MEPO-ML: a robust graph attention network model for rapid generation of partial atomic charges in metal-organic frameworks

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Jun Luo, Omar Ben Said, Peigen Xie, Marco Gibaldi, Jake Burner, Cécile Pereira, Tom K. Woo
{"title":"MEPO-ML: a robust graph attention network model for rapid generation of partial atomic charges in metal-organic frameworks","authors":"Jun Luo, Omar Ben Said, Peigen Xie, Marco Gibaldi, Jake Burner, Cécile Pereira, Tom K. Woo","doi":"10.1038/s41524-024-01413-4","DOIUrl":null,"url":null,"abstract":"<p>Accurate computation of the gas adsorption properties of MOFs is usually bottlenecked by the DFT calculations required to generate partial atomic charges. Therefore, large virtual screenings of MOFs often use the QEq method which is rapid, but of limited accuracy. Recently, machine learning (ML) models have been trained to generate charges in much better agreement with DFT-derived charges compared to the QEq models. Previous ML charge models for MOFs have all used training sets with less than 3000 MOFs obtained from the CoRE MOF database, which has recently been shown to have high structural error rates. In this work, we developed a graph attention network model for predicting DFT-derived charges in MOFs where the model was developed with the ARC-MOF database that contains 279,632 MOFs and over 40 million charges. This model, which we call <i>MEPO-ML</i>, predicts charges with a mean absolute error of 0.025e on our test set of over 27 K MOFs. Other ML models reported in the literature were also trained using the same dataset and descriptors, and MEPO-ML was shown to give the lowest errors. The gas adsorption properties evaluated using MEPO-ML charges are found to be in significantly better agreement with the reference DFT-derived charges compared to the empirical charges, for both polar and non-polar gases. Using only a single CPU core on our benchmark computer, MEPO-ML charges can be generated in less than two seconds on average (including all computations required to apply the model) for MOFs in the test set of 27 K MOFs.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":null,"pages":null},"PeriodicalIF":9.4000,"publicationDate":"2024-09-18","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/s41524-024-01413-4","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

Accurate computation of the gas adsorption properties of MOFs is usually bottlenecked by the DFT calculations required to generate partial atomic charges. Therefore, large virtual screenings of MOFs often use the QEq method which is rapid, but of limited accuracy. Recently, machine learning (ML) models have been trained to generate charges in much better agreement with DFT-derived charges compared to the QEq models. Previous ML charge models for MOFs have all used training sets with less than 3000 MOFs obtained from the CoRE MOF database, which has recently been shown to have high structural error rates. In this work, we developed a graph attention network model for predicting DFT-derived charges in MOFs where the model was developed with the ARC-MOF database that contains 279,632 MOFs and over 40 million charges. This model, which we call MEPO-ML, predicts charges with a mean absolute error of 0.025e on our test set of over 27 K MOFs. Other ML models reported in the literature were also trained using the same dataset and descriptors, and MEPO-ML was shown to give the lowest errors. The gas adsorption properties evaluated using MEPO-ML charges are found to be in significantly better agreement with the reference DFT-derived charges compared to the empirical charges, for both polar and non-polar gases. Using only a single CPU core on our benchmark computer, MEPO-ML charges can be generated in less than two seconds on average (including all computations required to apply the model) for MOFs in the test set of 27 K MOFs.

Abstract Image

MEPO-ML:用于快速生成金属有机框架中部分原子电荷的稳健图注意网络模型
对 MOFs 气体吸附特性的精确计算通常受制于生成部分原子电荷所需的 DFT 计算。因此,MOFs 的大型虚拟筛选通常使用 QEq 方法,这种方法虽然快速,但准确性有限。与 QEq 模型相比,最近经过训练的机器学习 (ML) 模型生成的电荷与 DFT 导出电荷的一致性要好得多。以前的 MOF ML 电荷模型都使用了从 CoRE MOF 数据库中获得的少于 3000 个 MOF 的训练集,而最近的研究表明该数据库具有很高的结构错误率。在这项工作中,我们开发了一种图注意网络模型,用于预测 MOF 中的 DFT 衍生电荷,该模型是利用 ARC-MOF 数据库开发的,该数据库包含 279632 个 MOF 和 4000 多万个电荷。我们将该模型称为 MEPO-ML,它在超过 27 K 个 MOF 的测试集上预测电荷的平均绝对误差为 0.025e。文献中报道的其他 ML 模型也使用相同的数据集和描述符进行了训练,结果表明 MEPO-ML 的误差最小。对于极性和非极性气体,使用 MEPO-ML 电荷评估的气体吸附特性与参考的 DFT 衍生电荷相比,与经验电荷的一致性明显更好。在我们的基准计算机上仅使用一个 CPU 内核,就可以在平均不到两秒的时间内为 27 K MOFs 测试集中的 MOFs 生成 MEPO-ML 电荷(包括应用模型所需的所有计算)。
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