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Improving Molecular Modeling with Geometric GNNs: an Empirical Study

arXiv 2407.08313 ↗First posted 11 Jul 2024cs.LGcited by 0

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Abstract (arXiv)

Rapid advancements in machine learning (ML) are transforming materials science by significantly speeding up material property calculations. However, the proliferation of ML approaches has made it challenging for scientists to keep up with the most promising techniques. This paper presents an empirical study on Geometric Graph Neural Networks for 3D atomic systems, focusing on the impact of different (1) canonicalization methods, (2) graph creation strategies, and (3) auxiliary tasks, on performance, scalability and symmetry enforcement. Our findings and insights aim to guide researchers in selecting optimal modeling components for molecular modeling tasks.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 11 Jul 2024.

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References · 24

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  11. 2023EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representationscited by 201
  12. 2022Sign and Basis Invariant Networks for Spectral Graph Representation Learningcited by 176
12 more
  1. 2021Frame Averaging for Invariant and Equivariant Network Designcited by 166
  2. 2022The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalystscited by 95
  3. 2023FAENet: Frame Averaging Equivariant GNN for Materials Modelingcited by 72
  4. 2021ForceNet: A Graph Neural Network for Large-Scale Quantum Calculationscited by 58
  5. 2022Geometrically Equivariant Graph Neural Networks: A Surveycited by 55
  6. 2023Ewald-based Long-Range Message Passing for Molecular Graphscited by 46
  7. 2021Simple GNN Regularisation for 3D Molecular Property Prediction & Beyondcited by 44
  8. 2024Equivariant Frames and the Impossibility of Continuous Canonicalizationcited by 41
  9. 2023Smooth, exact rotational symmetrization for deep learning on point cloudscited by 39
  10. 2023From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Predictioncited by 39
  11. 2023CHGNet: Pretrained universal neural network potential for charge-informed atomistic modelingcited by 13
  12. 2022PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Designcited by 6

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