GENIUS让非专家也能自动完成材料模拟全流程。
GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols
- 用大模型+知识图谱自动生成并修复模拟参数。
- 80%任务能成功运行，76%可自动修复，成功率随时间衰减至7%。
- 大幅降低错误率和计算成本，适合科研与工业界使用。
预测性原子模拟推动了材料发现，但常规设置与调试仍需计算机专业人员。这一知识鸿沟限制了集成计算材料工程（ICME）的发展，尽管先进代码存在，却对非专家不友好。我们提出GENIUS，一种基于智能量子ESPRESSO知识图谱与分层大语言模型的自主代理框架，由有限状态错误恢复机监督。结果显示，GENIUS可将自然语言提示转化为可运行的输入文件，在295个多样化基准中约80%成功执行，其中76%可自主修复，成功率呈指数衰减至7%基线。相比仅使用大模型的基线，GENIUS将推理成本减半，几乎消除幻觉。该框架通过智能自动化协议生成、验证与修复，使电子结构密度泛函理论（DFT）模拟更易获取，助力大规模筛选，并加速学术界与工业界全球范围内的ICME设计循环。
原文摘要 · Abstract (English)
Predictive atomistic simulations have propelled materials discovery, yet routine setup and debugging still demand computer specialists. This know-how gap limits Integrated Computational Materials Engineering (ICME), where state-of-the-art codes exist but remain cumbersome for non-experts. We address this bottleneck with GENIUS, an AI-agentic workflow that fuses a smart Quantum ESPRESSO knowledge graph with a tiered hierarchy of large language models supervised by a finite-state error-recovery machine. Here we show that GENIUS translates free-form human-generated prompts into validated input files that run to completion on $\approx$80% of 295 diverse benchmarks, where 76% are autonomously repaired, with success decaying exponentially to a 7% baseline. Compared with LLM-only baselines, GENIUS halves inference costs and virtually eliminates hallucinations. The framework democratizes electronic-structure DFT simulations by intelligently automating protocol generation, validation, and repair, opening large-scale screening and accelerating ICME design loops across academia and industry worldwide.
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