Research revealing how to scale Graph Neural Networks (GNNs) for superior performance in atomistic materials modeling, with direct applications in engineering and materials science.

• GNNs represent the state-of-the-art approach for modeling complex atomic structures
• The research establishes scaling laws showing how model size, data volume, and computational resources relate to prediction accuracy
• Findings enable more efficient model design for materials discovery applications
• Results can accelerate innovations in structural materials, electronics, and drug discovery

For engineering teams, these insights provide a framework for optimizing computational resources when designing new materials, potentially reducing R&D cycles and costs.

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling