The feature dimension for the internal representation of GTNN was established at 128, with the exception of the embedding dimension for the reaction and atomic properties, which was set to 64. Additionally, the first MLP layer following multiset graph transformer-based pooling was configured to have 256 dimensions. The transformer architecture employed two attention heads for pooling. These parameter settings translated into neural network sizes with approximately two million trainable parameters for GTNN.