{"ID":2832242,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.06404","arxiv_id":"2512.06404","title":"GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols","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.","short_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 wit...","url_abs":"https://arxiv.org/abs/2512.06404","url_pdf":"https://arxiv.org/pdf/2512.06404v1","authors":"[\"Mohammad Soleymanibrojeni\",\"Roland Aydin\",\"Diego Guedes-Sobrinho\",\"Alexandre C. Dias\",\"Maurício J. Piotrowski\",\"Wolfgang Wenzel\",\"Celso Ricardo Caldeira Rêgo\"]","published":"2025-12-06T11:28:35Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cond-mat.mtrl-sci\",\"physics.chem-ph\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}