期刊
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 18, 期 9, 页码 5492-5501出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.2c00511
关键词
-
资金
- Studienstiftung des deutschen Volkes
- Alexander von Humboldt-Stiftung
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy [EXC 2033-390677874-RESOLV]
- DFG [MA 1547/19]
- Center for Solvation Science ZEMOS - German Federal Ministry of Education and Research
- Ministry of Culture and Research of North Rhine-Westphalia
Infrared spectroscopy is an important technique for studying molecular structures, reactions, and conformational changes. Accurately predicting infrared spectra based on first-principle theories has been a pursuit. In this study, we use cutting-edge machine learning techniques to predict fully anharmonic vibrational spectra of large systems at finite temperatures, expanding the limits of accuracy, speed, and system size for theoretical spectroscopy.
Infrared spectroscopy is key to elucidating molecular structures, monitoring reactions, and observing conformational changes, while providing information on both structural and dynamical properties. This makes the accurate prediction of infrared spectra based on first-principle theories a highly desirable pursuit. Molecular dynamics simulations have proven to be a particularly powerful approach for this task, albeit requiring the computation of energies, forces and dipole moments for a large number of molecular configurations as a function of time. This explains why highly accurate first-principles methods, such as coupled cluster theory, have so far been inapplicable for the prediction of fully anharmonic vibrational spectra of large systems at finite temperatures. Here, we push cutting-edge machine learning techniques forward by using neural network representations of energies, forces, and in particular dipoles to predict such infrared spectra fully at gold standard coupled cluster accuracy as demonstrated for protonated water clusters as large as the protonated water hexamer, in its extended Zundel configuration. Furthermore, we show that this methodology can be used beyond the scope of the data considered during the development of the neural network models, allowing for the computation of finite temperature infrared spectra of large systems inaccessible to explicit coupled cluster calculations. This substantially expands the hitherto existing limits of accuracy, speed, and system size for theoretical spectroscopy and opens up a multitude of avenues for the prediction of vibrational spectra and the understanding of complex intra-and intermolecular couplings.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据