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PolymerGPT represents a significant advancement in inverse generative design by utilizing a decoder-based GPT architecture to simultaneously optimize up to 37 distinct polymer properties. This approach overcomes the limitations of existing single-property optimization methods, enabling precise control over macroscopic material behaviors through learned conditioning prefixes and scaffold specifications. The model's ability to generate structures that closely match multiple target values highlights its potential for accelerating high-performance materials discovery via machine learning.
PolymerGPT: Multi-property Optimization with a Decoder-Based GPT Model for Generative Polymer Design
arXiv:2608.01431v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
arXiv:2608.01431 [cs.AI]
(or arXiv:2608.01431v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.01431 arXiv-issued DOI via DataCite (pending registration)
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