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

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

6 March 2023

Jianye Hao

Pheng-Ann Heng

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

6 / 6 papers shown

Title
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers Md Shamim Hussain Mohammed J. Zaki D. Subramanian ViT 19 4 0 07 Feb 2024
Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction Chen Liang Bo-Lan Wang Shaogang Hao Guangyong Chen Pheng-Ann Heng Xiaolong Zou 40 1 0 15 Sep 2022
Spherical Channels for Modeling Atomic Interactions C. L. Zitnick Abhishek Das Adeesh Kolluru Janice Lan Muhammed Shuaibi Anuroop Sriram Zachary W. Ulissi Brandon M. Wood 79 57 0 29 Jun 2022
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs Yi-Lun Liao Tess E. Smidt 75 211 0 23 Jun 2022
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 190 1,229 0 08 Jan 2021
The Open Catalyst 2020 (OC20) Dataset and Community Challenges L. Chanussot Abhishek Das Siddharth Goyal Thibaut Lavril Muhammed Shuaibi ... Brandon M. Wood Junwoong Yoon Devi Parikh C. L. Zitnick Zachary W. Ulissi 221 498 0 20 Oct 2020