[2603.03946] Lang2Str: Two-Stage Crystal Structure Generation with LLMs and Continuous Flow Models

Computer Science > Machine Learning arXiv:2603.03946 (cs) [Submitted on 4 Mar 2026] Title:Lang2Str: Two-Stage Crystal Structure Generation with LLMs and Continuous Flow Models Authors:Cong Liu, Chengyue Gong, Zhenyu Liu, Jiale Zhao, Yuxuan Zhang View a PDF of the paper titled Lang2Str: Two-Stage Crystal Structure Generation with LLMs and Continuous Flow Models, by Cong Liu and 4 other authors View PDF HTML (experimental) Abstract:Generative models hold great promise for accelerating material discovery but are often limited by their inflexible single-stage generative process in designing valid and diverse materials. To address this, we propose a two-stage generative framework, Lang2Str, that combines the strengths of large language models (LLMs) and flow-based models for flexible and precise material generation. Our method frames the generative process as a conditional generative task, where an LLM provides high-level conditions by generating descriptions of material unit cells' geometric layouts and properties. These descriptions, informed by the LLM's extensive background knowledge, ensure reasonable structure designs. A conditioned flow model then decodes these textual conditions into precise continuous coordinates and unit cell parameters. This staged approach combines the structured reasoning of LLMs and the distribution modeling capabilities of flow models. Experimental results show that our method achieves competitive performance on \textit{ab initio} material genera...