DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

2024-12-19

npj Computational Materials

AI Summary

Existing generative models lack efficient and accurate tools for predicting material properties; conventional graph neural networks (GNNs) suffer from training instability, depth limitations, and poor generalization across diverse material systems. Method: We propose a scalable, general-purpose deep GNN framework featuring dense skip connections, hierarchical normalization, and scale-aware message passing—enabling stable training of ultra-deep GNNs (>30 layers) on multiscale material graphs. Integrated with multi-task adaptive loss and material graph configuration encoding, the model enhances both representation fidelity and transferability. Contribution/Results: Our method achieves state-of-the-art performance across 12 benchmarks—including QM9 and Materials Project—with an average 18.7% improvement in prediction accuracy. It accelerates inference by 3.2× over standard GCNs and supports batched prediction on million-scale material structures, demonstrating robust cross-system generalization and practical scalability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Problem

• Generative Models
• Graph Neural Networks (GNN)
• Material Property Prediction

Innovation

• DenseGNN
• MaterialsPrediction
• GraphNeuralNetworks