Radar / AI models / Hydrogen-1.0

Hydrogen-1.0: Deep Principle's 753B open-weight model for chemistry, materials and life sciences

Deep Principle post-trained Z.ai's GLM-5.3 on lab notebooks and discovery trajectories to build a 753B-parameter (40B active) MIT-licensed science model with a 1M-token context; the company claims 38.3% on FrontierScience-Research versus 33.3% for GPT-6 Astra.

new launchmodels · open-weightsJEV traction 0.45added 2026-11-03
Open Hydrogen-1.0 →View on the radar

Why it matters

It is a frontier-scale specialist trained on what researchers actually recorded, including failed experiments, rather than general web text.

What you could build with it

A materials-research lab could run Hydrogen-1.0 over its own experiment notes to propose candidate compounds and full experiment plans, then verify each proposal against calculations and literature before committing bench time.

Does it hold up?

Too early to judge: weights are downloadable under MIT terms but need an 8-GPU cluster, all benchmark wins are vendor-reported, and training data and recipes stay closed.

Built with Hydrogen-1.0

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technical deep dive →

First spotted on article: source.

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