  This technical note describes the recent updates of Graphormer, including
architecture design modifications, and the adaption to 3D molecular dynamics
simulation. The "Graphormer-V2" could attain better results on large-scale
molecular modeling datasets than the vanilla one, and the performance gain
could be consistently obtained on downstream 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. Graphormer-V2
achieves much less MAE than the vanilla Graphormer on the PCQM4M quantum
chemistry dataset used in KDD Cup 2021, where the latter one won the first
place in this competition. In the meanwhile, Graphormer-V2 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 models could be found at
\url{https://github.com/Microsoft/Graphormer}.
