Other systems studied by RMLPs include radical-radical reactions and isomerizations . In 2020, Käser et al. built a global RMLP for acetaldehyde (AA) to study the competing isomerization and decomposition pathways . They developed a PhysNet PES with 432k data points generated through MD and adaptive sampling. The trained RMLP accurately predicts relative energies, TS on the MEP, and provides valuable mechanistic insights in MD simulations. In 2022, Schreiner and coworkers developed NeuralNEB based on a general-purposed small reaction dataset Transition1x with PaiNN, a modern equivariant MPNN model. They applied NEB algorithms with NeuralNEB, Density Functional based Tight Binding (DFTB), and DFT level of theory to compare barrier height prediction. Compared to DFT results, NeuralNEB provides better barrier prediction than semiempirical (SE) DFTB methods with twice speed-up and three times better accuracy.