Abstract
Identifying stable speciation in multi-component liquid solutions is fundamentally important to areas from electrochemistry to organic chemistry and biomolecular systems. Here we introduce a fully automated, high-throughput computational framework for the accurate prediction of stable species in liquid solutions by computing the nuclear magnetic resonance (NMR) chemical shifts. The framework automatically extracts and categorizes hundreds of thousands of atomic clusters from classical molecular dynamics simulations, identifies the most stable species in solution and calculates their NMR chemical shifts via density functional theory calculations. Additionally, the framework creates a database of computed chemical shifts for liquid solutions across a wide chemical and parameter space. We compare our computational results to experimental measurements for magnesium bis(trifluoromethanesulfonyl)imide Mg(TFSI)2 salt in dimethoxyethane solvent. Our analysis of the Mg2+ solvation structural evolutions reveals key factors that influence the accuracy of NMR chemical shift predictions in liquid solutions. Furthermore, we show how the framework reduces the performance of over 300 13C and 600 1H density functional theory chemical shift predictions to a single submission procedure.
Highlights
Liquid solutions are critical components of various chemical, materials science, engineering, and biological applications such as batteries[1-3], fuel[4], food industry[5], and drug discovery[6,7].Optimizing the performance of these technologies requires taking into careful account transport and structural features, along with the thermodynamic stability of chemical compounds comprising the solution
Its functionalities span from processing and manipulating molecular structures, preparing and executing Density functional theory (DFT) and classical molecular dynamics (CMD) simulations on supercomputing resources, parsing and analyzing output data, and creating output databases that organize the results from individual calculations
Minimal inputs comprising structures of species in solution and their force field parameters are required to obtain accurate shifts, but the calculation procedure can be tuned by overriding default inputs like the level of theory and solvation model
Summary
Liquid solutions are critical components of various chemical, materials science, engineering, and biological applications such as batteries[1-3], fuel[4], food industry[5], and drug discovery[6,7]. Optimizing the performance of these technologies requires taking into careful account transport and structural features, along with the thermodynamic stability of chemical compounds comprising the solution. Developing a fundamental understanding of the correlations between functional properties and the underlying atomistic interactions is necessary for advancing the rational design of liquid solutions. In this regard, nuclear magnetic resonance (NMR). Technological advances in NMR spectroscopy have significantly improved the operational ease and spectral resolutions obtainable from non-traditional nuclei NMR spectroscopy is limited by the temporal scale and low sensitivity, making it difficult to speciate structural patterns that are often driven by electrostatic interactions, reactivity, temperature, compositional variance, and pressure[13-15]
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