发表机构
The Chinese University of Hong Kong（香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
UniPolymer是聚酰亚胺设计的统一框架，通过建立结构-属性映射、生成候选并评估排序，提升了属性预测与候选评估性能，减少了高成本实验的候选数量。
AI 中文摘要
设计具有特定玻璃化转变温度（Tg）的聚酰亚胺结构极具挑战性。现有方法主要聚焦于目标条件下的生成，缺乏对生成结构与目标属性一致性的评估，这导致偏离设计目标的低质量候选结构进入后续流程，增加了无效实验并延长了开发周期。为解决该问题，我们提出UniPolymer——一种用于聚酰亚胺设计中属性预测、目标条件生成、候选结构评估及结构推荐的统一框架，并构建了包含10066个带Tg标签的去重聚酰亚胺重复单元的数据集PITg-Curated。为提升生成候选结构与目标Tg的一致性，UniPolymer首先通过自监督化学语义学习、结构一致性增强及多尺度信息融合建立可靠的结构-属性关系映射；随后，模型采用连续-离散联合Tg表示引导SELFIES的自回归生成；生成的候选结构进一步通过冻结属性预测器和聚酰亚胺特异性结构约束进行评估，并根据其与目标Tg的偏差排序，从而阻止偏离目标的结构进入后续验证阶段。实验结果显示，UniPolymer的属性预测准确率R²=0.93，候选结构评估通过率为73.79%，分别比最优基线高2%和1.21%；同时，推荐候选的预测Tg值与分子动力学模拟结果高度一致，减少了进入高成本实验阶段的候选数量。
英文摘要
Designing polyimide structures with specific glass transition temperatures (Tg) is highly challenging. Existing methods primarily focus on target-conditioned generation, lacking an assessment of the consistency between the generated structure and the target properties. This leads to low-quality candidates deviating from the design objective entering subsequent processes, increasing invalid experiments and prolonging the development cycle. To address this issue, we propose UniPolymer, a unified framework for property prediction, target-conditioned generation, candidate evaluation, and structure recommendation in polyimide design and a dataset containing 10066 deduplicated polyimide repeating units with Tg tags (PITg-Curated) was constructed. To improve the consistency between generated candidate structures and the target Tg, UniPolymer first establishes a reliable structure-property relationship mapping through self-supervised chemical semantic learning, structural consistency enhancement, and multi-scale information fusion. Subsequently, the model employs a continuous-discrete joint Tg representation to guide the autoregressive generation of SELFIES. The generated candidate structures are further evaluated using a frozen property predictor and polyimide-specific structural constraints, and ranked according to their deviation from the target Tg, thereby preventing structures deviating from the target from entering the subsequent validation stage. Experimental results show that UniPolymer achieved a property prediction accuracy of R^2=0.93 and a candidate structure evaluation pass rate of 73.79%, which are 2% and 1.21% higher than the best baseline, respectively. Meanwhile, the predicted Tg values of the recommended candidates are in high agreement with the results of molecular dynamics simulations, thereby reducing the number of candidates that enter the high-cost experimental stage.
