A-Lab

Headlines · 2

1. 2d
   AI in the lab: who is AI for Science really working for?

   This commentary, credited to the think tank Sice Zhiku and republished by TMTPost, 36Kr and Huxiu, surveys what AI for Science has actually delivered and where its limits lie. The author says that as of 2026, more than 700 materials predicted by Google DeepMind's GNoME have been synthesized in labs worldwide, including 528 potential solid-state electrolyte candidates and 52,000 graphene-like layered compounds; GNoME had predicted 2.2 million new potential crystal structures, over 380,000 of them judged thermodynamically stable. The piece also cites automated systems such as A-Lab, which it says produced 41 new materials in under a month, and says an AI-assisted molecule showed lung-function improvement in Phase IIa trials and is headed for Phase III in 2026. The author pushes back on the hype: materials scientists have argued many predicted structures are variants of known phases, and AI has mainly moved the bottleneck from generating ideas to validating them, with pilot lines, yields, regulation and scarce validation capacity still governing commercialization, and profits not necessarily staying at the model layer.

   钛媒体 · 🔥 11
2. 3d
   PAIR 2026 roundtable: who verifies AI-generated scientific findings?

   PAIR Conference 2026, themed on the frontier of next-generation physical intelligence, was held in Shanghai on Sept 20–21, and its "next-generation scientific discovery" forum closed with a roundtable on how model outputs become trustworthy scientific evidence — cross-disciplinary verification, reproduction and applicability limits. Panelists said AI generates candidates far faster than experiments can check them: the best AI virtual-cell data covers only about 100 perturbed genes out of roughly 20,000 human genes, GNoME's ~2.2 million predicted crystal structures were criticized as largely simple extensions of known compounds, and A-Lab corrected its "new materials" wording after re-analysis of its phase identification. Their proposed principles: fix verification standards before experiments and independently of results, separate the generator from the verifier, use independent labs and cross-platform replication, and require results to be independent, prospective and extrapolative.

   MIT科技评论中文 · 🔥 0

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