ReviewCommunications materials2022
Graph neural networks for materials science and chemistry.
Review in Communications materials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 176 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
176 citing papers in PubMed.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models.The journal of physical chemistry letters · 2026Article
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Artificial Intelligence-Assisted Design of High-Entropy Oxide Catalysts.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Semantic knowledge improves molecular machine learning for chemical toxicity prediction.iScience · 2026Article
- Interpretable Small-Sample Deep Learning with Approximate Attribution Enhancement for Atomically Precise Gold Nanoclusters Synthesis.Molecules (Basel, Switzerland) · 2026Article
- Tunable Surface Chemistry of Molten Salt Derived MXenes for Data-Driven Electrochemical Materials Discovery.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- Multiscale Numerical Modelling and Structural Design of Bulk Heterojunction Nanocomposites for Organic Photovoltaics: From Molecular Interfaces to Device Optimization.Materials (Basel, Switzerland) · 2026Review
- Artificial intelligence in impurity prediction: current landscape, challenges, and future directions.Journal of computer-aided molecular design · 2026Review
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.The Journal of biological chemistry · 2026Article
- Artificial intelligence for design strategies of tissue engineering materials.Fundamental research · 2026Review
- A partially quantum‑informed crystal graph network for direct adsorption energy prediction in catalysis.Scientific reports · 2026Article
- Benchmarking molecular representations and machine learning algorithms for asymmetric catalysis: a palladium-catalysed decarboxylative asymmetric allylic alkylation case study.Journal of cheminformatics · 2026Article
- PolyT-GNN: A Graph Neural Network Framework for Data-Driven Discovery of High-Temperature Two-Way Shape Memory Polymers.ACS applied materials & interfaces · 2026Article
- Structure-Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles.Macromolecular rapid communications · 2026Article
116 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and 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, 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.