  In order to advance large-scale graph machine learning, the Open Graph
Benchmark Large Scale Challenge (OGB-LSC) was proposed at the KDD Cup 2021. The
PCQM4M-LSC dataset defines a molecular HOMO-LUMO property prediction task on
about 3.8M graphs. In this short paper, we show our current work-in-progress
solution which builds an ensemble of three graph neural networks models based
on GIN, Bayesian Neural Networks and DiffPool. Our approach outperforms the
provided baseline by 7.6%. Moreover, using uncertainty in our ensemble's
prediction, we can identify molecules whose HOMO-LUMO gaps are harder to
predict (with Pearson's correlation of 0.5181). We anticipate that this will
facilitate active learning.
