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Link to original content: https://doi.org/10.1038/s43588-022-00200-9
An automated framework for high-throughput predictions of NMR chemical shifts within liquid solutions | Nature Computational Science
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An automated framework for high-throughput predictions of NMR chemical shifts within liquid solutions

Nature Computational Science volume 2, pages 112–122 (2022)Cite this article

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A preprint version of the article is available at Research Square.

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.

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Fig. 1: Scheme of the computational framework used to calculate NMR chemical shifts in solution as implemented in the MISPR high-throughput infrastructure.
Fig. 2: Structural properties of Mg(TFSI)2 in DME at 298.15 K using FF1 (GAFF), FF2 (non-polarizable OPLS) and FF3 (polarizable OPLS).
Fig. 3: Predicted 25Mg NMR chemical shifts using the NMR computational protocol and the experimental NMR spectrum along with the corresponding predicted solvation structures of 1:18 Mg(TFSI)2 in DME solution.
Fig. 4: Strip plot of the computed and experimental 13C NMR chemical shifts assigned to CH3 of DME coordinated to Mg2+ (‘bound CH3’) and CH3 of free DME (‘free CH3’).
Fig. 5: Effect of multiple conformers on predicted chemical shifts.
Fig. 6: Structural analysis of 1:18 Li(TFSI) in DME solution at 298.15 K.

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Data availability

The dataset used to generate the results in this work along with the optimized 3D structures in XYZ format and initial and final MD trajectories are available at GitHub (https://github.com/rashatwi/nmr-dataset) and Zenodo68. Source data are provided with this paper.

Code availability

The open-source LAMMPS-code is used in the CMD simulations while the proprietary Gaussian-code is primarily used in the DFT calculations. The framework shown in Fig. 1 is implemented using (1) MISPR infrastructure, which defines, executes, manages and stores DFT and CMD workflows, and (2) MDPropTools, a Python package which performs statistical analysis of CMD outputs. The MISPR and MDPropTools packages are publicly available free of charge at https://github.com/molmd/mispr and https://github.com/molmd/mdproptools. The scripts used for producing the results are available in the Code Ocean capsule69.

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Acknowledgements

High-performance computational resources for this research were provided by the Extreme Science and Engineering Discovery Environment (XSEDE) Bridges and Comet computational resources (charge number: TG-DMR 190087), which is supported by National Science Foundation (NSF) grant number ACI-1548562. This work also used computational resources at the Stony Brook Institute for Advanced Computational Science (iACS). N.N.R. was supported by the startup funds from Stony Brook University. N.N.R. and R.A. acknowledge the startup funds from Tufts University. We acknowledge the iACS Junior Research Award to R.A. Experimental NMR research work was supported as part of the Joint Center for Energy Storage Research, an Energy Innovation Hub funded by the US Department of Energy, Office of Science, Basic Energy Sciences. The NMR experiments were performed using EMSL (grid.436923.9, Y.C., K.S.H., V.M., K.T.M.), a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research. PNNL is a multi-programme national laboratory operated for the DOE by Battelle Memorial Institute under contract DE-AC06-76RLO 1830 (Y.C., K.S.H., V.M., K.T.M.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Materials Science and Chemical Engineering, Stony Brook University, Stony Brook, NY, USA

    Rasha Atwi & Nav Nidhi Rajput

  2. The Joint Center for Energy Storage Research (JCESR), Pacific Northwest National Laboratory, Richland, WA, USA

    Ying Chen, Kee Sung Han, Karl T. Mueller & Vijayakumar Murugesan

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  1. Rasha Atwi
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Contributions

R.A. developed the automated NMR framework and the underlying Python-based codes, performed all the necessary calculations and had primary writing responsibilities. Y.C. and K.S.H. carried the NMR experiments. V.M. and K.T.M. guided the experimental aspect of the project. N.N.R. guided and led the computational aspects of the project. All authors contributed to writing and reviewing the manuscript.

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Correspondence to Nav Nidhi Rajput.

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Nature Computational Science thanks Yanfei Guan, Alexej Jerschow and Yeonjoon Kim for their contribution to the peer review of this work. Handling editor: Kaitlin McCardle, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Supplementary information

Supplementary Information

Supplementary Sections 1–4, Figs. 1–19, Tables 1–11 and References.

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Source data

Source Data Fig. 2

Radial distribution function and coordination number data for the Mg(TFSI)2 in DME system.

Source Data Fig. 3

Experimental and computational 25Mg chemical shifts.

Source Data Fig. 4

Experimental and computational 13C (CH3) chemical shifts.

Source Data Fig. 5

Computational 25Mg, 13C and 1H chemical shifts for multiple conformers.

Source Data Fig. 6

Radial distribution function data for the LiTFSI in DME system, experimental and computational 7Li chemical shifts.

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Atwi, R., Chen, Y., Han, K.S. et al. An automated framework for high-throughput predictions of NMR chemical shifts within liquid solutions. Nat Comput Sci 2, 112–122 (2022). https://doi.org/10.1038/s43588-022-00200-9

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