RECORD
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
Machine Learning · Submitted 6 Mar 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | 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. | background 1.00 | 0.06 | 0.03 | asserted 1.00 |
| s2 | In this paper, we present a novel graph neural network (GNN) supervision and prediction strategy DR-Label. | contribution 1.00 | 0.92 | 0.07 | asserted 0.99 |
| s3 | 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. | method 0.62 | 0.73 | 0.13 | asserted 1.00 |
| s4 | DR-Label first Deconstructs finer-grained equilibrium state information to the model by projecting the node-level supervision signal to each edge. | method 0.99 | 0.88 | 0.05 | asserted 1.00 |
| s5 | Reversely, the model Reconstructs a more robust equilibrium state prediction by transforming edge-level predictions to node-level with a sphere-fitting algorithm. | method 0.99 | 0.87 | 0.08 | asserted 1.00 |
| s6 | The DR-Label strategy was applied to three radically distinct models, each of which displayed consistent performance enhancements. | result 0.99 | 0.93 | 0.19 | asserted 1.00 |
| s7 | 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. | result 0.85 | 0.96 | 0.43 | asserted 1.00 |
| s8 | 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. | interpretation 0.57 | 0.11 | 0.21 | hedged possibility 0.66 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original 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.
Human review
How the literature uses it
1 arXiv paper cite this in 1 sentence
What it cites · 19 sentences without a confident label
Uses the method or tool of 3 papers
-
For the experiments on the SAA datasets, we followed the experiment setting from [ref] and made 10 random splits with train:validation: test = 60%:20%:20%, report the mean and standard deviation of the result.
Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction · Model Evaluation Settings · 0.97
-
Noisy Node Following [ref] , we further add noise-augmented instances along each batch.
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond · The DRFormer Model · 0.91
-
For the experiments on OC20 datasets, our testing set results have been reported based on our submission to the official evaluation server on eval.ai [ref] .
EvalAI: Towards Better Evaluation Systems for AI Agents · Model Evaluation Settings · 0.87
Uses data or values from 1 paper
-
We examined our methods using the Open Catalyst 2020 (OC20) [ref] dataset and the Cu-based single-atom-alloyed CO adsorption (SAA) dataset [ref] in simulating the relaxation and adsorption energy prediction process of a catalyst system.
Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction · Introduction · 0.96
Cites as support 2 papers
-
[ref] [ref] revealed that given the initial state $𝒢^0_comb$, auxiliary supervision of node positional displacement $Δ𝒫^⋆$ towards the relaxed state largely improved the model performance.
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond · Catalyst Adsorption Energy Prediction · 0.96
-
[ref] [ref] revealed that given the initial state $𝒢^0_comb$, auxiliary supervision of node positional displacement $Δ𝒫^⋆$ towards the relaxed state largely improved the model performance.
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs · Catalyst Adsorption Energy Prediction · 0.96
Cites as background 5 papers
-
Another direction focuses on finer equivariant representation designing [ref] [ref] ; [ref] ; [ref] .
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds · Invariant and Equivariant GNNs · 0.98
-
Another direction focuses on finer equivariant representation designing [ref] [ref] ; [ref] ; [ref] .
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs · Invariant and Equivariant GNNs · 0.98
-
The iterative relaxation method, such as [ref] , trains models as an imitation of DFT calculation.
GemNet: Universal Directional Graph Neural Networks for Molecules · Catalyst Adsorption Energy Prediction · 0.86
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.