用大模型实现可合成的聚合物结构自动设计
Polymer-Agent: Large Language Model Agent for Polymer Design
- 基于大模型推理实现属性预测与结构生成闭环
- 生成结构兼顾合成可及性与复杂度评分
- 适合缺乏代码能力的实验室研究人员使用
按需发现聚合物对生物医学到增强材料等多个行业至关重要。聚合物实验通常依赖长期试错，耗费大量资源。虽然机器学习已在性质预测和潜在空间搜索方面加速科学发现，但实验室研究人员因基础设施限制难以获取代码和模型来提取独立结构与性质。本文提出一个集成于终端的闭环聚合物结构-性质预测框架，通过大模型推理支持性质预测、属性引导的结构生成和结构修改。生成的SMILES序列受合成可及性得分与合成复杂度得分（SC Score）约束，确保结构尽可能接近可合成的单体级结构。该框架解决了实验室研究人员生成新型聚合物结构的难题，为聚合物研究提供计算洞察。
原文摘要 · Abstract (English)
On-demand Polymer discovery is essential for various industries, ranging from biomedical to reinforcement materials. Experiments with polymers have a long trial-and-error process, leading to use of extensive resources. For these processes, machine learning has accelerated scientific discovery at the property prediction and latent space search fronts. However, laboratory researchers cannot readily access codes and these models to extract individual structures and properties due to infrastructure limitations. We present a closed-loop polymer structure-property predictor integrated in a terminal for early-stage polymer discovery. The framework is powered by LLM reasoning to provide users with property prediction, property-guided polymer structure generation, and structure modification capabilities. The SMILES sequences are guided by the synthetic accessibility score and the synthetic complexity score (SC Score) to ensure that polymer generation is as close as possible to synthetically accessible monomer-level structures. This framework addresses the challenge of generating novel polymer structures for laboratory researchers, thereby providing computational insights into polymer research.
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