迈向材料科学的智能体化智能

摘要

人工智能与材料科学的交汇带来了变革性机遇，但实现真正的发现加速需要超越任务孤立的微调模型，转向能在完整发现循环中规划、行动和学习的智能体系统。本文提出独特的流程中心视角，涵盖从语料库构建与预训练、领域自适应与指令微调，到连接仿真与实验平台的目标导向型智能体。与既往综述不同，我们将全流程视为端到端系统进行优化，以取得实质性发现成果而非替代性基准指标。这一视角使我们能追溯上游设计选择（如数据整理和训练目标）如何通过有效的功劳分配与下游实验成功相衔接。为搭建跨学科桥梁并建立共同参照系，我们首先提出整合性框架，统一人工智能与材料科学在术语体系、评估标准和工作流程阶段的认知。继而通过双重视角解析该领域：从人工智能视角，详述大语言模型在文献挖掘、材料表征和性能预测中的模式识别、预测分析和自然语言处理优势；从材料科学视角，重点分析其在材料设计、工艺优化，以及通过与外部工具（如密度泛函理论计算、机器人实验室）集成加速计算工作流方面的应用。最后，我们对比被动响应式方法与智能体设计范式，在梳理现有成果的同时，推动构建具备自主性、记忆能力和工具使用能力的长期目标追寻系统。本综述为开发面向新颖实用材料发现的自主化、安全感知的大语言模型智能体绘制了实用路线图。

English

The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-isolated, fine-tuned models toward agentic systems that plan, act, and learn across the full discovery loop. This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining, through domain adaptation and instruction tuning, to goal-conditioned agents interfacing with simulation and experimental platforms. Unlike prior reviews, we treat the entire process as an end-to-end system to be optimized for tangible discovery outcomes rather than proxy benchmarks. This perspective allows us to trace how upstream design choices-such as data curation and training objectives-can be aligned with downstream experimental success through effective credit assignment. To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science. We then analyze the field through two focused lenses: From the AI perspective, the survey details LLM strengths in pattern recognition, predictive analytics, and natural language processing for literature mining, materials characterization, and property prediction; from the materials science perspective, it highlights applications in materials design, process optimization, and the acceleration of computational workflows via integration with external tools (e.g., DFT, robotic labs). Finally, we contrast passive, reactive approaches with agentic design, cataloging current contributions while motivating systems that pursue long-horizon goals with autonomy, memory, and tool use. This survey charts a practical roadmap towards autonomous, safety-aware LLM agents aimed at discovering novel and useful materials.

迈向材料科学的智能体化智能

Towards Agentic Intelligence for Materials Science

摘要

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