  This technical report presents GPS++, the first-place solution to the Open
Graph Benchmark Large-Scale Challenge (OGB-LSC 2022) for the PCQM4Mv2 molecular
property prediction task. Our approach implements several key principles from
the prior literature. At its core our GPS++ method is a hybrid MPNN/Transformer
model that incorporates 3D atom positions and an auxiliary denoising task. The
effectiveness of GPS++ is demonstrated by achieving 0.0719 mean absolute error
on the independent test-challenge PCQM4Mv2 split. Thanks to Graphcore IPU
acceleration, GPS++ scales to deep architectures (16 layers), training at 3
minutes per epoch, and large ensemble (112 models), completing the final
predictions in 1 hour 32 minutes, well under the 4 hour inference budget
allocated. Our implementation is publicly available at:
https://github.com/graphcore/ogb-lsc-pcqm4mv2.
