  One of the most appealing aspects of machine learning for material design is
its high throughput exploration of chemical spaces, but to reach the ceiling of
ML-aided exploration, more than current model architectures and processing
algorithms are required. New architectures such as Graph Neural Networks (GNNs)
have seen significant research investments recently. For heterogeneous
catalysis, defining substrate intramolecular bonds and adsorbate/substrate
intermolecular bonds is a time-consuming and challenging process. Before
applying a model, dataset pre-processing, node/bond descriptor design, and
specific model constraints have to be considered. In this work, a framework
designed to solve these issues is presented in the form of an automatic graph
representation algorithm (AGRA) tool to extract the local chemical environment
of metallic surface adsorption sites is presented. This tool is able to gather
multiple adsorption geometry datasets composed of different systems and combine
them into a single model. To show AGRA's excellent transferability and reduced
computational cost compared to other graph representation methods, it was
applied to 5 different catalytic reaction datasets and benchmarked against the
Open Catalyst Projects (OCP) graph representation method. The two ORR datasets
with O/OH adsorbates obtained 0.053 eV RMSD when combined together, whereas the
three CO2RR datasets with CHO/CO/COOH obtained an average performance of 0.088
eV RMSD. To further display the algorithm's versatility and extrapolation
ability, a model was trained on a subset combination of all 5 datasets with an
RMSD of 0.105 eV. This universal model was then used to predict a wide range of
adsorption energies and an entirely new ORR catalyst system and then verified
through Density Functional Theory calculations
