ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2204.02782
  4. Cited By
GemNet-OC: Developing Graph Neural Networks for Large and Diverse
  Molecular Simulation Datasets

GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets

6 April 2022
Johannes Gasteiger
Muhammed Shuaibi
Anuroop Sriram
Stephan Günnemann
Zachary W. Ulissi
C. L. Zitnick
Abhishek Das
    AI4TS
    MLAU
ArXivPDFHTML

Papers citing "GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets"

11 / 11 papers shown
Title
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
Yasir Ghunaim
Andrés Villa
Gergo Ignacz
Gyorgy Szekely
Motasem Alfarra
Bernard Ghanem
AI4CE
84
0
0
28 Apr 2025
Pre-training Graph Neural Networks with Structural Fingerprints for Materials Discovery
Shuyi Jia
Shitij Govil
Manav Ramprasad
Victor Fung
AI4CE
64
1
0
03 Mar 2025
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
Luis Barroso-Luque
Muhammed Shuaibi
Xiang Fu
Brandon M. Wood
Misko Dzamba
Meng Gao
Ammar Rizvi
C. L. Zitnick
Zachary W. Ulissi
AI4CE
PINN
24
16
0
16 Oct 2024
GeoMFormer: A General Architecture for Geometric Molecular
  Representation Learning
GeoMFormer: A General Architecture for Geometric Molecular Representation Learning
Tianlang Chen
Shengjie Luo
Di He
Shuxin Zheng
Tie-Yan Liu
Liwei Wang
AI4CE
36
5
0
24 Jun 2024
On the Completeness of Invariant Geometric Deep Learning Models
On the Completeness of Invariant Geometric Deep Learning Models
Zian Li
Xiyuan Wang
Shijia Kang
Muhan Zhang
31
2
0
07 Feb 2024
On the importance of catalyst-adsorbate 3D interactions for relaxed
  energy predictions
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions
Alvaro Carbonero
Alexandre Duval
Victor Schmidt
Santiago Miret
Alex Hernandez-Garcia
Yoshua Bengio
David Rolnick
25
0
0
10 Oct 2023
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Alexandre Duval
Victor Schmidt
A. Garcia
Santiago Miret
Fragkiskos D. Malliaros
Yoshua Bengio
David Rolnick
20
52
0
28 Apr 2023
Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs
Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs
Saro Passaro
C. L. Zitnick
3DPC
14
78
0
07 Feb 2023
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and
  Nonlocal Effects
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Oliver T. Unke
Stefan Chmiela
M. Gastegger
Kristof T. Schütt
H. E. Sauceda
K. Müller
142
244
0
01 May 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate
  Interatomic Potentials
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
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
1