MLPs are functions that mimic ab inito potential energy surfaces (PES) from which all structural properties can be derived. 12 Structures are represented by nodes and edges, where nodes correspond to atoms and edges represent distances between atoms within a cutoff radius (Figure ). Nodes are associated with scalar features such as atom distances, vectors describing relative position and higher order tensors to create a rotationally invariant description of the system. However, generating sufficient training data at the DFT level can be expensive for long timescale process, such as pyrolysis, acting as a barrier for creating diverse structural datasets. In this context, pre-trained MLPs are a valuable tool that offer a more efficient alternative. Foundational models are MLPs that are ready to use "out-of-thebox" such as MACE-MP-0, SevenNet-0 and ORB-d3-v2. The accuracy of foundational models are listed on MATBench where currently the ranking, from best to worst, is ORB-d3-v2, SevenNet-0 then MACE-MP-0. 17