(364k) Graph Invariants As Rapid Features for Classification and Regression of Bulk Hydrogen Intercalation Energies in Reducible Metal Oxides
AIChE Annual Meeting
Tuesday, November 9, 2021 - 5:30pm to 5:42pm
In this talk, we will discuss our recent application of graph fingerprints, commonly used in convolutional neural networks (CNNs) for image recognition and molecular ML applications, to predict DFT-calculated hydrogen intercalation energies in bulk metal oxides. The graphs represented the lattice structures of the metal oxides, considering the number of bonds and neighbors at each atom center. Specifically, we considered the graphs as Laplacian matrices of the bulk metal oxides and their intercalated counterparts. Instead of using the full matrices, as is typical for CNNs, we collapsed the full matrices into various single-valued graph invariants. These invariants were then used as features for our ML models that comprise a direct physical link to the geometries of the metal oxides. We demonstrate that a simple logistic regression model can be used to classify metal oxides as either intercalating or non-intercalating oxides. We also show that this classification model can be used to focus on a region of training and improve the performance of regression models that predict the intercalation energy of hydrogen in metal oxides. In summary, this work illustrates a quantitative link between the graph invariants representing metal oxide lattice geometries and changes in material properties associated with bulk hydrogen intercalation. Overall, our ML approach can be applied to rapidly screen energy storage materials and assess the stability and performance of electrocatalysts.