Direction-Based Graph Representation to Accelerate Stable Catalyst Discovery

To realize a renewable and sustainable energy cycle, there has been a lot of effort put into discovering catalysts with desired properties from a large chemical space. To achieve this goal, several screening strategies have been proposed, most of which require validation of thermodynamic stability and synthesizability of candidate materials via computationally intensive quantum chemistry or solid-state physics calculations. This problem can be overcome by reducing the number of calculations through machine learning methods, which predict target properties using unrelaxed crystal structures as inputs. However, numerical input representations of most of the previous models are based on either too specific (e.g., atomic coordinates) or too ambiguous (e.g., stoichiometry) information, practically inapplicable to energy prediction of unrelaxed initial structures. In this work, we develop a direction-based crystal graph convolutional neural network (D-CGCNN) with the highest accuracy toward formation energy predictions of the relaxed structures using the initial structures as inputs. By comparing with other approaches, we revealed correlations between crystal graph similarities and model performances, elucidating the origin of the improved accuracy of our model. We applied this model to the ongoing high-throughput virtual screening project, where the model discovered 1,725 stable materials from 15,318 unrelaxed structures by performing 3,966 structure optimizations (similar to 25%).