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GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols

Published: December 6, 2025 | arXiv ID: 2512.06404v1

By: Mohammad Soleymanibrojeni , Roland Aydin , Diego Guedes-Sobrinho and more

Potential Business Impact:

AI helps scientists design new materials faster.

Business Areas:
Intelligent Systems Artificial Intelligence, Data and Analytics, Science and Engineering

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.

Country of Origin
🇩🇪 Germany

Repos / Data Links

Page Count
20 pages

Category
Computer Science:
Artificial Intelligence