The first HyDRA challenge for computational vibrational spectroscopy.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Fischer Taija L, Bödecker Margarethe, Schweer Sophie M, Dupont Jennifer, Lepère Valéria, Zehnacker-Rentien Anne, Suhm Martin A, Schröder Benjamin, Henkes Tobias, Andrada Diego M, Balabin Roman M, Singh Haobam Kisan, Bhattacharyya Himangshu Pratim, Sarma Manabendra, Käser Silvan, Töpfer Kai, Vazquez-Salazar Luis I, Boittier Eric D, Meuwly Markus, Mandelli Giacomo, Lanzi Cecilia, Conte Riccardo, Ceotto Michele, Dietrich Fabian, Cisternas Vicente, Gnanasekaran Ramachandran, Hippler Michael, Jarraya Mahmoud, Hochlaf Majdi, Viswanathan Narasimhan, Nevolianis Thomas, Rath Gabriel, Kopp Wassja A, Leonhard Kai, Mata Ricardo A
Аннотация
Vibrational spectroscopy in supersonic jet expansions is a powerful tool to assess molecular aggregates in close to ideal conditions for the benchmarking of quantum chemical approaches. The low temperatures achieved as well as the absence of environment effects allow for a direct comparison between computed and experimental spectra. This provides potential benchmarking data which can be revisited to hone different computational techniques, and it allows for the critical analysis of procedures under the setting of a blind challenge. In the latter case, the final result is unknown to modellers, providing an unbiased testing opportunity for quantum chemical models. In this work, we present the spectroscopic and computational results for the first HyDRA blind challenge. The latter deals with the prediction of water donor stretching vibrations in monohydrates of organic molecules. This edition features a test set of 10 systems. Experimental water donor OH vibrational wavenumbers for the vacuum-isolated monohydrates of formaldehyde, tetrahydrofuran, pyridine, tetrahydrothiophene, trifluoroethanol, methyl lactate, dimethylimidazolidinone, cyclooctanone, trifluoroacetophenone and 1-phenylcyclohexane-cis-1,2-diol are provided. The results of the challenge show promising predictive properties in both purely quantum mechanical approaches as well as regression and other machine learning strategies.