ALKEMIE智能体：用于计算材料设计的自主平台
ALKEMIE Agent: an autonomous platform for computational materials design
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中文总结 AI 辅助
该研究针对材料计算工作流程碎片化的问题，提出集成多种技术的ALKEMIE智能体平台，通过多种材料相关应用验证其能力，并展望了该类平台的未来发展方向与挑战。
中文摘要 AI 辅助
尽管材料领域已建立了强大的多尺度建模方法和高通量基础设施，但实际的材料计算工作流程仍呈碎片化且高度依赖人工，需要研究人员不断衔接软件工具、数据分析和中间决策。方法学能力与实际执行之间日益扩大的差距凸显了对新型自主计算框架的需求，该框架能以更统一、自适应的方式协调工具、知识和工作流程。本文介绍了ALKEMIE智能体，这是一个智能体平台，其中检索增强生成、材料计算知识库、已注册技能、数据库支持的溯源、AI辅助结构建模、受限任务执行、工具调用迭代和错误诊断辅助被集成在可追踪的控制循环中。ALKEMIE智能体的能力通过材料推荐、结构建模、声子计算、机器学习原子间势训练、LAMMPS模拟、从头算蒙特卡洛（AIMC）采样以及基于主动学习的材料筛选等应用得到验证。最后，我们概述了用于计算材料设计的智能体平台开发的未来方向和挑战。
英文摘要
Despite the powerful multi-scale modeling methods and high-throughput infrastructures established in the materials community, real material computation workflows remain fragmented and heavily manual, requiring researchers to constantly bridge software tools, data analysis, and intermediate decisions. This growing gap between methodological capability and practical execution highlights the need for a new kind of autonomous computational framework, one that can coordinate tools, knowledge, and workflows in a more unified and adaptive way. Here, we introduce ALKEMIE Agent, an agentic platform in which retrieval-augmented generation, a materials-computation knowledge base, registered skills, database-supported provenance, AI-assisted structure modeling, bounded task execution, tool-calling iteration, and error-diagnostic assistance are integrated within a traceable control loop. The capabilities of ALKEMIE Agent are demonstrated through applications including materials recommendation, structure modeling, phonon calculations, machine-learned interatomic potential training, LAMMPS simulations, Ab Initio Monte Carlo (AIMC) sampling, and active-learning-based materials screening. Finally, we outline the future directions and challenges for the development of agentic platforms for computational materials design.
发表机构
- Beihang University（北京航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。
