# Periodic Neon

Periodic trained Periodic Neon on X-ray diffraction data from its own labs, reaching a 55.3% success rate on FrontierXRD, its hardest internal evaluation set, against an initial 2.7% for Kimi K2.6, the open-weight model it post-trained. The evaluation covers 134 samples from its labs that human experts take hours to resolve because of their complexity and ambiguity; Neon surpasses frontier models at a lower cost per analysis and is now deployed in its labs analyzing experiments in the search for better superconductors and magnets.

Periodic trained Periodic Neon on X-ray diffraction data from its own labs, reaching a 55.3% success rate on FrontierXRD, its hardest internal evaluation set, against an initial 2.7% for Kimi K2.6, the open-weight model it post-trained. The evaluation covers 134 samples from its labs that human experts take hours to resolve because of their complexity and ambiguity; Neon surpasses frontier models at a lower cost per analysis and is now deployed in its labs analyzing experiments in the search for better superconductors and magnets.

Periodic Neon is a trillion-parameter model post-trained with midtraining and reinforcement learning on data from the company's own labs and deployed to its high-throughput physical labs in Menlo Park to analyze X-ray diffraction results. It outperforms Claude Fable 5.1 and GPT-6 Astra on diffraction analysis, and inference is optimized so the model can be deployed at scale. Its training data comes from physical experiments rather than elastically scalable digital environments.

Periodic Neon is a trillion-parameter model post-trained with midtraining and reinforcement learning on data from the company's own labs and deployed to its high-throughput physical labs in Menlo Park to analyze X-ray diffraction results. It outperforms Claude Fable 5.1 and GPT-6 Astra on diffraction analysis, and inference is optimized so the model can be deployed at scale. Its training data comes from physical experiments rather than elastically scalable digital environments.