The discovery of novel materials is critical for global energy and quantum technology transitions. While deep learning has fundamentally reshaped this landscape, existing predictive or generative models typically operate in isolation, lacking the autonomous orchestration required to execute the full discovery process. Here we present ElementsClaw, an agentic framework for materials discovery that synergizes Large Atomic Models (LAMs) with Large Language Models (LLMs). In response to varied human queries, ElementsClaw orchestrates a suite of LAM tools finetuned from our proposed 1-billion-parameter model Elements for atomic-scale numerical computation, while leveraging LLMs for high-level semantic reasoning. This shift moves AI-driven materials science from isolated processes toward integrated and human interactive discovery. Applied to superconductors, ElementsClaw screens 2.4 million crystals in just 28 GPU hours to identify 68,000 high-confidence candidates (The complete dataset of screened superconductors is available at https://developer.damo-academy.com/material), expanding known superconducting space by orders of magnitude compared to datasets curated over decades. Critically, ElementsClaw achieves a high success rate in identifying superconductors hidden in literature and discovers four novel experimentally verified superconductors, exemplified by Zr3ScRe8 with a transition temperature of 6.8 K and HfZrRe4 at 6.7 K. Together, our results establish a knowledge integrated, autonomously orchestrated, and experimentally grounded paradigm for materials discovery.

翻译：新材料的发现对于全球能源和量子技术转型至关重要。尽管深度学习已从根本上重塑了这一领域，但现有的预测或生成模型通常孤立运作，缺乏执行完整发现过程所需的自主编排能力。在此，我们提出ElementsClaw——一个协同大型原子模型（LAMs）与大型语言模型（LLMs）的材料发现智能框架。针对多样化的用户查询，ElementsClaw编排一套基于我们提出的10亿参数模型Elements微调而来的LAM工具，用于原子尺度数值计算，同时利用LLMs进行高层语义推理。这一转变将AI驱动的材料科学从孤立过程推向集成化、人机交互式发现。应用于超导体领域时，ElementsClaw仅用28个GPU小时筛选了240万种晶体，识别出68000个高置信度候选材料（完整筛选超导体数据集见https://developer.damo-academy.com/material），将已知超导空间比以往数十年整理的数据集扩大了数个数量级。关键在于，ElementsClaw在识别文献中隐藏的超导体方面取得了高成功率，并发现了四种经过实验验证的新型超导体，例如转变温度为6.8K的Zr3ScRe8和6.7K的HfZrRe4。综上，我们的研究结果为材料发现建立了一种知识集成、自主编排且经实验验证的范式。