快速预测复杂有机分子的非谐波振动光谱

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-10-10 DOI:10.1038/s41524-024-01400-9
Mattia Miotto, Lorenzo Monacelli
{"title":"快速预测复杂有机分子的非谐波振动光谱","authors":"Mattia Miotto, Lorenzo Monacelli","doi":"10.1038/s41524-024-01400-9","DOIUrl":null,"url":null,"abstract":"<p>Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular motions, even when highly anharmonic, without the need for computationally expensive ab initio calculations. Our approach stems from the time-dependent stochastic self-consistent harmonic approximation to capture quantum nuclear fluctuations in atom dynamics while describing interatomic interaction through state-of-the-art reactive machine-learning force fields. Finally, we employ an isotropic charge model and a bond capacitor model trained on ab initio data to predict the intensity of IR and Raman signals.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"62 1","pages":""},"PeriodicalIF":9.4000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fast prediction of anharmonic vibrational spectra for complex organic molecules\",\"authors\":\"Mattia Miotto, Lorenzo Monacelli\",\"doi\":\"10.1038/s41524-024-01400-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular motions, even when highly anharmonic, without the need for computationally expensive ab initio calculations. Our approach stems from the time-dependent stochastic self-consistent harmonic approximation to capture quantum nuclear fluctuations in atom dynamics while describing interatomic interaction through state-of-the-art reactive machine-learning force fields. Finally, we employ an isotropic charge model and a bond capacitor model trained on ab initio data to predict the intensity of IR and Raman signals.</p>\",\"PeriodicalId\":19342,\"journal\":{\"name\":\"npj Computational Materials\",\"volume\":\"62 1\",\"pages\":\"\"},\"PeriodicalIF\":9.4000,\"publicationDate\":\"2024-10-10\",\"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/s41524-024-01400-9\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"npj Computational Materials","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1038/s41524-024-01400-9","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

摘要

解读缺乏对称性的复杂有机分子的拉曼和红外振动光谱是一项艰巨的挑战。在本研究中,我们提出了一种创新方法,用于模拟振动光谱并将观测到的峰值归因于分子运动,即使是在高度非谐波的情况下,也无需进行计算成本高昂的 ab initio 计算。我们的方法源于随时间变化的随机自洽谐波近似,以捕捉原子动力学中的量子核波动,同时通过最先进的反应式机器学习力场来描述原子间的相互作用。最后,我们采用各向同性电荷模型和在 ab initio 数据基础上训练的键电容模型来预测红外和拉曼信号的强度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Fast prediction of anharmonic vibrational spectra for complex organic molecules

Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular motions, even when highly anharmonic, without the need for computationally expensive ab initio calculations. Our approach stems from the time-dependent stochastic self-consistent harmonic approximation to capture quantum nuclear fluctuations in atom dynamics while describing interatomic interaction through state-of-the-art reactive machine-learning force fields. Finally, we employ an isotropic charge model and a bond capacitor model trained on ab initio data to predict the intensity of IR and Raman signals.

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.
期刊最新文献
Deep learning potential model of displacement damage in hafnium oxide ferroelectric films Thermodynamics of solids including anharmonicity through quasiparticle theory Neural network potential for dislocation plasticity in ceramics Exhaustive search for novel multicomponent alloys with brute force and machine learning A Ring2Vec description method enables accurate predictions of molecular properties in organic solar cells
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1