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R-GNN: recurrent graph neural networks for font classification of oracle bone inscriptions
Font classification of oracle bone inscriptions serves as a crucial basis for determining the historical period to which they belong and holds...
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Polymer graph neural networks for multitask property learning
The prediction of a variety of polymer properties from their monomer composition has been a challenge for material informatics, and their development...
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Graph neural networks for materials science and chemistry
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties,...
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Towards accurate prediction of configurational disorder properties in materials using graph neural networks
The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound...
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Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids
This work presents an E(3) equivariant graph neural network called HamGNN, which can fit the electronic Hamiltonian matrix of molecules and solids by...
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Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo
Metal-organic frameworks (MOF) are an attractive class of porous materials due to their immense design space, allowing for application-tailored...
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Perovskite synthesizability using graph neural networks
Perovskite is an important material type in geophysics and for technologically important applications. However, the number of synthetic perovskites...
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Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks
Structural defects are abundant in solids, and vital to the macroscopic materials properties. However, a defect-property linkage typically requires...
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Molecular descriptor-enhanced graph neural network for energetic molecular property prediction
Energetic molecules (EMs) play an important role in both military and civilian applications. Traditionally, determining the physicochemical...
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Designing Ti-6Al-4V microstructure for strain delocalization using neural networks
The deformation behavior of Ti-6Al-4V titanium alloy is significantly influenced by slip localized within crystallographic slip bands. Experimental...
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Enhancing interpretability in the exploration of high-energy conversion efficiency in CsSnBr3−xIx configurations using crystal graph convolutional neural networks and adversarial example methods
Crystal graph convolutional neural networks (CGCNNs) have revolutionized materials research by eliminating the need for manual feature engineering....
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Benchmarking graph neural networks for materials chemistry
Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for...
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Graph neural network modeling of grain-scale anisotropic elastic behavior using simulated and measured microscale data
Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys....
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CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment
We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature...
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Stretchable array electromyography sensor with graph neural network for static and dynamic gestures recognition system
With advances in artificial intelligence (AI)-based algorithms, gesture recognition accuracy from sEMG signals has continued to increase....
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Atomistic Line Graph Neural Network for improved materials property predictions
Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling...
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Validating neural networks for spectroscopic classification on a universal synthetic dataset
To aid the development of machine learning models for automated spectroscopic data classification, we created a universal synthetic dataset for the...
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Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets
Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the...
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Data-augmentation for graph neural network learning of the relaxed energies of unrelaxed structures
Computational materials discovery has grown in utility over the past decade due to advances in computing power and crystal structure prediction...
