  This technical note describes the recent updates of Graphormer, including
architecture design modifications, and the adaption to 3D molecular dynamics
simulation. With these simple modifications, Graphormer could attain better
results on large-scale molecular modeling datasets than the vanilla one, and
the performance gain could be consistently obtained on 2D and 3D molecular
graph modeling tasks. In addition, we show that with a global receptive field
and an adaptive aggregation strategy, Graphormer is more powerful than classic
message-passing-based GNNs. Empirically, Graphormer could achieve much less MAE
than the originally reported results on the PCQM4M quantum chemistry dataset
used in KDD Cup 2021. In the meanwhile, it greatly outperforms the competitors
in the recent Open Catalyst Challenge, which is a competition track on NeurIPS
2021 workshop, and aims to model the catalyst-adsorbate reaction system with
advanced AI models. All codes could be found at
https://github.com/Microsoft/Graphormer.
