mlsanityData to 7 Oct 2026

Graph neural networks for materials science and chemistry

arXiv 2208.09481 ↗First posted 5 Aug 2022physics.chem-ph cond-mat.mtrl-sci cs.LGcited by 181

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Abstract (arXiv)

Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design new materials, and to predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this review article, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.

Key facts. Cited by 181 papers in the map (data through 7 Oct 2026). Builds on 142 of its 324 references that are in the map. First posted on arXiv 5 Aug 2022.

Builds on

142 of 324 references.

References · 142

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
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  10. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
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  12. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
38 more
  1. 2016Density estimation using Real NVPcited by 2,029
  2. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  3. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
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  6. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
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  15. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,037
  16. 2021E(n) Equivariant Graph Neural Networkscited by 951
  17. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  18. 2021How Attentive are Graph Attention Networks?cited by 818
  19. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  20. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
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  22. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
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  24. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
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50 most cited of 142 All 142 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 30; 2024: 58; 2025: 55; 2026: 37.

181 papers.

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  1. 2023Attending to Graph Transformerscited by 81
  2. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  3. 2024A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensationcited by 57
  4. 2025Position: Graph Learning Will Lose Relevance Due To Poor Benchmarkscited by 52
  5. 2023Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformerscited by 32
  6. 2024Text-Guided Molecule Generation with Diffusion Language Modelcited by 31
  7. 2024A Review of Graph Neural Networks in Epidemic Modelingcited by 31
  8. 2023Probabilistically Rewired Message-Passing Neural Networkscited by 26
  9. 2023Fine-grained Expressivity of Graph Neural Networkscited by 25
  10. 2024Spectral Graph Pruning Against Over-Squashing and Over-Smoothingcited by 23
  11. 2023Graph Neural Network-based EEG Classification: A Surveycited by 21
  12. 2024Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Modelscited by 21
38 more
  1. 2025GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Designcited by 21
  2. 2023Machine Learning for Practical Quantum Error Mitigationcited by 20
  3. 2024textttMiniMol: A Parameter-Efficient Foundation Model for Molecular Learningcited by 19
  4. 2023Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Predictioncited by 17
  5. 2024Structure-based out-of-distribution (OOD) materials property prediction: a benchmark studycited by 17
  6. 2024LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learningcited by 17
  7. 2024Crystalformer: Infinitely Connected Attention for Periodic Structure Encodingcited by 15
  8. 2024Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Modelcited by 14
  9. 2024A review of graph neural network applications in mechanics-related domainscited by 14
  10. 2025Temperature-Annealed Boltzmann Generatorscited by 14
  11. 2025GNNs Getting ComFy: Community and Feature Similarity Guided Rewiringcited by 13
  12. 2024Thermodynamics-Consistent Graph Neural Networkscited by 12
  13. 2023Accelerating Molecular Graph Neural Networks via Knowledge Distillationcited by 11
  14. 2025MOFGPT: Generative Design of Metal-Organic Frameworks using Language Modelscited by 11
  15. 2023Complete Neural Networks for Complete Euclidean Graphscited by 10
  16. 2023A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systemscited by 10
  17. 2024Disentangled Condensation for Large-scale Graphscited by 10
  18. 2024Less can be more for predicting properties with large language modelscited by 10
  19. 2024AI-driven inverse design of materials: Past, present and futurecited by 10
  20. 2023Exploiting Code Symmetries for Learning Program Semanticscited by 9
  21. 2024A Logic for Reasoning About Aggregate-Combine Graph Neural Networkscited by 9
  22. 2023Differentiable graph-structured models for inverse design of lattice materialscited by 8
  23. 2023Graph Neural Networks for Pressure Estimation in Water Distribution Systemscited by 8
  24. 2024Simple Graph Condensationcited by 8
  25. 2023Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Predictioncited by 7
  26. 2023Molecular Hypergraph Neural Networkscited by 7
  27. 2024GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insightscited by 7
  28. 2025xChemAgents: Agentic AI for Explainable Quantum Chemistrycited by 7
  29. 2022Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directionscited by 6
  30. 2024From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networkscited by 6
  31. 2024GraphXForm: Graph transformer for computer-aided molecular designcited by 6
  32. 2025Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformerscited by 6
  33. 2023GPS++: Reviving the Art of Message Passing for Molecular Property Predictioncited by 5
  34. 2023Investigating the Interplay between Features and Structures in Graph Learningcited by 5
  35. 2023Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Taskscited by 5
  36. 2024GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networkscited by 5
  37. 2025Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolvercited by 5
  38. 2025Molecular Machine Learning in Chemical Process Designcited by 5

50 most cited of 181 All 181 (JSON)