Periodic Labs：让 AI 科学家进入物理实验室

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Periodic Labs 联合创始人 Liam Fedus 与 Ekin Doğuş Çubuk 和主持人讨论：AI 要发现新材料，为什么不能只依靠推理与已有论文，还需要提出假设、运行物理实验并从结果中学习。节目介绍将材料发现分为预测、合成与表征的循环，关注真实实验中的噪声、不确定性和失败数据。

本期还涉及物理环境中的强化学习、模拟与实验之间的关系，以及如何把 AI、化学、物理和自动化硬件连接成自主实验室。两位嘉宾介绍他们所说的“合成超级智能”，以及让模型学习整个科研过程而不只是最终发表结果的设想。

Periodic Labs co-founders Liam Fedus and Ekin Doğuş Çubuk discuss why discovering new materials requires more than reasoning over existing papers: AI systems also need to form hypotheses, run physical experiments, and learn from outcomes. The publisher’s introduction frames discovery as a loop of prediction, synthesis, and characterization, with noise, uncertainty, and failed experiments as central challenges.

The episode also covers reinforcement learning in physical environments, the relationship between simulation and experiments, and connecting AI, chemistry, physics, and automated hardware into autonomous laboratories. The guests outline their vision of synthesis superintelligence and models that learn from the entire scientific process, rather than only published results.

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编辑导览，依据原始来源 ↗，整理于 2026-10-09。中英文均由本站概括整理。内容介绍与下方原话、主张的逐条核对分开标注。

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