从自动化模拟到自主发现：面向智能体计算材料学的层次化框架
From Automated Simulation to Autonomous Discovery: A Hierarchical Framework for Agentic Computational Materials Science
- School of Materials Science and Engineering, Beihang University（北京航空航天大学材料科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对计算材料发现中智能体系统缺乏统一评估框架的问题，提出CMA-AL层次化分类体系，定义六个自主等级并映射现有系统，以引导其向更高自主性演进。
AI中文摘要：
大语言模型、材料专用基础模型与智能体人工智能的融合正在重塑计算材料发现的研究范式。尽管高通量计算、自动化工作流和数据驱动建模极大地扩展了材料探索的规模，但核心的科学决策循环在很大程度上仍由人类主导。智能体人工智能引入了系统能够自主推理材料目标、执行模拟并优化策略的可能性。然而，此类系统的快速涌现产生了对统一且可操作的框架的迫切需求，以定义、评估和引导计算材料发现中的科学自主性。在本展望中，我们提出了计算材料智能体自主等级（CMA-AL）框架，这是一个层次化分类体系，定义了计算材料科学中自主智能体的六个等级：脚本执行器、大语言模型辅助操作员、自适应探索器、实验就绪建模器、智能体数字孪生和自扩展智能。我们进一步将新兴的智能体系统映射到该框架上，并识别出迈向更高自主性的关键科学与技术挑战。CMA-AL为表征智能体计算材料发现、评估新兴系统的成熟度以及引导其向日益自主的材料发现演进提供了共同语言。
英文摘要：
The convergence of large language models, materials-specific foundation models, and agentic artificial intelligence is reshaping the paradigm of computational materials discovery. While high-throughput computation, automated workflows, and data-driven modeling have greatly expanded the scale of materials exploration, the core scientific decision-making loop remains largely human-directed. Agentic AI introduces the possibility of systems that can autonomously reason about materials objectives, execute simulations, and refine strategies. However, the rapid emergence of such systems has created a critical need for a unified and operational framework to define, evaluate, and guide scientific autonomy in computational materials discovery. In this Perspective, we propose the Computational Materials Agent Autonomy Level (CMA-AL) framework, a hierarchical taxonomy defining six levels of autonomous agency in computational materials science: scripted excecutor, LLM-assisted operator, adaptive explorer, experiment-ready modeler, agentic digital twin, and self-extending intelligence. We further map emerging agentic systems onto the framework and identify key scientific and technological challenges toward higher autonomy. CMA-AL provides a common language for characterizing agentic computational materials discovery, evaluating the maturity of emerging systems, and guiding their evolution toward increasingly autonomous materials discovery.
