Deep Principle released Hydrogen-1.0, a 753B-parameter Mixture-of-Experts model (~40B active) derived from Z.ai's GLM-5.3 base via post-training on domain knowledge, scientist feedback, Mira-captured laboratory records, and agent discovery trajectories including failed attempts. Licensed under MIT, the model features a 1M-token context window and 128K maximum output. Training combined supervised fine-tuning with reinforcement learning using resumable rollouts and importance correction via preserved behavior log-probabilities, running on Megatron/SGLang infrastructure across NVIDIA CUDA and AMD ROCm with dynamic sparse attention maintained across context-parallel shards and Muon optimizer states shuttled between CPU, GPU, and disk. A layout adapter converts checkpoints to BF16 and 128×128 blockwise FP8 inference weights. Hydrogen-1.0 scores 38.3% on FrontierScience-Research versus GPT-6-Astra's 33.3%, reaches 93.0% on GPQA-Diamond (matching Kimi-K3), and leads GPT-6-Astra by 5.13 and 4.76 points on scientific computing and forward reaction prediction respectively, though it trails by 3.33 points on NMR structure elucidation. In transition metal complex design over five rounds, Hydrogen-1.0 achieved a mean best HOMO-LUMO gap of 4.318 eV versus 3.689 eV (GPT-6-Astra) and 3.353 eV (Kimi-K3). Deployed via SGLang with tensor parallelism 8 on 8 GPUs, it supports NVFP4 for NVIDIA B200/B300 and MXFP4 for AMD MI350X/MI355X, exposing an OpenAI-compatible API with reasoning_effort control. Weights are available on Hugging Face and ModelScope.