发表机构
Texas A&M University; University of Pittsburgh; Virginia Tech; University of North Carolina Greensboro; Old Dominion University（得克萨斯农工大学; 匹兹堡大学; 弗吉尼亚理工大学; 北卡罗来纳大学格林斯伯勒分校; 奥多明尼昂大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PolymerGPT是一款基于解码器的GPT模型，可将多达37种聚合物属性融入生成过程，经实验验证其在多属性优化的聚合物生成任务中表现优异。
AI 中文摘要
聚合物属性预测与针对目标属性的逆生成设计是机器学习辅助聚合物设计的两项关键任务。前者已受到大量关注，但后者的方法却十分有限。现有方法在生成过程中聚焦于单属性优化，而宏观材料行为的精准预测需同时控制多个物理属性。本文提出了一个用于直接优化大量聚合物属性的变革性框架。我们提出了PolymerGPT，这是一种基于解码器的GPT模型，通过学习到的条件前缀将多达37种常用聚合物属性融入生成过程，它还支持指定预测结构所需骨架的骨架条件。实验结果表明，PolymerGPT在无条件和条件生成中均表现出优异性能，同时保持高有效性、唯一性和新颖性；以5种关键属性为条件生成的结构，其预测值与所有目标属性均高度匹配。
英文摘要
Polymer property prediction and inverse generative design targeting desired properties are two crucial tasks in machine learning-assisted polymer design. While the former has received considerable attention, there have been limited methods developed for the latter. Existing methods focus on single-property optimization in the generative process, whereas accurate prediction of macroscopic material behavior requires simultaneous control of multiple physical properties. In this paper, we provide a transformative framework for direct optimization of a large collection of polymer properties. We propose PolymerGPT, a decoder-based GPT model that incorporates up to 37 commonly used polymer properties into the generative process via learned conditioning prefixes. It also supports a scaffold condition that specifies a desired scaffold for predicted structures. Our experimental results demonstrate that PolymerGPT achieves exceptional performance for unconditional and conditional generation while maintaining high validity, uniqueness, and novelty. Conditioning on five key properties yields generated structures whose predicted values closely match all target properties simultaneously.
