Periodic trained an AI model called Neon that outperforms frontier models like GPT-6 at analyzing X-ray diffraction data—a task materials scientists spend hours on—using less compute and lower cost. The model learned from experimental lab data through reinforcement learning with expert judgment, achieving a 55% success rate on their hardest internal benchmark.

• Neon reached 55.3% success on FrontierXRD (134 complex samples), a 20x jump from the base model's 2.7%, while costing less per analysis than GPT-6 Astra or Claude Fable 5.1
• Periodic built a custom scientific harness that achieved 3.8x higher success rates than Claude Code with standard tools, showing that model capability depends heavily on available databases and software
• The company used an LLM-judge ensemble calibrated to human expert ratings (74.6% agreement with humans, 84% with consensus) to generate training signals for reinforcement learning on tasks without ground-truth answers