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Abstract
In the field of vibrational spectroscopy simulation, hybrid approximations to Kohn-Sham density-functional theory (KS-DFT) are often claimed as computationally prohibitive due to the large effort required to evaluate the exchange-correlation potential in planevawe codes. In this Letter, we show that by taking advantage of KS-DFT porting on GPU and machine-learning techniques, simulating IR and Raman spectra of real-life chromophores in bulk aqueous solution becomes a routine application at this level of theory.
Supplementary materials
Title
Boosting the Modeling of InfraRed and Raman Spectra of Bulk Phase Chromophores with Machine Learning
Description
The Supporting Information reports all the details regarding the time-series analysis methodology (Section S1), the computational details (Section S2), the list of the vibrational modes of ADOTA$^{+}$ (Section S3), and the description and implementation of the machine learning model (Section S4).
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