Graph neural networks for molecular and materials representation

This comprehensive survey is intended to present a holistic view of GNNs for MMR, focusing on the core concepts, the main techniques, and the future trends in ...

Graph neural networks for materials science and chemistry - Nature

Their architecture allows them to directly work on natural input representations of molecules and materials, which are chemical graphs of atoms ...

This workflow pits the conventional method against two alternative approaches: density functional-based tight binding (DFTB) calculations and a GCNN model. The ...

Graph neural networks for molecular and material representation

The presence of point defects such as vacancies plays an important role in material design. Here, we demonstrate that a graph neural network (GNN) model trained ...

Graph neural networks for materials science and chemistry - arXiv

They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules ...

8. Graph Neural Networks - deep learning for molecules & materials

Before we dive too deep into them, we must first understand how a graph is represented in a computer and how molecules are converted into graphs. You can find ...

[2209.05582] Graph Neural Networks for Molecules - arXiv

Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems.

Chain-aware graph neural networks for molecular property prediction

We develop a novel chain-aware graph neural network model, wherein the chain structures are captured by learning the representation of the center node along ...

Graph Networks as a Universal Machine Learning Framework for ...

... molecular properties. (30,31) ... material space with representations learned from different layers of graph convolutional neural networks.

Graph neural networks for materials science and chemistry - PubMed

... graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials.

Atomistic Line Graph Neural Network for improved materials ...

Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and ...

Graph-based deep learning frameworks for molecules and solid ...

Graph neural networks (GNNs) are a kind of deep learning model capable of learning on graph data and have been successfully applied in the property prediction ...

Graph neural network (GNN) for molecular property ... - GitHub

The code of a graph neural network (GNN) for molecules, which is based on learning representations of r-radius subgraphs (i.e., fingerprints) in molecules.

Could graph neural networks learn better molecular representation ...

Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies ...

(PDF) Graph neural networks for materials science and chemistry

They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules ...

Applying graph neural network models to molecular property ...

For molecular property prediction, a key question can be the representation of the structure of the molecule or crystal [27]. When the 3D coordinates of the ...

Learning Molecular Mixture Property Using Chemistry-Aware Graph ...

Using the graph representation, graph neural networks (GNNs) have demonstrated efficacy in molecular modeling and design applications [11–14].

Extended study on atomic featurization in graph neural networks for ...

The two main components of molecular property prediction are the representation of chemical compounds and the model used to calculate the ...

Attention-Based Graph Neural Network for Molecular Solubility ...

Recently, many graph neural networks (GNNs) have been designed for molecular graph representation learning. ... molecules and materials. J ...

Examining graph neural networks for crystal structures - Science

Because materials can be intuitively represented as graphs, with atoms forming the nodes and bonds forming the edges, graph neural networks ( ...