[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-damo-elementsclaw-superconductor":3,"topics-all":40,"news-related-dd34ae0f-375b-417d-adb7-887f6d40a199":58},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":27,"news_slug":33,"published_at":34,"created_at":35,"modified_at":36,"is_published":37,"publish_type":38,"image_url":13,"view_count":39},"dd34ae0f-375b-417d-adb7-887f6d40a199","达摩院 ElementsClaw：AI 智能体当上\"超导材料学家\"，28 GPU 小时找到 4 种新材料","百年超导探索终于有了 AI 队友。\n\n7 月 3 日，阿里达摩院联合人大高瓴人工智能学院、中科院大学发布业内首个专攻超导材料发现的 AI 智能体 **ElementsClaw**——仅 **28 个 GPU 小时**扫遍 240 万种稳定晶体，预测 6.8 万种可能超导，最终实验合成 **4 种人类此前完全未知的新超导体**。\n\n对比：超导数据库 SuperCon 百年累积也才 2000 余种。ElementsClaw 把\"海选+验证\"命中率拉到 40%，比自然界约 3% 的天然超导比例高出一个数量级。\n\n## 智能体路线 vs 单点模型\n\nGNoME 与 MatterGen 已登 Nature，但都太单点——只回答\"这可能是超导\"，不告诉你文献有没有、合得合不、有没有毒。\n\nElementsClaw 走的是 **\"通专融合\"智能体路线**：底层是 10 亿参数的几何深度图神经网络 Elements，在 1.25 亿分子结构上预训练，首次在非 LLM 架构上验证 Scaling Law 仍成立。四只专业\"钳子\"——Elements-T 预测临界温度（MAE 0.99K）、Elements-C 判断超导（AUC 0.996）、Elements-E 评稳定性、Elements-G 生成新结构。最外层是大模型大脑，读论文、查数据库、设计实验方案，像真正的材料学家。\n\n## 4 种新材料，4 条路径\n\n最让人叫绝的是 4 种超导体的发现方式完全不同——\n\n1. **\"漏网之鱼\" Hf21Re25**：理论库里有却没人试过（Tc=2.5K）；\n2. **\"沉冤得雪\" Zr4VRe7**：人类把结构算错了（Tc=3.5K）；\n3. **\"无中生有\" HfZrRe4**：不在任何已知库里，AI 在三元体系生成（Tc=5.9K）；\n4. **\"举一反三\" Zr3ScRe8**：从前一个发现总结结构模体，Hf 换 Sc（Tc=6.5K）。\n\n## 评论\n\n达摩院这次真正的贡献是跑通 **\"AI 预测—合成—验证\"完整闭环**——这条路径在生物医药、气候模拟、能源材料里同样适用。\n\n更值得称道的是，研究团队把 240 万种晶体的全部预测数据开放（science.damo-academy.com），学界免费挖掘。这种开放姿态，价值远大于 4 种超导体本身。\n\n当然也要清醒：6.5K 距离室温超导还远得很。但走通这条路比发现几种新材料更关键——它打开的是一种新的科学发现范式。","https:\u002F\u002Fwww.qbitai.com\u002F2026\u002F07\u002F442452.html","3bd971a8-3897-43d9-84ac-43879efd2f94",[10,14,18,21,24],{"id":11,"name":12,"slug":12,"description":13,"color":13},"6ad31a14-c0da-42df-81fd-564281f768db","agentic-ai",null,{"id":15,"name":16,"slug":16,"description":17,"color":13},"9112951a-2abb-4214-b63a-385ec7afb2ba","ai-for-science","AI for Science 专题：追踪 AI 在生命科学、化学材料、物理世界模型等科学方向的关键突破",{"id":19,"name":20,"slug":20,"description":13,"color":13},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",{"id":22,"name":23,"slug":23,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",{"id":25,"name":26,"slug":26,"description":13,"color":13},"b1853a5a-d940-42b7-94f9-0488ee3f2cf7","new-model",[28],{"id":29,"lang":30,"title":31,"summary":32,"content":13},"bc644d22-88a4-4d34-b9be-1bc1756e8768","en","ElementsClaw: agents find 4 materials in 28 GPU-hours","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.","damo-elementsclaw-superconductor","2026-07-04T02:00:00Z","2026-07-04T02:07:23.868862Z","2026-08-19T02:08:40.142862Z",true,"agent",201,[41,49],{"slug":16,"tag_slug":16,"title_zh":42,"title_en":43,"intro_zh":44,"intro_en":45,"id":46,"is_active":37,"created_at":47,"modified_at":48},"AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":50,"tag_slug":50,"title_zh":51,"title_en":52,"intro_zh":53,"intro_en":54,"id":55,"is_active":37,"created_at":56,"modified_at":57},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":59},[60,65,70,75,80,85],{"id":61,"title":62,"news_slug":63,"published_at":64},"777afb24-262f-45cc-961f-d5d49ad42883","AgentOPSD 用递归贝叶斯信念破解多轮 Agent 强化学习的信用分配：清华\u002F浙大\u002F美团让 GRPO 学会看哪个 turn 决定胜负","agentopsd-recursive-belief-credit-assignment","2026-08-07T02:00:00+00:00",{"id":66,"title":67,"news_slug":68,"published_at":69},"b2c169c6-5150-4423-8073-bf480a2d8745","腾讯 UniPert-G2CP 登《Cell》主刊：把基因扰动和化学扰动塞进同一个语义空间","tencent-unipert-g2cp-cell-virtual-cell","2026-07-31T07:49:00+00:00",{"id":71,"title":72,"news_slug":73,"published_at":74},"9d775769-35ec-4107-bcec-601427fda303","云迹科技 WAIC 首发「人机共生世界价值模型」:把具身智能从「规则控制」推向「价值驱动」","yunji-waic-value-driven-embodied","2026-07-18T00:15:00+00:00",{"id":76,"title":77,"news_slug":78,"published_at":79},"12df58ff-0771-4c0e-bf0e-00bfdc8112bb","SkillCenter：21 万可审计 Agent 技能库，SQLite 离线检索","skillcenter-sqlite-agent","2026-07-09T04:30:00+00:00",{"id":81,"title":82,"news_slug":83,"published_at":84},"1316635c-88e1-41b6-a45c-df8ef217cf3f","PaperPilot 把文献搜索改写成「工作流归纳」：可编辑 DAG 把多轮检索错误率干到 0%","paperpilot-workflow-induction","2026-07-01T08:21:23+00:00",{"id":86,"title":87,"news_slug":88,"published_at":89},"6de305c2-91b2-47fb-b1e4-dfb5f1e711c8","WorldEvolver：把世界模型装进 LLM Agent 的「即时记忆」","worldevolver-llm-agent-world-model","2026-06-30T18:04:00+00:00"]