发表机构
Georgia Institute of Technology; Matmerize Inc.（佐治亚理工学院; Matmerize公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究结合VFS与polyBART、POLYT5模型生成100万个可合成的ROP聚合物结构数据集，经严格筛选后可作为下游可持续应用的宝贵资源。
AI 中文摘要
通过环状单体的开环聚合（ROP）合成的聚合物，因具备化学可回收性且可应用于多个关键领域，成为一类重要材料。本研究结合虚拟正向合成（VFS）与聚合物专家语言模型，生成了100万个可合成的ROP聚合物结构数据集，并通过严格化学启发式规则进行筛选。VFS通过对现有单体应用已知反应生成ROP聚合物；聚合物基础模型polyBART与POLYT5进一步助力ROP候选物生成，其中polyBART探索其学习到的潜在空间，POLYT5则通过序列到序列生成产生候选物。所得ROP聚合物需经自动化验证管道与本研究首次提出的一套全面的化学启发式规则相结合的稳健过滤标准，以确保新颖性、有效性及整体数据质量。本研究希望该数据集能为下游可持续应用提供有价值的资源。
英文摘要
Polymers synthesized via ring-opening polymerization (ROP) of cyclic monomers represent an important class of materials due to their chemical recyclability and possible insertion in several critical applications. We present a dataset of 1 million synthetically realizable ROP polymer structures generated through a combination of Virtual Forward Synthesis (VFS) and polymer expert language models and qualified by stringent chemical heuristics. VFS is used to generate ROP polymers by applying known reactions to existing monomers. The polymer foundation models polyBART and POLYT5 further enable the generation of ROP candidates, with polyBART exploring its learned latent space and POLYT5 producing candidates via sequence-to-sequence generation. The resulting ROP polymers are subjected to robust filtering criteria to ensure novelty, validity and overall data quality through a combination of automated validation pipelines and a comprehensive set of chemist-informed heuristic rules introduced in this work for the first time. We hope that this dataset will serve as a valuable resource for downstream sustainable applications.
