mlsanityData to 14 Oct 2026

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

arXiv 2501.03278 ↗First posted 5 Jan 2025cond-mat.mtrl-sci cs.LGcited by 13

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Abstract (arXiv)

Generative models generate vast numbers of hypothetical materials, necessitating fast, accurate models for property prediction. Graph Neural Networks (GNNs) excel in this domain but face challenges like high training costs, domain adaptation issues, and over-smoothing. We introduce DenseGNN, which employs Dense Connectivity Network (DCN), Hierarchical Node-Edge-Graph Residual Networks (HRN), and Local Structure Order Parameters Embedding (LOPE) to address these challenges. DenseGNN achieves state-of-the-art performance on datasets such as JARVIS-DFT, Materials Project, and QM9, improving the performance of models like GIN, Schnet, and Hamnet on materials datasets. By optimizing atomic embeddings and reducing computational costs, DenseGNN enables deeper architectures and surpasses other GNNs in crystal structure distinction, approaching X-ray diffraction method accuracy. This advances materials discovery and design.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 17 of its 63 references that are in the map. First posted on arXiv 5 Jan 2025.

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17 of 63 references.

References · 17

  1. 2017Graph Attention Networkscited by 8,923
  2. 2017Inductive Representation Learning on Large Graphscited by 6,320
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  5. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,102
  6. 2020Directional Message Passing for Molecular Graphscited by 561
  7. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 545
  8. 2014Deeply-Supervised Netscited by 367
  9. 2022The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalystscited by 95
  10. 2019Predicting materials properties without crystal structure: Deep representation learning from stoichiometrycited by 62
  11. 2019Generative adversarial networks (GAN) based efficient sampling of chemical space for inverse design of inorganic materialscited by 46
  12. 2019Data-Driven Approach to Encoding and Decoding 3-D Crystal Structurescited by 34
5 more
  1. 2018CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networkscited by 31
  2. 2021Scalable deeper graph neural networks for high-performance materials property predictioncited by 26
  3. 2021HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networkscited by 13
  4. 2022Formula graph self-attention network for representation-domain independent materials discoverycited by 7
  5. 2021Implementing graph neural networks with TensorFlow-Kerascited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 8.

13 papers.

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  1. 2025GNNs as Predictors of Agentic Workflow Performancescited by 17
  2. 2025Stress-Testing Multimodal Foundation Models for Crystallographic Reasoningcited by 4
  3. 2026mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural Networkscited by 4
  4. 2025Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Frameworkcited by 1
  5. 2025SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property predictioncited by 0
  6. 2025Artificial Intelligence-driven Intelligent Wearable Systems: A full-stack Integration from Material Design to Personalized Interactioncited by 0
  7. 2026Graph-Informed Adversarial Modeling: Infimal Subadditivity of Interpolative Divergencescited by 0
  8. 2026Teaching Molecular Dynamics to a Non-Autoregressive Ionic Transport Predictorcited by 0
  9. 2026Optimizing Expert-Designed Crystal Graph Networks for Band-Gap Prediction with an Autonomous LLM Research Loopcited by 0
  10. 2026A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Modelscited by 0
  11. 2026Equivariant Covariance Tensors: Guaranteed SPD Uncertainty for Tensor-Valued Geometric Learningcited by 0
  12. 2026A Hierarchical Synergistic Deep Learning Framework Integrating Composition, Structure, and Ionic Transport for Solid-State Electrolyte Discoverycited by 0
1 more
  1. 2026Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashingcited by 0