Additional node features were included using a variety of different schemes to improve model performance. This included combinatoric mapping of integer features and the inclusion of a graph feature encoding MLP to produce learned node embeddings at the graph level, to provide the most human engineered information possible, while giving the graph encoder the most flexibility in the embedding space. At the time of writing, this is only the second time that such a technique has been implemented, and the first time it has been incorporated in any context outside of single value regression (the previous use case was in prediction of HOMO-LUMO gaps). A variety of different node features and encoding schemes were tested, but the best performing encoder involved a two-layer MLP that received atomic number, hybridization, atomic mass, number of bonds, explicit valence, number of hydrogens, aromaticity, and Gasetgier partial charge. This version of Graphormer-IR obtained final test 𝑆𝐼𝑆 𝜇 = 0.8523 ± 0.0006, which is on average 19.7𝜎 greater than the previous state-of-the-art results.