MLPs are ML models that serve as surrogates for computationally expensive DFT or ab initio calculations by predicting the PES for a given set of atoms. Analogous to PaiNN in the actor-critic example, these models are trained to predict forces, energies, and other relevant properties corresponding to specific atomic configurations. MLPs can be extremely powerful, achieving nearquantum accuracy at a fraction of the computational cost. This makes them particularly valuable for studying large systems, including in materials chemistry and biological systems.