The first HyDRA challenge for computational vibrational spectroscopy.

Area of Science:

• Physical Chemistry
• Computational Chemistry
• Spectroscopy

Background:

• Vibrational spectroscopy in supersonic jet expansions offers near-ideal conditions for molecular aggregate analysis.
• This technique facilitates direct comparison between experimental and computed spectra due to low temperatures and minimal environmental effects.
• It serves as a valuable tool for benchmarking quantum chemical approaches and refining computational methods.

Purpose of the Study:

• To present spectroscopic and computational results from the first HyDRA blind challenge.
• To evaluate the predictive accuracy of computational models for water donor stretching vibrations in organic monohydrates.
• To provide a benchmark dataset for assessing quantum chemical and machine learning strategies.

Main Methods:

• Utilizing supersonic jet expansions for vibrational spectroscopy of vacuum-isolated monohydrates.
• Conducting a blind challenge where computational models predict experimental vibrational wavenumbers.
• Testing a set of 10 diverse organic monohydrate systems.

Main Results:

• Experimental vibrational wavenumbers (OH stretching) were obtained for 10 organic monohydrates.
• The challenge assessed the performance of various quantum mechanical, regression, and machine learning approaches.
• Results indicate promising predictive capabilities across different computational strategies.

Conclusions:

• Supersonic jet expansion vibrational spectroscopy is a robust method for generating benchmarking data.
• Computational methods, including quantum mechanics and machine learning, show potential for accurately predicting vibrational spectra.
• The HyDRA challenge successfully provided an unbiased assessment of computational models for molecular systems.