Direction-based Graph Representation to Accelerate Stable Catalysts Discovery

30 September 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


To realize 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 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 on-going high-throughput virtual screening project, where the model discovered 1,725 stable materials from 15,318 unrelaxed structures by performing 3,966 structure optimizations (~25 %).


Thermodynamic Stability
High-throughput Screening
Machine Learning
Graph Neural Network
Density Functional Theory Calculations

Supplementary materials

Supplementary information
Supplementary information of "Direction-based Graph Representation to Accelerate Stable Catalysts Discovery"

Supplementary weblinks


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.