用GPT模型同时优化37种聚合物性能，实现精准逆向设计。
PolymerGPT: Multi-property Optimization with a Decoder-Based GPT Model for Generative Polymer Design
- 基于GPT的解码器架构，用学习到的前缀条件控制多属性生成
- 同时满足5个目标属性时，预测值与目标值高度吻合
- 支持骨架约束生成，兼顾结构有效性与新颖性
聚合物性质预测与面向特定性能的逆向生成设计是机器学习辅助聚合物设计中的两个关键任务。尽管前者已获广泛关注，但后者方法仍有限。现有方法多聚焦于生成过程中的单属性优化，而实际材料宏观行为的准确预测需同时调控多种物理性质。本文提出一种变革性框架，可直接优化大量聚合物性质。我们构建了PolymerGPT——一种基于解码器的GPT模型，通过学习到的条件前缀将最多37种常用聚合物性质融入生成过程，并支持指定期望骨架结构的条件生成。实验表明，PolymerGPT在无条件与条件生成中均表现优异，保持高结构有效性、唯一性与新颖性。对五个关键性质进行条件控制后，生成结构的预测值能同时精确匹配所有目标属性。
原文摘要 · Abstract (English)
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.
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