Deep Principle’s open-weight Hydrogen-1.0 model democratizes advanced scientific discovery by providing a massive, MIT-licensed tool for chemistry and life sciences research.
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Deep Principle has released Hydrogen-1.0, an open-weight scientific model for chemistry, materials science and life sciences. It is built by post-training Z.ai’s GLM-5.3 on domain data, laboratory records and agent discovery trajectories. For developers, it offers open weights that report a FrontierScience-Research win over GPT-6-Astra. The weights ship under the MIT License. TL;DR Size: 753B parameters (MoE, ~40B active per the GLM-5.3 base), 1M-token context, 128K max output. Runs on: 8 GPUs with SGLang (tested); NVFP4 for NVIDIA B200/B300, MXFP4 for AMD MI350X/MI355X. Performance: Beats GPT-6-Astra on FrontierScience-Research; roughly matches Kimi-K3 on GPQA-Diamond. Best: 38.3% on FrontierScience-Research vs 33.3% for GPT-6-Astra (self-reported). Worst: Trails GPT-6-Astra (max thinking) by 3.33 points on NMR structure elucidation. What is Hydrogen-1.0? Hydrogen-1.0 is a scientific research assistant model from Deep Principle, a Hangzhou-based AI-for-materials company. The model card describes it as a GLM-5.3 derivative for scientific problem solving. It targets question framing, experiment planning, results analysis and open-ended discovery. Deep Principle team states that proprietary models produced recent AI science results, but their discovery capabilities are not open. Hydrogen-1.0 is its attempt at a reproducible, inspectable alternative. How was Hydrogen-1.0 trained? The pipeline combines supervised fine-tuning (SFT) with reinforcement learning (RL). Data comes from 4 sources: Domain knowledge: chemistry, materials science and life sciences data. Research scenarios: task-specific feedback from Deep Principle scientists. Laboratory records: full-cycle electronic lab notebook records captured through Mira. Discovery trajectories: sequences of choices and feedback, including failed attempts. Infrastructure builds on Megatron and SGLang across NVIDIA CUDA and AMD ROCm. The team keeps dynamic sparse attention causal across context-parallel shards
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