用大模型+流匹配生成新晶体，效率提升三倍以上。
FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions
- 以大模型为基分布，结合黎曼流匹配迭代优化晶体结构。
- 生成稳定晶体率提升3倍，新颖独特晶体率增50%。
- 生成晶体更接近真实结构，降低后续计算成本。
材料发现是具有革命性潜力的研究领域，涉及碳捕获、可再生能源和电子等多个方向。然而，化学空间规模庞大，难以通过实验全面探索。本文提出FlowLLM，一种结合大语言模型（LLMs）与黎曼流匹配（RFM）的生成模型，用于设计新型晶态材料。首先微调一个大模型，学习元稳定晶体在文本表示下的有效基分布；随后转换为图表示，由RFM模型从大模型采样并迭代优化原子坐标与晶格参数。该方法显著优于现有最优技术：稳定材料生成率提升超过三倍，稳定、独特且新颖晶体的生成率提高约50%，同时生成晶体更接近松弛态，大幅降低后续计算成本。
原文摘要 · Abstract (English)
Material discovery is a critical area of research with the potential to revolutionize various fields, including carbon capture, renewable energy, and electronics. However, the immense scale of the chemical space makes it challenging to explore all possible materials experimentally. In this paper, we introduce FlowLLM, a novel generative model that combines large language models (LLMs) and Riemannian flow matching (RFM) to design novel crystalline materials. FlowLLM first fine-tunes an LLM to learn an effective base distribution of meta-stable crystals in a text representation. After converting to a graph representation, the RFM model takes samples from the LLM and iteratively refines the coordinates and lattice parameters. Our approach significantly outperforms state-of-the-art methods, increasing the generation rate of stable materials by over three times and increasing the rate for stable, unique, and novel crystals by $\sim50\%$ - a huge improvement on a difficult problem. Additionally, the crystals generated by FlowLLM are much closer to their relaxed state when compared with another leading model, significantly reducing post-hoc computational cost.
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