Towards Fully Automated Molecular Simulations: Multi-Agent Framework for Simulation Setup and Force Field Extraction
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Abstract (arXiv)
Automated characterization of porous materials has the potential to accelerate materials discovery, but it remains limited by the complexity of simulation setup and force field selection. We propose a multi-agent framework in which LLM-based agents can autonomously understand a characterization task, plan appropriate simulations, assemble relevant force fields, execute them and interpret their results to guide subsequent steps. As a first step toward this vision, we present a multi-agent system for literature-informed force field extraction and automated RASPA simulation setup. Initial evaluations demonstrate high correctness and reproducibility, highlighting this approach's potential to enable fully autonomous, scalable materials characterization.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 12 Sep 2025.
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14 of 41 references.
References · 14
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- 2025Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery
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