Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We introduce Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), an active learning framework where large language models autonomously design, execute, and interpret atomistic simulations. In MASTER, a multimodal system translates natural language into density functional theory workflows, while higher-level reasoning agents guide discovery through a hierarchy of strategies, including a single agent baseline and three multi-agent approaches: peer review, triage-ranking, and triage-forms. Across two chemical applications, CO adsorption on Cu-surface transition metal (M) adatoms and on M-N-C catalysts, reasoning-driven exploration reduces required atomistic simulations by up to 90% relative to trial-and-error selection. Reasoning trajectories reveal chemically grounded decisions that cannot be explained by stochastic sampling or semantic bias. Altogether, multi-agent collaboration accelerates materials discovery and marks a new paradigm for autonomous scientific exploration.

翻译：人工智能正在重塑科学探索，但大多数方法仅自动化程序性任务，而未参与科学推理，这限制了发现的自主性。我们提出了电子结构推理模拟与理论材料智能体（MASTER），一种主动学习框架，其中大语言模型自主设计、执行并解释原子尺度模拟。在MASTER中，多模态系统将自然语言转化为密度泛函理论工作流，而高层推理智能体通过策略层次指导发现过程，包括单智能体基线及三种多智能体方法：同行评审、分诊排序和分诊表单。在两个化学应用案例中——Cu表面过渡金属（M）吸附原子上的CO吸附以及M-N-C催化剂上的CO吸附——推理驱动的探索相较于试错选择，将所需原子尺度模拟减少了高达90%。推理轨迹揭示了基于化学原理的决策，这些决策无法通过随机采样或语义偏差解释。总体而言，多智能体协作加速了材料发现，并标志着自主科学探索的新范式。