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 long procedures and 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.

翻译：按需聚合物发现对于从生物医学到增强材料等各个行业都至关重要。聚合物实验通常涉及漫长的试错过程，导致流程冗长且资源消耗巨大。在这些过程中，机器学习已在性质预测和潜空间搜索方面加速了科学发现。然而，由于基础设施限制，实验室研究人员往往难以直接访问相关代码和模型以获取特定结构与性质信息。本文提出了一种集成于终端的闭环聚合物结构-性质预测器，用于早期聚合物发现。该框架通过大语言模型的推理能力，为用户提供性质预测、性质导向的聚合物结构生成以及结构修改功能。SMILES序列的生成受合成可及性分数与合成复杂度评分（SC Score）的引导，以确保生成的聚合物结构尽可能接近可合成的单体层面结构。该框架解决了为实验室研究人员生成新型聚合物结构的挑战，从而为聚合物研究提供计算层面的洞见。