An automated framework for high-throughput predictions of NMR chemical shifts within liquid solutions
نویسندگان
چکیده
Identifying stable speciation in multi-component liquid solutions is fundamentally important to areas from electrochemistry organic chemistry and biomolecular systems. Here we introduce a fully automated, high-throughput computational framework for the accurate prediction of species by computing nuclear magnetic resonance (NMR) chemical shifts. The automatically extracts categorizes hundreds thousands atomic clusters classical molecular dynamics simulations, identifies most solution calculates their NMR shifts via density functional theory calculations. Additionally, creates database computed across wide parameter space. We compare our results experimental measurements magnesium bis(trifluoromethanesulfonyl)imide Mg(TFSI)2 salt dimethoxyethane solvent. Our analysis Mg2+ solvation structural evolutions reveals key factors that influence accuracy shift predictions solutions. Furthermore, show how reduces performance over 300 13C 600 1H single submission procedure.
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ژورنال
عنوان ژورنال: Nature Computational Science
سال: 2022
ISSN: ['2662-8457']
DOI: https://doi.org/10.1038/s43588-022-00200-9