Graph neural networks for materials science and chemistry.

Area of Science:

• Chemistry
• Materials Science
• Machine Learning
• Artificial Intelligence

Background:

• Machine learning is crucial for predicting material properties, accelerating simulations, and designing new materials.
• Graph neural networks (GNNs) are highly relevant to chemistry and materials science due to their ability to process graph-based structural data.

Purpose of the Study:

• To provide a comprehensive overview of Graph Neural Networks (GNNs).
• To discuss the principles, datasets, architectures, and applications of GNNs in chemistry and materials science.
• To outline a future roadmap for GNN development and implementation.

Main Methods:

• Review of fundamental Graph Neural Network (GNN) principles.
• Analysis of widely used datasets for GNN training in chemistry and materials science.
• Examination of state-of-the-art GNN architectures and their performance.

Main Results:

• GNNs offer direct access to structural information of molecules and materials.
• Diverse applications of GNNs in predicting properties, designing structures, and optimizing synthesis routes are highlighted.
• The review synthesizes current advancements and identifies key trends in GNN utilization.

Conclusions:

• Graph neural networks (GNNs) are a powerful tool for advancing chemistry and materials science.
• The review provides a foundational understanding and practical insights into GNN applications.
• Future directions emphasize continued development and broader adoption of GNNs in scientific discovery.