De novo design of polymer electrolytes using GPT-based and diffusion-based generative models

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Zhenze Yang, Weike Ye, Xiangyun Lei, Daniel Schweigert, Ha-Kyung Kwon, Arash Khajeh
{"title":"De novo design of polymer electrolytes using GPT-based and diffusion-based generative models","authors":"Zhenze Yang, Weike Ye, Xiangyun Lei, Daniel Schweigert, Ha-Kyung Kwon, Arash Khajeh","doi":"10.1038/s41524-024-01470-9","DOIUrl":null,"url":null,"abstract":"<p>Solid polymer electrolytes offer promising advancements for next-generation batteries, boasting superior safety, enhanced specific energy, and extended lifespans over liquid electrolytes. However, low ionic conductivity and the vast polymer space hinder commercialization. This study leverages generative AI for de novo polymer electrolyte design, comparing GPT-based and diffusion-based models with extensive hyperparameter tuning. We evaluate these models using various metrics and full-atom molecular dynamics simulations. Among 46 candidates tested, 17 exhibit superior ionic conductivity, surpassing existing polymers in our database, with some doubling the conductivity values. Additionally, by adopting pretraining and fine-tuning methodologies, we significantly enhance our generative models, achieving quicker convergence, better performance with limited data, and greater diversity. Our method efficiently generates a large number of novel, diverse, and valid polymers, with a high likelihood of synthesizability, enabling the identification of promising candidates with markedly improved efficiency and effectiveness for practical applications.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"56 1","pages":""},"PeriodicalIF":9.4000,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Computational Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1038/s41524-024-01470-9","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Solid polymer electrolytes offer promising advancements for next-generation batteries, boasting superior safety, enhanced specific energy, and extended lifespans over liquid electrolytes. However, low ionic conductivity and the vast polymer space hinder commercialization. This study leverages generative AI for de novo polymer electrolyte design, comparing GPT-based and diffusion-based models with extensive hyperparameter tuning. We evaluate these models using various metrics and full-atom molecular dynamics simulations. Among 46 candidates tested, 17 exhibit superior ionic conductivity, surpassing existing polymers in our database, with some doubling the conductivity values. Additionally, by adopting pretraining and fine-tuning methodologies, we significantly enhance our generative models, achieving quicker convergence, better performance with limited data, and greater diversity. Our method efficiently generates a large number of novel, diverse, and valid polymers, with a high likelihood of synthesizability, enabling the identification of promising candidates with markedly improved efficiency and effectiveness for practical applications.

Abstract Image

基于gpt和基于扩散的生成模型的聚合物电解质从头设计
与液态电解质相比,固态聚合物电解质具有更高的安全性、更强的比能量和更长的使用寿命,为下一代电池提供了广阔的发展前景。然而,低离子电导率和广阔的聚合物空间阻碍了商业化进程。本研究利用生成式人工智能进行聚合物电解质的全新设计,比较了基于 GPT 的模型和基于扩散的模型,并进行了广泛的超参数调整。我们使用各种指标和全原子分子动力学模拟对这些模型进行了评估。在测试的 46 种候选材料中,有 17 种表现出卓越的离子电导率,超过了我们数据库中现有的聚合物,其中一些材料的电导率值还翻了一番。此外,通过采用预训练和微调方法,我们极大地增强了生成模型,实现了更快的收敛、在数据有限的情况下更好的性能和更大的多样性。我们的方法能有效生成大量新颖、多样和有效的聚合物,而且具有很高的可合成性,从而能识别出有潜力的候选聚合物,并显著提高了实际应用的效率和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信