On the importance of catalyst-adsorbate 3D interactions for relaxed
  energy predictions
Alvaro Carbonero Alexandre Duval Victor Schmidt Santiago Miret Alex Hernandez-Garcia Yoshua Bengio David Rolnick
The use of machine learning for material property prediction and discovery
has traditionally centered on graph neural networks that incorporate the
geometric configuration of all atoms. However, in practice not all this
information may be readily available, e.g.~when evaluating the potentially
unknown binding of adsorbates to catalyst. In this paper, we investigate
whether it is possible to predict a system's relaxed energy in the OC20 dataset
while ignoring the relative position of the adsorbate with respect to the
electro-catalyst. We consider SchNet, DimeNet++ and FAENet as base
architectures and measure the impact of four modifications on model
performance: removing edges in the input graph, pooling independent
representations, not sharing the backbone weights and using an attention
mechanism to propagate non-geometric relative information. We find that while
removing binding site information impairs accuracy as expected, modified models
are able to predict relaxed energies with remarkably decent MAE. Our work
suggests future research directions in accelerated materials discovery where
information on reactant configurations can be reduced or altogether omitted.