Source 1review2022Communications Materials
Toolkit/graph neural networks
graph neural networks
Also known as: GNN, GNNs
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
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.
Usefulness & Problems
No literature-backed usefulness or problem-fit explainer has been materialized for this record yet.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete computational method used to design, rank, or analyze an engineered system.
Mechanisms
graph-based representation learningTarget processes
recombinationValidation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
Graph neural networks are particularly relevant to chemistry and materials science because they directly operate on graph or structural representations of molecules and materials.
The review states that graph neural networks have access to relevant information required to characterize materials through graph or structural representations.
Machine learning is increasingly used in chemistry and materials science for property prediction, simulation acceleration, structure design, and synthesis-route prediction.
Approval Evidence
2 sources6 linked approval claimsfirst-pass slug graph-neural-networks
Graph neural networks (GNNs) exhibited superior performance in integrating biological knowledge and identifying coexpression gene panels
Source:
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.
Source:
capability statementsupports
The identified coexpression gene panels could be used to predict unknown phenotypes, elucidate molecular biology mechanisms, and advance biotechnologies.
which could be utilized to predict unknown phenotypes, elucidate molecular biology mechanisms, and advance biotechnologies
Source:
performance statementsupports
Graph neural networks exhibited superior performance in integrating biological knowledge and identifying coexpression gene panels.
Graph neural networks (GNNs) exhibited superior performance in integrating biological knowledge and identifying coexpression gene panels
Source:
potential applicationsupports
GNN-empowered optogenetic approaches have potential for regulating denitrification and accelerating mechanistic discovery of microbiomes.
Our study showcased the potential of GNNs-empowered optogenetic approaches for regulating denitrification and accelerating the mechanistic discovery of microbiomes
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capability summarysupports
Graph neural networks are particularly relevant to chemistry and materials science because they directly operate on graph or structural representations of molecules and materials.
Source:
information access summarysupports
The review states that graph neural networks have access to relevant information required to characterize materials through graph or structural representations.
Source:
scope summarysupports
Machine learning is increasingly used in chemistry and materials science for property prediction, simulation acceleration, structure design, and synthesis-route prediction.
Source:
Comparisons
No literature-backed comparison notes have been materialized for this record yet.
Ranked Citations
- 1.