Reasoning-to-Simulation: An Agentic Framework for Discovery of Electrolyte Materials

Jurģis Ruža, Rafael Gomez-Bombarelli

Keywords: LLM, large language models, agentic AI, materials discovery, materials optimization, agents, battery materials

TL;DR: We propose an agentic framework that leverages Large Language Models (LLMs) as autonomous orchestrators for closed-loop materials discovery.

Abstract: The discovery of novel metal-ion battery electrolytes is hindered by the vast combinatorial space of solvent-salt formulations and by the complexity of physics-based validation. We propose an agentic framework that leverages Large Language Models (LLMs) as autonomous orchestrators for closed-loop materials discovery. Our system features a Discovery Agent that performs multi-generational composition optimization and a Molecular Dynamics (MD) simulation Assistant that translates natural language prompts into executable liquid electrolyte molecular dynamics simulations. By integrating generative reasoning with automated simulation verification, our framework enables an autonomous "reasoning-to-simulation" cycle that identifies high-performance electrolytes. This work demonstrates the ability of agentic LLMs to bridge the gap between chemical intuition and rigorous computational materials science by discovering a high-performance liquid electrolyte solvent formulation with computationally measured 106 mS/cm ionic conductivity.

Submission Track: Full Paper

Submission Category: AI-Guided Design

Submission Number: 37