How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science
arXiv:2602.09309v2 Announce Type: replace-cross Abstract: Every generative model for crystalline materials harbors a critical structure size beyond which its outputs become unreliable; we call this the extrapolation frontier. Despite its consequences for nanomaterial design, this frontier has never been systematically measured. We introduce RADII, a radius-resolved benchmark of ~75,000 crystal-derived nanoparticle structures (33-11,298 atoms) that treats radius as a continuous scaling knob, tracing generation quality from in- to out-of-distribution under leakage-free splits. Each model is cond
The increasing sophistication of AI models for materials science necessitates robust benchmarks to understand their limitations, especially as AI moves beyond interpolation to extrapolation for novel designs.
This work establishes a critical metric for evaluating generative AI in materials science, directly impacting the reliability and trustworthiness of AI-designed advanced materials, particularly nanomaterials.
The ability to systematically measure the 'extrapolation frontier' for graph generative models provides a standardized way to assess their performance and identify risks when designing new materials out-of-distribution.
- · Materials scientists using AI
- · Nanomaterial designers
- · AI model developers for chemistry/materials
- · Biomedical research
- · Generative models with poor extrapolation
- · Organizations relying on unverified AI-designed materials
Improved confidence in AI-generated material designs and accelerated discovery of novel materials.
New safety protocols and validation standards for AI-designed materials based on understanding extrapolation limits.
Enhanced industrial adoption of generative AI for materials discovery, potentially leading to breakthroughs in energy, electronics, and medicine.
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Read at arXiv cs.LG