DimeNet, published in 2022, is another molecular representation learning model, distinguished by its use of an MPGNN to predict molecular properties from 3D inputs. Its primary innovation is the use of spherical Bessel functions to encode interatomic distances, and spherical harmonics to capture bond angles. By incorporating angular information, DimeNet computes messages that reflect both pairwise atomic distances and angular contributions, allowing it to capture more nuanced geometric and chemical relationships. Unlike SchNet, DimeNet is equivariant to rotation and translation, meaning that while its internal representations change under these transformations, the predicted properties remain invariant. In its original publication, DimeNet outperformed SchNet and other GNNs in predicting energies and forces on the QM9 dataset and in molecular dynamics simulations on the MD17 dataset. Building on this, DimeNet++, published in the same year as the original model, further improved accuracy and computational efficiency. Two years later, advancements led to the publication of GemNet, which combined spherical representations with edge-based two-hop geometric message passing, a formalism that obtains the molecule's full geometric information and improves the universality of the model. DimeNet++ has also been applied to predicting activation energy barriers, as demonstrated by Spiekermann et al. In this study, 3D reactant and product geometries from the Isomerisation 103 dataset were used in single-point energy calculations at the CCSD(T)-F12a/cc-pVDZ-F12 130 to yield a dataset of gas-phase elementary reactions with highly accurate activation energy barriers. This dataset was used to train DimeNet++ and it's performance was compared to Chemprop, a 2D graph-based MPGNN, amended to be able to predict reaction barriers. The authors hypothesised that incorporating 3D information would enhance predictive capabilities relative to 2D graph representations.