Scholarlink

<p>Predictive atomistic simulations have propelled materials discovery, yet routine setup and debugging still demand computer specialists. This know-how gap limits the use of 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 Quantum ESPRESSO input files that pass early execution validation for &#xa0;≈&#xa0;80% of 295 diverse benchmarks. Zero-shot generation succeeds for 14.2% of all prompts, and among cases that do not succeed initially, 76.3% are autonomously recovered by the automated error-handling loop, with the attempt-wise success rate decaying exponentially toward a 7% baseline. Compared with LLM-only baselines, GENIUS increases inference and computational efficiency and virtually eliminates hallucinations. The framework democratizes electronic-structure DFT simulations by intelligently automating protocol generation, validation, and repair, enabling large-scale screening and accelerating ICME design loops worldwide across academia and industry.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

GENIUS: an agentic AI framework for autonomous design and execution of simulation protocols

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月29日

查看全文
  • Mohammad Soleymanibrojeni,
  • Roland Aydin,
  • Diego Guedes-Sobrinho,
  • Alexandre C. Dias,
  • Maurício J. Piotrowski,
  • Wolfgang Wenzel,
  • Celso Ricardo Caldeira Rêgo

摘要

Predictive atomistic simulations have propelled materials discovery, yet routine setup and debugging still demand computer specialists. This know-how gap limits the use of 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 Quantum ESPRESSO input files that pass early execution validation for  ≈ 80% of 295 diverse benchmarks. Zero-shot generation succeeds for 14.2% of all prompts, and among cases that do not succeed initially, 76.3% are autonomously recovered by the automated error-handling loop, with the attempt-wise success rate decaying exponentially toward a 7% baseline. Compared with LLM-only baselines, GENIUS increases inference and computational efficiency and virtually eliminates hallucinations. The framework democratizes electronic-structure DFT simulations by intelligently automating protocol generation, validation, and repair, enabling large-scale screening and accelerating ICME design loops worldwide across academia and industry.