Damo Academy unveils an AI agent able to discover superconductors, which could revolutionise scientific materials research and innovation

# Can Alibaba’s AI transform superconducting materials discovery? Source: [https://www.scmp.com/tech/big-tech/article/3359335/alibabas-elements-claw-ai-agent-unearths-four-new-superconductors](https://www.scmp.com/tech/big-tech/article/3359335/alibabas-elements-claw-ai-agent-unearths-four-new-superconductors) Alibaba Group Holding’s Damo Academy has unveiled what it calls the industry’s first artificial intelligence agent for discovering superconducting materials, saying that the tool has already found four previously unknown compounds that were later verified in laboratory experiments\. [Superconducting](https://sc.mp/ic4qq?utm_source=copy-link&utm_campaign=3358642&utm_medium=share_widget)materials are substances able to conduct electricity without resistance and expel magnetic fields when cooled to low temperatures – a capability breakthrough that could revolutionise power grids, quantum computing and high\-speed maglev trains\. Discovering new superconductors has long relied on laborious, trial\-and\-error experiments because scientists still lack a complete theoretical framework to predict superconductivity\. Over the decades, researchers have only accumulated about 2,000 known superconducting materials in the widely used SuperCon database\. The[AI agent](https://sc.mp/k2hp9?utm_source=copy-link&utm_campaign=3358639&utm_medium=share_widget), dubbed Elements Claw, was designed to accelerate the timeline by scanning scientific literature and screening millions of crystal structures to propose candidate materials for laboratory validation, according to Damo\. The system was developed in collaboration with Renmin University of China and the University of Chinese Academy of Sciences\. Powered by a specialised, one\-billion\-parameter foundation model trained on 125 million molecular and crystal structures, Elements Claw screened 2\.4 million stable crystal structures in 28 hours of graphics processor computing time\. It identified about 68,000 candidates with superconducting potential, which it then narrowed down to the most promising options for physical testing\.