通过高通量分子动力学和可解释的人工智能探索高性能粘度指数改善聚合物

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Rui Zhou, Luyao Bao, Weifeng Bu, Feng Zhou
{"title":"通过高通量分子动力学和可解释的人工智能探索高性能粘度指数改善聚合物","authors":"Rui Zhou, Luyao Bao, Weifeng Bu, Feng Zhou","doi":"10.1038/s41524-025-01539-z","DOIUrl":null,"url":null,"abstract":"<p>Data-driven material innovation has the potential to revolutionize the traditional Edisonian process and significantly shorten development cycles. However, the scarcity of data in materials science and the poor interpretability of machine learning pose serious obstacles to the adoption of this new paradigm. Here, we propose a pipeline that integrates data production, virtual screening, and theoretical innovation using high-throughput all-atom molecular dynamics (MD) as a data flywheel. Using this pipeline, we explored high-performance viscosity index improver polymers and constructed a dataset of 1166 entries for viscosity index improvers (VII) started from only five types of polymers. Under multi-objective constraints, 366 potential high-viscosity-temperature performance polymers were identified, and six representative polymers were validated through direct MD simulations. Starting from high-dimensional physical features, we conducted an unbiased systematic analysis of the quantitative structure-property relationships for polymers VII, providing an explicit mathematical model with promising application in VII industry. This work demonstrates the advanced capabilities and reliability of the pipeline proposed here in initiating material innovation cycles in data-scarce fields, and the establishment of the VII dataset and models will serve as a critical starting point for the data-driven design of high viscosity-temperature performance polymers.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"34 1","pages":""},"PeriodicalIF":9.4000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AI\",\"authors\":\"Rui Zhou, Luyao Bao, Weifeng Bu, Feng Zhou\",\"doi\":\"10.1038/s41524-025-01539-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Data-driven material innovation has the potential to revolutionize the traditional Edisonian process and significantly shorten development cycles. However, the scarcity of data in materials science and the poor interpretability of machine learning pose serious obstacles to the adoption of this new paradigm. Here, we propose a pipeline that integrates data production, virtual screening, and theoretical innovation using high-throughput all-atom molecular dynamics (MD) as a data flywheel. Using this pipeline, we explored high-performance viscosity index improver polymers and constructed a dataset of 1166 entries for viscosity index improvers (VII) started from only five types of polymers. Under multi-objective constraints, 366 potential high-viscosity-temperature performance polymers were identified, and six representative polymers were validated through direct MD simulations. Starting from high-dimensional physical features, we conducted an unbiased systematic analysis of the quantitative structure-property relationships for polymers VII, providing an explicit mathematical model with promising application in VII industry. This work demonstrates the advanced capabilities and reliability of the pipeline proposed here in initiating material innovation cycles in data-scarce fields, and the establishment of the VII dataset and models will serve as a critical starting point for the data-driven design of high viscosity-temperature performance polymers.</p>\",\"PeriodicalId\":19342,\"journal\":{\"name\":\"npj Computational Materials\",\"volume\":\"34 1\",\"pages\":\"\"},\"PeriodicalIF\":9.4000,\"publicationDate\":\"2025-03-01\",\"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/s41524-025-01539-z\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Computational Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1038/s41524-025-01539-z","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

摘要

数据驱动的材料创新有可能彻底改变传统的爱迪生工艺,并显著缩短开发周期。然而,材料科学中数据的稀缺性和机器学习的可解释性差对采用这种新范式构成了严重障碍。在这里,我们提出了一个整合数据生产、虚拟筛选和理论创新的管道,使用高通量全原子分子动力学(MD)作为数据飞轮。利用该管道,我们探索了高性能的粘度指数改善聚合物,并从五种聚合物开始构建了一个包含1166个条目的粘度指数改善(VII)数据集。在多目标约束下,确定了366种潜在的高粘高温性能聚合物,并通过直接MD模拟对其中6种具有代表性的聚合物进行了验证。从聚合物VII的高维物理特征出发,对聚合物VII的定量构效关系进行了无偏的系统分析,提供了一个明确的数学模型,在VII工业中具有广阔的应用前景。这项工作证明了本文提出的管道在启动数据稀缺领域的材料创新周期方面的先进能力和可靠性,并且VII数据集和模型的建立将成为高粘高温性能聚合物数据驱动设计的关键起点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AI

Exploring high-performance viscosity index improver polymers via high-throughput molecular dynamics and explainable AI

Data-driven material innovation has the potential to revolutionize the traditional Edisonian process and significantly shorten development cycles. However, the scarcity of data in materials science and the poor interpretability of machine learning pose serious obstacles to the adoption of this new paradigm. Here, we propose a pipeline that integrates data production, virtual screening, and theoretical innovation using high-throughput all-atom molecular dynamics (MD) as a data flywheel. Using this pipeline, we explored high-performance viscosity index improver polymers and constructed a dataset of 1166 entries for viscosity index improvers (VII) started from only five types of polymers. Under multi-objective constraints, 366 potential high-viscosity-temperature performance polymers were identified, and six representative polymers were validated through direct MD simulations. Starting from high-dimensional physical features, we conducted an unbiased systematic analysis of the quantitative structure-property relationships for polymers VII, providing an explicit mathematical model with promising application in VII industry. This work demonstrates the advanced capabilities and reliability of the pipeline proposed here in initiating material innovation cycles in data-scarce fields, and the establishment of the VII dataset and models will serve as a critical starting point for the data-driven design of high viscosity-temperature performance polymers.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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