Synthesis-Aware Materials Redesign via Large Language Models

We propose a novel framework that leverages large language models (LLMs) to transform synthetically infeasible inorganic crystal structures into synthetically feasible ones. Unlike previous studies on synthesis predictions, which focus primarily on estimating synthesizability, our method provides actionable solutions for redesigning unsynthesizable materials into synthesizable ones. By integrating an invertible structural representation and an iterative fine-tuning strategy, our framework not only predicts synthetic feasibility but also modifies unsynthesizable materials into viable candidates. As a result, we demonstrate that LLMs can effectively modify materials of various types, enhancing their synthesizability and increasing the likelihood of successful synthesis. As an indirect experimental validation, we demonstrate that 34 materials among the top 100 redesigned (but originally unsynthesizable) structures have indeed been experimentally reported in the literature. This approach addresses a critical gap between design and synthesis in materials science, and enables the discovery of experimentally realizable compounds by employing the "learn-and-regenerate" strategy in LLMs.