scorer σ, which take in route-level properties and reaction-level features as their inputs, respectively. The encoder network processes each reaction individually, producing a route-level encoding that, along with additional route properties, is passed to the scorer to compute the final score.