Deep Principle releases Hydrogen-1.0 for scientific research
Deep Principle released Hydrogen-1.0, an open-weight model post-trained from GLM-5.3 for chemistry, materials science, and life sciences. The team trained it with supervised fine-tuning and reinforcement learning using domain data, scientist feedback, lab records, and discovery trajectories, and published weights, a model card, evaluations, and workflow examples. In vendor-reported evaluations, Hydrogen-1.0 achieved a 38.3% pass rate on FrontierScience Research versus 33.3% for GPT-6 Astra at max thinking, with estimated output-side cost of $0.25 versus $0.88 per problem. It scored 92.9% on GPQA Diamond, slightly below Kimi K3's 93.5%.
The release provides open weights and detailed scientific-workflow training materials, but its benchmark leads and cost advantage remain vendor-reported and its selected model-plus-tool demonstrations do not establish autonomous laboratory performance.
| Hey everyone— we’re the Deep Principle team behind Mira AI Scientist. We just released Hydrogen-1.0, an open-weight model post-trained from GLM-5.3 for chemistry, materials science, and life sciences. Here’s how it performed in our evaluations: On separate internal task sets, Hydrogen also scored higher than GPT-6 Astra at max thinking on scientific computing (+5.13 percentage points) and single-step forward reaction prediction (+4.76 points). It trailed Astra on NMR structure elucidation (−3.33 points). The write-up covers the evaluations and five workflow examples. Why we built it If you’ve used LLMs for research, you’ve probably spent a fair amount of time connecting the steps yourself: turning a question into a plan, preparing inputs, running tools, checking the output, and figuring out what to try when something breaks. We wanted to see whether training on scientific workflows could make the model better at handling those connected decisions. How we trained it We used supervised fine-tuning and reinforcement learning with domain data, scientist feedback, records from our own labs, and discovery trajectories. The lab records keep the research goal, procedure, observations, and outcome together. The discovery trajectories include how the next step changes after feedback, failed attempts, or unexpected results. SFT gives the model worked examples of scientific reasoning, experiment planning, protocol design, and analysis. RL uses feedback on research outcomes to improve its decisions. What the workflows look like The article walks through five cases: A bit of context: these are selected model-plus-tool walkthroughs, not an overall autonomous-research success rate. GROMACS does the MD calculations, and the protocol validation is virtual—it doesn’t mean the physical experiment was carried out. The attached video gives a quick look at the research interactions. The article has the detailed walkthroughs, evaluations, and training approach. Links We’d especially like to hear from people working in computational chemistry and materials science. What would you try it on first, and what would you check before trusting the result? submitted by /u/AKAMA199 [link] [comments] |
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