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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2312.04013 |
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| _version_ | 1866909562457554944 |
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| author | Khajeh, Arash Lei, Xiangyun Ye, Weike Yang, Zhenze Schweigert, Daniel Kwon, Ha-Kyung |
| author_facet | Khajeh, Arash Lei, Xiangyun Ye, Weike Yang, Zhenze Schweigert, Daniel Kwon, Ha-Kyung |
| contents | In this work, we introduce a polymer discovery platform to efficiently design polymers with tailored properties, exemplified by the discovery of high-performance polymer electrolytes. The platform integrates three core components: a conditioned generative model, a computational evaluation module, and a feedback mechanism, creating a self-improving system for material innovation. To demonstrate the efficacy of this platform, it is used to design polymer electrolyte materials with high ionic conductivity. A simple conditional generative model, based on the minGPT architecture, can effectively generate candidate polymers that exhibit a mean ionic conductivity that is significantly greater than those in the original training set. This approach, coupled with molecular dynamics simulations (MD) for testing and a specifically planned acquisition mechanism, allows the platform to refine its output iteratively. Notably, after the first iteration, we observed an increase in both the mean and the lower bound of the ionic conductivity of the new polymer candidates. The platform's effectiveness is underscored by the identification of 14 polymer repeating units, each displaying a computed ionic conductivity surpassing that of Polyethylene Oxide (PEO). The performance of these polymers in MD simulations verifies the platform's efficacy in generating potential polymer candidate materials. Acknowledging current limitations, future work will focus on enhancing modeling techniques, evaluation processes, and acquisition strategies, aiming for broader applicability in polymer science and machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_04013 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model Khajeh, Arash Lei, Xiangyun Ye, Weike Yang, Zhenze Schweigert, Daniel Kwon, Ha-Kyung Chemical Physics In this work, we introduce a polymer discovery platform to efficiently design polymers with tailored properties, exemplified by the discovery of high-performance polymer electrolytes. The platform integrates three core components: a conditioned generative model, a computational evaluation module, and a feedback mechanism, creating a self-improving system for material innovation. To demonstrate the efficacy of this platform, it is used to design polymer electrolyte materials with high ionic conductivity. A simple conditional generative model, based on the minGPT architecture, can effectively generate candidate polymers that exhibit a mean ionic conductivity that is significantly greater than those in the original training set. This approach, coupled with molecular dynamics simulations (MD) for testing and a specifically planned acquisition mechanism, allows the platform to refine its output iteratively. Notably, after the first iteration, we observed an increase in both the mean and the lower bound of the ionic conductivity of the new polymer candidates. The platform's effectiveness is underscored by the identification of 14 polymer repeating units, each displaying a computed ionic conductivity surpassing that of Polyethylene Oxide (PEO). The performance of these polymers in MD simulations verifies the platform's efficacy in generating potential polymer candidate materials. Acknowledging current limitations, future work will focus on enhancing modeling techniques, evaluation processes, and acquisition strategies, aiming for broader applicability in polymer science and machine learning. |
| title | A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2312.04013 |