A second approach to using RL for TS geometry prediction is based on an actor-critic framework, where two NNs work collaboratively to optimise molecular geometries along a minimum energy pathway. An initial pathway is proposed via interpolation between reactant and product geometries. At each step along this pathway, the actor network suggests adjustments to atomic positions. These adjustments are guided by a pre-trained equivariant MPGNN, PaiNN, which predicts the forces acting on each atom. A reward is then calculated based on how effectively these forces are minimised towards the desired configuration. The critic network evaluates each atomic configuration and its corresponding reward and tries to estimate the long-term reward based on the actions taken by the actor. The actor's goal is to maximise this long-term reward, adjusting its weights based on feedback from the critic. Over time, the actor network becomes better at refining the atomic positions, ultimately identifying the desired molecular configurations for each point along the minimum energy pathway. This approach differs from previously explored methods, which aimed to directly predict the TS geometry. Instead, under this framework, the TS is identified as the point of maximum energy along the constructed minimum energy pathway. This method is summarised in Figure . A final approach for generating TS geometries involves the use of Machine Learning Potentials (MLPs).