After a century of superconductivity exploration, there's finally an AI teammate. On July 3, Alibaba's DAMO Academy, together with Renmin University Gaoling AI School and University of Chinese Academy of Sciences, releases the industry's first AI Agent dedicated to superconducting material discovery — ElementsClaw — scanning 2.4 million stable crystals in just 28 GPU hours, predicting 68,000 potential superconductors, and ultimately experimentally synthesizing 4 superconductors completely unknown to humanity. Comparison: the SuperCon superconductivity database has accumulated only about 2,000 kinds in a hundred years. ElementsClaw pushes the "audition + verification" hit rate to 40%, more than an order of magnitude higher than the natural superconductivity rate of about 3%. ## Agent route vs. single-point model GNoME and MatterGen have made it to Nature, but both are too single-point — they only answer "this might be a superconductor", without telling you whether there's literature, whether it's synthesizable, whether it's toxic. ElementsClaw takes a "general-plus-specialist" Agent route: the bottom layer is a 1B-parameter geometric deep graph neural network Elements, pretrained on 125 million molecular structures, the first time validating that the Scaling Law still holds on a non-LLM architecture. Four professional "pliers" — Elements-T predicts critical temperature (MAE 0.99K), Elements-C judges superconductivity (AUC 0.996), Elements-E evaluates stability, Elements-G generates new structures. The outermost layer is a large-model brain, reading papers, querying databases, designing experimental plans, like a real material scientist. ## 4 new materials, 4 paths The most amazing thing is that the 4 superconductors were discovered in completely different ways — 1. "Net-crosser" Hf21Re25: existed in theoretical libraries but no one tried it (Tc=2.5K); 2. "Wrongly accused" Zr4VRe7: humans calculated the structure wrong (Tc=3.5K); 3. "Out of nothing" HfZrRe4: not in any known library, AI generated it in the ternary system (Tc=5.9K); 4. "Drawing inferences" Zr3ScRe8: summarized structural motifs from the previous discovery, Hf replaced by Sc (Tc=6.5K). ## Commentary DAMO Academy's real contribution this time is running through the "AI prediction—synthesis—verification" complete closed loop — this path is equally applicable in biopharma, climate modeling, and energy materials. What's even more commendable is that the research team has open-sourced all 2.4 million crystal predictions (science.damo-academy.com), free for the academic community to mine. This kind of open posture is worth far more than the 4 superconductors themselves. Of course, we need to stay sober: 6.5K is still far from room-temperature superconductivity. But walking through this path is more critical than discovering a few new materials — it opens a new paradigm of scientific discovery.