In this work, the GCNN model implemented in HydraGNN [19] is used as a surrogate model for HOMO-LUMO gap property. The GC layer used is principal neighborhood aggregation (PNA) . HydraGNN is an open source software built on PyTorch and PyTorch Geometric library. It is capable of multitask prediction of hybrid node-level and graph-level properties in large batch of graphs with variable number of nodes. HydraGNN utilizes ADIOS , a data framework suitable for high performance computing, for data loading in order to handle large graph data set. It has been tested on multiple systems including ORNL's Summit and NERSC's Perlmutter with data sets larger than 100 GB.