![Author ORCID: We display the ORCID iD icon alongside authors names on our website to acknowledge that the ORCiD has been authenticated when entered by the user. To view the users ORCiD record click the icon. [opens in a new tab]](https://chemrxiv.org/engage/assets/public/chemrxiv/images/logos/orcid.png)
Abstract
In the field of vibrational spectroscopy simulation, hybrid approximations to Kohn-Sham density-functional theory (KS-DFT) are often considered computationally prohibitive due to the significant effort required to evaluate the exchange-correlation potential in planevawe codes. In this Letter, we show that by leveraging the porting of KS-DFT on GPU and incorporating 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).
Actions
