DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction

TL;DR

This paper introduces DR-Label, a novel GNN supervision strategy that improves equilibrium state predictions in catalysis systems by deconstructing and reconstructing node and edge information, leading to state-of-the-art results.

Contribution

The paper presents DR-Label, a new supervision method for GNNs that enhances prediction robustness and accuracy in catalysis systems, and introduces DRFormer with superior performance on benchmark datasets.

Findings

DR-Label improves GNN performance across different models.

DRFormer achieves state-of-the-art results on OC20 and SAA datasets.

The strategy enhances equilibrium state property prediction accuracy.

Abstract

Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. Machine learning methods with finer supervision strategies have been applied to boost and guide the relaxation process of an atomic system and better predict its properties at the equilibrium state. In this paper, we present a novel graph neural network (GNN) supervision and prediction strategy DR-Label. The method enhances the supervision signal, reduces the multiplicity of solutions in edge representation, and encourages the model to provide node predictions that are graph structural variation robust. DR-Label first Deconstructs finer-grained equilibrium state information to the model by projecting the node-level supervision signal to each edge. Reversely, the model Reconstructs a more robust equilibrium state prediction by transforming edge-level predictions to node-level with a sphere-fitting algorithm. The DR-Label strategy was applied to three radically distinct models, each of which displayed consistent performance enhancements. Based on the DR-Label strategy, we further proposed DRFormer, which achieved a new state-of-the-art performance on the Open Catalyst 2020 (OC20) dataset and the Cu-based single-atom-alloyed CO adsorption (SAA) dataset. We expect that our work will highlight crucial steps for the development of a more accurate model in equilibrium state property prediction of a catalysis system.