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

• URL: http://arxiv.org/abs/2512.06404v1
• Date: Sat, 06 Dec 2025 11:28:35 GMT
• Title: GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols
• Authors: Mohammad Soleymanibrojeni, Roland Aydin, Diego Guedes-Sobrinho, Alexandre C. Dias, Maurício J. Piotrowski, Wolfgang Wenzel, Celso Ricardo Caldeira Rêgo,
• Abstract summary: GENIUS is 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.<n> 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.<n>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.
• Score: 32.505127447635864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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