让AI在材料模拟中积累经验，像专家一样越做越准。
From Experiments to Expertise: Scientific Knowledge Consolidation for AI-Driven Computational Physics
- AI执行后记录全过程，下次计算前主动调用经验
- 知识累积使推理开销降低67%，误差从47%降至3%
- 迁移到新物质仍保持1%误差，零失败
尽管大语言模型已使AI代理在计算材料科学中具备高效执行能力，但完成百次模拟并不等于成为研究者。真正的科研在于知识的逐步积累——识别失败方法、发现跨体系规律、并应用于新问题。然而，当前AI驱动的计算科学普遍将每次执行视为孤立事件，忽视中间积累的经验。本文提出QMatSuite开源平台，解决这一问题：智能体在每次运行后完整记录成果，新任务前检索已有知识，并通过专门的反思环节修正错误、提炼跨化合物模式。在六步量子力学模拟工作流的测试中，知识积累使推理开销减少67%，准确率从47%偏差提升至仅3%偏差；迁移至陌生材料时，实现1%偏差且无任何流程失败。
原文摘要 · Abstract (English)
While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations does not make a researcher. What distinguishes research from routine execution is the progressive accumulation of knowledge - learning which approaches fail, recognizing patterns across systems, and applying understanding to new problems. However, the prevailing paradigm in AI-driven computational science treats each execution in isolation, largely discarding hard-won insights between runs. Here we present QMatSuite, an open-source platform closing this gap. Agents record findings with full provenance, retrieve knowledge before new calculations, and in dedicated reflection sessions correct erroneous findings and synthesize observations into cross-compound patterns. In benchmarks on a six-step quantum-mechanical simulation workflow, accumulated knowledge reduces reasoning overhead by 67% and improves accuracy from 47% to 3% deviation from literature - and when transferred to an unfamiliar material, achieves 1% deviation with zero pipeline failures.
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