CataPath: Expert-Augmented Path Search for Constraint-Aware Scientific Reasoning in Catalyst Design

Large Language Models (LLMs) have shown promise as scientific assistants, yet their use in open-ended material design remains limited by violations of domain-specific constraints. We study rational catalyst design as a challenging testbed for scientific reasoning, where an agent must generate chemically coherent intermediate decisions. We propose CataPath, an expert-augmented LLM agent that formulates catalyst design as search over a chemically informed action space. CataPath uses Monte Carlo Tree Search to explore design trajectories and a hybrid reward mechanism that combines property-prediction signals with an Expert Preference Reward Model trained from domain feedback. To support this framework, we curate CataDesignQR, a dataset of literature-grounded design goals, verified solutions, and expert-scored reasoning trajectories. We evaluate CataPath on two catalyst-related material-design settings, CO${}_2$ reduction electrocatalyst generation and organic structure-directing agent optimization. Experiments show that CataPath improves generation validity, proxy effectiveness, and expert alignment over other baselines. These results suggest that process-level search with expert-informed rewards can improve the reliability of LLM agents for scientific design tasks.